IVR: Introduction to Control (IV)
|
|
- Patrick Wade
- 5 years ago
- Views:
Transcription
1 IVR: Introduction to Control (IV) 16/11/2010
2 Proportional error control (P) Example Control law: V B = M k 2 R ds dt + k 1s V B = K ( ) s goal s Convenient, simple, powerful (fast and proportional reaction to errors) No need for modelling (sign and the order of magnitude of K p must be known) Problems: Steady state error, load droop, oscillations about the goal state (ringing)
3 Example: Motor with gears Modifying the dynamics by altering the robot morphology Thus: V B = 1 γ MR k 2 Gear ratio γ where more gear-teeth near output means γ > 1. smotor = γ sout: for γ > 1, output velocity is slower torque motor = γ 1 torque out : for γ > 1, output torque is higher ds dt + γk 1s Same form, but dierent steady-state, time-constant (half-life): s = V B γk 1 τ 1/2 = 0.7 MR γ 2 k 1 k 2 i.e. for γ > 1, reach lower speed in faster time, robot is more responsive, though slower. (Note: we've ignored that spur gears reduce eciency by 10%)
4 Example: Motor with gears Steady state error (P-control) K ( ) s goal s = M R ds γk 2 dt +γk 1s i.e. Ks goal = M R ds γk 2 dt +(K + γk 1) s Steady state s = K K + γk 1 s goal For K < : Steady state error increases for γ > 1 s goal s = s goal K K + γk 1 s goal = γk 1 K + γk 1 s goal
5 Proportional Integral (PI) Control Example: ) V B = K p (s goal s + ε = M R ds k 2 dt + k 1s Integrate the error over time Control law (P and PI): V B = K p (s goal s ε = ( s goal s ) dt ) ( ) + K i s goal s dt Reacts to small systematic errors; choosing K p and K i appropriately steady state error can be eliminated Problems: slow (K i usually small), oscillation about the goal state, wind-up (accumulation of large errors) Solutions: Freezing at disturbances, using model information to compensate large errors
6 Proportional derivative (PD) control Example Control law (P and PD): T = J d 2 θ dt 2 + F dθ dt ) dθ T = K p (θ goal θ + K d dt Dampens oscillations, improves stability Useful for large inertia & small friction (adds articial friction) Often: derivative of error θ goal θ ( K d has opposite sign), but derivative of θ gives better stability when θ goal changes Possible problems: Derivative is sensitive to measurement noise Tends to slow down the control action Does not know the (constant) goal state usually in combination with other control signals
7 PD, PI,... single-letter controllers are less often used P-controller: Possible if goal state can be kept with zero action and if the system is self-damping I-controller: Possible if overshoots and wind-up can be dealt with, e.g. by `leaky' integration D-controller: Does not know the (constant!) goal state useful only in combination with other control signals or as a fall-back controller PD or PI controllers are fairly common PD: Possible if goal state can be kept with zero action PI: Possible if the system is self-damping
8 Combine as PID control (Three mode control) Black sectors denote negation ) t T = K p (θ goal θ + K i How to choose K p, K i, K d? 0 ( θ goal θ ( t )) dt dθ + K d dt PID controllers are used in more than 95% of feedback control applications (as of 1995)
9 Characterising the behaviour of a control system 1 Rise Time: time it takes for the plant output to rise beyond 90% of the desired level for the rst time 2 Settling Time: time it takes for the system to converge to its steady state. Settling time may be long in the case of on-going oscillations Rise time and settling time replace half time (which was for non-oscillatory exponential convergence) 3 Overshoot: how much the the peak level is higher than the steady state, normalized against the steady state. 4 Steady-state Error: the dierence between the steady-state output and the desired output. Jinghua Zhong: PID Controller Tuning: A Short Tutorial (saba.kntu.ac.ir/eecd/pcl/download/pidtutorial.pdf)
10 A remark on linear dierential equations Second-order system Dene ρ = dθ dt d 2 θ and insert ( dρ dt dθ dt dt 2 = A 1 dρ dt = A 1 dθ dt + A 0θ + C dθ dt + A 0θ + C We get an equivalent two-dimensional rst-order system ) ( ) ( ) ( ) A1 A = 0 ρ C θ 0 Diagonalize: R dρ dt dθ dt! «««A0 A1 = R R 1 ρ C R + R θ such that R «A0 A1 R 1 = 1 0 «λ1 0 0 λ 2 Transform to the new coordinates, solve two simple di-eqs with constant terms, transform back to ρ,θ, insert initial conditions, done
11 PID control example Second-order system T (t) = J d 2 θ dt 2 + F dθ dt Controller ) ( ) dθ K p (θ goal θ (t) + K i θ goal θ (t) dt + K d dt = T (t) Numerical solution with: A 1 = K d F, A J 0 = Kp J, C = K i J t 0 ( ) θ goal θ (t) dt + Kp θ J goal d 2 θ dt 2 = A 1 dθ dt + A 0θ + C
12 PID control example 1 Behaviour at K p = K i = K d = 0: Overdamped (see lect. 14) 2 Start with proportional control: K i = K d = 0, θ goal = 1 θ Kp= Kp=1.0 Kp= Kp= Ki=0 Kd= t
13 PID control example 3 K p = 1, K i 0: Similar eect as further increasing K p θ Ki=0.5 Ki=0.3 Ki=0.1 Ki= Kd=0 Kp= t
14 PID control example Note, that the negative sign of K d is for consistency with the equation two slides back. Considered as articial friction, the used dierential term is clearly damping. 4 K p = 1, K d 0: Reduce oscillations but keep rise time low θ Kd= Kd=-0.2 Kd=-0.5 Kd=-1.0 Ki=0 Kp= t
15 PID control example: Evaluation Often PD control is sucient Integral term needed only if steady state error is expected Consider priorities when determining overshoot: When catching a ball: fast rise time is essential (set goal state to a lower value and make sure that the ball arrives at the overshoot) When moving towards a position near an obstacle: slow rise time, overdamped movement no overshoot General case: adjust K d to realise critical damping
16 Eects of increasing a parameter independently Parameter Rise time Overshoot K p Decrease Increase Settling time Small change K i Decrease Increase Increase K d Minor increase Minor decrease Minor change Steadystate error Decrease Decrease signicantly No eect in theory Stability Degrade Degrade Improve if K d is small (except red entries)
17 Control with changing set points In tracking tasks the set point changes continuously: goal trajectory Rise time and settling time appear as delays (phase shifts) which depend on the rate of change of the goal trajectory Delays can be reduced by high-gain proportional control θ Kp=5 Kp=1 goal trajectory Ki=0 Kd= t
18 Control with changing set points Dierential feedback can reduce the overshoot, but tends to increases phase shift Integral feedback will not improve the situation Solution: Forward models, θ 1.5 Kd=0 Kd=-1.6 goal trajectory Kp=5 Ki= t
19 Changing set-points: Improvements Set-point switching: Change the set point in a ramp-like way Initializing the integral term at a suitable value Disabling the integral function until the state has entered the controllable region Limiting the time period over which the integral error is calculated Preventing the integral term from accumulating above or below pre-determined bounds
20 Typical steps for designing a PID controller Determine what characteristics of the system needs to be improved. Use K p to decrease the rise time. Use K d Use K i to reduce the overshoot and settling time. to eliminate the steady-state error. This works in many cases
21 Ziegler-Nichols tuning rule (reaction curve method) Practical control method, i.e. the controlled system can only be accessed experimentally 1 Set I and D gains to zero. 2 Check sign of gain (say positive) 3 Increase P gain (from zero) until until output starts to oscillate `ultimate gain' K u and oscillation period T u 4 Use K u and T u to set K p, K i and K d based on heuristic values: K p = 0.65K u, K i = 2K p /T u and K d = K p T u /8 May create some overshoot Stable to disturbances Not very good in tracking tasks Not equally good in all applications
22 Ziegler-Nichols rule tested Second order system (F = 0.5, J = 0.5) K u 1: one full oscillation period visible T u = 5 K p = 0.65, K d = 1.25, K i = 0.26 (scaled by time step!) θ t Performance similar as tuned by hand (Z-N rule works best for rst-order systems!) Why Do We Keep Hinting That Results are Lousy? (
23 Limitations of PID control PID control is the best controller with no model of the process (PID can be used on top of model-based control) Does not provide optimal control Is only reactive and needs errors to be able to react (combine with feed-forward control or forward models) D-term may suer from intrinsic or measurement noise ( use low-pass lter) D-term (error derivative) and I-term may suer from sudden set-point changes ( use set-point ramping) Is tuned to a particular working regime ( gain scheduling) Is linear and symmetric (e.g. usually a heating system does not involve symmetric cooling)
24 An example of D-only control in robotics Minimize changes of sensor values (this is achieved by D term) Use for control of direction (makes sense only with a persistent driving force, i.e. forward speed, and initialization) Generate useful behaviours (without re-programming): Obstacle avoidance (obstacle causes changes of sensor values) Following a wall or corridor based on distance sensor Stabilizing an image on a camera If not even sign of gain is know: Performing small probing movements provides information about (presently) correct sign
25 Further reading Most standard control textbooks discuss PID control, e.g.: Andrew D. Lewis: A Mathematical Approach to Classical Control igor.chudov.com/manuals/servo-tuning/pid-without-a-phd.pdf For the research on robot juggling see: Rizzi, A.A. & Koditschek, D.E. (1994) Further progress in robot juggling: Solvable mirror laws. IEEE International Conference on Robotics and Automation S. Schaal and C.G. Atkeson. Open Loop Stable Control Strategies for Robot Juggling. In Proc. IEEE Conf. Robotics and Automation, pages , Atlanta, Georgia, 1993.
26 Classical Control Paradigm: SPA SPA is SPA lacks: serial speed and eciency ad hoc exibility and adaptivity analytical modularity and scalability assumptious errortolerance and robustness
27 Rodney Brooks: fast, cheap, and out of control Planning is just a way of avoiding guring out what to do next. The world is its own best model Complex behavior need not necessarily be the product of a complex control system Simplicity is a virtue Robots should be cheap All on-board computation is important Systems should be build incrementally Intelligence is in the eye of the observer No representation, no calibration, no complex computers Some objections that can be misleading: In a dierent environment the robot will fail! A function that deals with this problem will create more problems The system cannot be debugged! Bugs, too, are in the eye of the observer It's not scalable! Elephants don't play chess
28 Subsumption Architecture
29 Evaluation of the Subsumption Architecture I wouldn't want one to be my chaueur (C. Torpe) Modications at low-levels aect higher levels Often there the hierarchy is not strict Priorities rather than inhibition Representations, plans, and models do help Reproducibility is a virtue SPA is top-down, Subsumption Architecture is bottom-up neats vs. scrues
30 Modular architectures Schemas (M. Arbib) Circuit architecture: Situated automata (L. Kaelbling) Action selection (P. Maes), behavior-based robotics (R. Arkin) Dynamical systems and ant colonies Cognitive architectures
31 Three-layer architecture (TLA) Elementary behaviours Competition, scheduling, and adaptation of behaviours Planning, search, reasoning standard programming Erann Gat, 1998
32 Architectures summary 1 Simplicity is a virtue 2 The subsumption architecture is simple and extendable and makes often a good start 3 The ultimate goal is to interface automatic reasoning with the real world 4 Limited resources, sensor noise, unpredictability and complexity of the environment are problems in any approach to robot control
Analysis and Design of Control Systems in the Time Domain
Chapter 6 Analysis and Design of Control Systems in the Time Domain 6. Concepts of feedback control Given a system, we can classify it as an open loop or a closed loop depends on the usage of the feedback.
More informationAN INTRODUCTION TO THE CONTROL THEORY
Open-Loop controller An Open-Loop (OL) controller is characterized by no direct connection between the output of the system and its input; therefore external disturbance, non-linear dynamics and parameter
More informationPID controllers. Laith Batarseh. PID controllers
Next Previous 24-Jan-15 Chapter six Laith Batarseh Home End The controller choice is an important step in the control process because this element is responsible of reducing the error (e ss ), rise time
More informationChapter 7 Interconnected Systems and Feedback: Well-Posedness, Stability, and Performance 7. Introduction Feedback control is a powerful approach to o
Lectures on Dynamic Systems and Control Mohammed Dahleh Munther A. Dahleh George Verghese Department of Electrical Engineering and Computer Science Massachuasetts Institute of Technology c Chapter 7 Interconnected
More informationEE 422G - Signals and Systems Laboratory
EE 4G - Signals and Systems Laboratory Lab 9 PID Control Kevin D. Donohue Department of Electrical and Computer Engineering University of Kentucky Lexington, KY 40506 April, 04 Objectives: Identify the
More informationFeedback Control of Linear SISO systems. Process Dynamics and Control
Feedback Control of Linear SISO systems Process Dynamics and Control 1 Open-Loop Process The study of dynamics was limited to open-loop systems Observe process behavior as a result of specific input signals
More informationControl 2. Proportional and Integral control
Control 2 Proportional and Integral control 1 Disturbance rejection in Proportional Control Θ i =5 + _ Controller K P =20 Motor K=2.45 Θ o Consider first the case where the motor steadystate gain = 2.45
More informationVideo 5.1 Vijay Kumar and Ani Hsieh
Video 5.1 Vijay Kumar and Ani Hsieh Robo3x-1.1 1 The Purpose of Control Input/Stimulus/ Disturbance System or Plant Output/ Response Understand the Black Box Evaluate the Performance Change the Behavior
More informationGAIN SCHEDULING CONTROL WITH MULTI-LOOP PID FOR 2- DOF ARM ROBOT TRAJECTORY CONTROL
GAIN SCHEDULING CONTROL WITH MULTI-LOOP PID FOR 2- DOF ARM ROBOT TRAJECTORY CONTROL 1 KHALED M. HELAL, 2 MOSTAFA R.A. ATIA, 3 MOHAMED I. ABU EL-SEBAH 1, 2 Mechanical Engineering Department ARAB ACADEMY
More informationK c < K u K c = K u K c > K u step 4 Calculate and implement PID parameters using the the Ziegler-Nichols tuning tables: 30
1.5 QUANTITIVE PID TUNING METHODS Tuning PID parameters is not a trivial task in general. Various tuning methods have been proposed for dierent model descriptions and performance criteria. 1.5.1 CONTINUOUS
More informationMASSACHUSETTS INSTITUTE OF TECHNOLOGY Department of Mechanical Engineering 2.04A Systems and Controls Spring 2013
MASSACHUSETTS INSTITUTE OF TECHNOLOGY Department of Mechanical Engineering 2.04A Systems and Controls Spring 2013 Problem Set #4 Posted: Thursday, Mar. 7, 13 Due: Thursday, Mar. 14, 13 1. Sketch the Root
More informationSpontaneous Speed Reversals in Stepper Motors
Spontaneous Speed Reversals in Stepper Motors Marc Bodson University of Utah Electrical & Computer Engineering 50 S Central Campus Dr Rm 3280 Salt Lake City, UT 84112, U.S.A. Jeffrey S. Sato & Stephen
More informationSeul Jung, T. C. Hsia and R. G. Bonitz y. Robotics Research Laboratory. University of California, Davis. Davis, CA 95616
On Robust Impedance Force Control of Robot Manipulators Seul Jung, T C Hsia and R G Bonitz y Robotics Research Laboratory Department of Electrical and Computer Engineering University of California, Davis
More informationLecture 6: Control Problems and Solutions. CS 344R: Robotics Benjamin Kuipers
Lecture 6: Control Problems and Solutions CS 344R: Robotics Benjamin Kuipers But First, Assignment 1: Followers A follower is a control law where the robot moves forward while keeping some error term small.
More informationOpen Loop Tuning Rules
Open Loop Tuning Rules Based on approximate process models Process Reaction Curve: The process reaction curve is an approximate model of the process, assuming the process behaves as a first order plus
More informationLaboratory Exercise 1 DC servo
Laboratory Exercise DC servo Per-Olof Källén ø 0,8 POWER SAT. OVL.RESET POS.RESET Moment Reference ø 0,5 ø 0,5 ø 0,5 ø 0,65 ø 0,65 Int ø 0,8 ø 0,8 Σ k Js + d ø 0,8 s ø 0 8 Off Off ø 0,8 Ext. Int. + x0,
More informationLecture 5 Classical Control Overview III. Dr. Radhakant Padhi Asst. Professor Dept. of Aerospace Engineering Indian Institute of Science - Bangalore
Lecture 5 Classical Control Overview III Dr. Radhakant Padhi Asst. Professor Dept. of Aerospace Engineering Indian Institute of Science - Bangalore A Fundamental Problem in Control Systems Poles of open
More informationDr Ian R. Manchester
Week Content Notes 1 Introduction 2 Frequency Domain Modelling 3 Transient Performance and the s-plane 4 Block Diagrams 5 Feedback System Characteristics Assign 1 Due 6 Root Locus 7 Root Locus 2 Assign
More informationGain Scheduling Control with Multi-loop PID for 2-DOF Arm Robot Trajectory Control
Gain Scheduling Control with Multi-loop PID for 2-DOF Arm Robot Trajectory Control Khaled M. Helal, 2 Mostafa R.A. Atia, 3 Mohamed I. Abu El-Sebah, 2 Mechanical Engineering Department ARAB ACADEMY FOR
More informationSystem Modeling: Motor position, θ The physical parameters for the dc motor are:
Dept. of EEE, KUET, Sessional on EE 3202: Expt. # 2 2k15 Batch Experiment No. 02 Name of the experiment: Modeling of Physical systems and study of their closed loop response Objective: (i) (ii) (iii) (iv)
More informationChapter 7 Control. Part Classical Control. Mobile Robotics - Prof Alonzo Kelly, CMU RI
Chapter 7 Control 7.1 Classical Control Part 1 1 7.1 Classical Control Outline 7.1.1 Introduction 7.1.2 Virtual Spring Damper 7.1.3 Feedback Control 7.1.4 Model Referenced and Feedforward Control Summary
More information6.1 Sketch the z-domain root locus and find the critical gain for the following systems K., the closed-loop characteristic equation is K + z 0.
6. Sketch the z-domain root locus and find the critical gain for the following systems K (i) Gz () z 4. (ii) Gz K () ( z+ 9. )( z 9. ) (iii) Gz () Kz ( z. )( z ) (iv) Gz () Kz ( + 9. ) ( z. )( z 8. ) (i)
More informationApplication Note #3413
Application Note #3413 Manual Tuning Methods Tuning the controller seems to be a difficult task to some users; however, after getting familiar with the theories and tricks behind it, one might find the
More informationQuanser NI-ELVIS Trainer (QNET) Series: QNET Experiment #02: DC Motor Position Control. DC Motor Control Trainer (DCMCT) Student Manual
Quanser NI-ELVIS Trainer (QNET) Series: QNET Experiment #02: DC Motor Position Control DC Motor Control Trainer (DCMCT) Student Manual Table of Contents 1 Laboratory Objectives1 2 References1 3 DCMCT Plant
More informationB1-1. Closed-loop control. Chapter 1. Fundamentals of closed-loop control technology. Festo Didactic Process Control System
B1-1 Chapter 1 Fundamentals of closed-loop control technology B1-2 This chapter outlines the differences between closed-loop and openloop control and gives an introduction to closed-loop control technology.
More informationIndex. Index. More information. in this web service Cambridge University Press
A-type elements, 4 7, 18, 31, 168, 198, 202, 219, 220, 222, 225 A-type variables. See Across variable ac current, 172, 251 ac induction motor, 251 Acceleration rotational, 30 translational, 16 Accumulator,
More informationRigid Manipulator Control
Rigid Manipulator Control The control problem consists in the design of control algorithms for the robot motors, such that the TCP motion follows a specified task in the cartesian space Two types of task
More informationProfessional Portfolio Selection Techniques: From Markowitz to Innovative Engineering
Massachusetts Institute of Technology Sponsor: Electrical Engineering and Computer Science Cosponsor: Science Engineering and Business Club Professional Portfolio Selection Techniques: From Markowitz to
More informationModelling and Control of Dynamic Systems. PID Controllers. Sven Laur University of Tartu
Modelling and Control of Dynamic Systems PID Controllers Sven Laur University of Tartu Basic structure of a PID controller r[k] + e[k] P I D + u[k] System ĝ[z] y[k] -1 PID controllers are unity feedback
More informationCM 3310 Process Control, Spring Lecture 21
CM 331 Process Control, Spring 217 Instructor: Dr. om Co Lecture 21 (Back to Process Control opics ) General Control Configurations and Schemes. a) Basic Single-Input/Single-Output (SISO) Feedback Figure
More informationNMT EE 589 & UNM ME 482/582 ROBOT ENGINEERING. Dr. Stephen Bruder NMT EE 589 & UNM ME 482/582
NMT EE 589 & UNM ME 482/582 ROBOT ENGINEERING NMT EE 589 & UNM ME 482/582 Simplified drive train model of a robot joint Inertia seen by the motor Link k 1 I I D ( q) k mk 2 kk Gk Torque amplification G
More informationCHAPTER 1 Basic Concepts of Control System. CHAPTER 6 Hydraulic Control System
CHAPTER 1 Basic Concepts of Control System 1. What is open loop control systems and closed loop control systems? Compare open loop control system with closed loop control system. Write down major advantages
More informationDOUBLE ARM JUGGLING SYSTEM Progress Presentation ECSE-4962 Control Systems Design
DOUBLE ARM JUGGLING SYSTEM Progress Presentation ECSE-4962 Control Systems Design Group Members: John Kua Trinell Ball Linda Rivera Introduction Where are we? Bulk of Design and Build Complete Testing
More informationDesign Artificial Nonlinear Controller Based on Computed Torque like Controller with Tunable Gain
World Applied Sciences Journal 14 (9): 1306-1312, 2011 ISSN 1818-4952 IDOSI Publications, 2011 Design Artificial Nonlinear Controller Based on Computed Torque like Controller with Tunable Gain Samira Soltani
More informationA Simple PID Control Design for Systems with Time Delay
Industrial Electrical Engineering and Automation CODEN:LUTEDX/(TEIE-7266)/1-16/(2017) A Simple PID Control Design for Systems with Time Delay Mats Lilja Division of Industrial Electrical Engineering and
More informationRobotics. Dynamics. Marc Toussaint U Stuttgart
Robotics Dynamics 1D point mass, damping & oscillation, PID, dynamics of mechanical systems, Euler-Lagrange equation, Newton-Euler recursion, general robot dynamics, joint space control, reference trajectory
More informationChapter 8. Feedback Controllers. Figure 8.1 Schematic diagram for a stirred-tank blending system.
Feedback Controllers Figure 8.1 Schematic diagram for a stirred-tank blending system. 1 Basic Control Modes Next we consider the three basic control modes starting with the simplest mode, proportional
More informationLecture 25: Tue Nov 27, 2018
Lecture 25: Tue Nov 27, 2018 Reminder: Lab 3 moved to Tuesday Dec 4 Lecture: review time-domain characteristics of 2nd-order systems intro to control: feedback open-loop vs closed-loop control intro to
More informationDC-motor PID control
DC-motor PID control This version: November 1, 2017 REGLERTEKNIK Name: P-number: AUTOMATIC LINKÖPING CONTROL Date: Passed: Chapter 1 Introduction The purpose of this lab is to give an introduction to
More informationProcess Control J.P. CORRIOU. Reaction and Process Engineering Laboratory University of Lorraine-CNRS, Nancy (France) Zhejiang University 2016
Process Control J.P. CORRIOU Reaction and Process Engineering Laboratory University of Lorraine-CNRS, Nancy (France) Zhejiang University 206 J.P. Corriou (LRGP) Process Control Zhejiang University 206
More informationRobotics & Automation. Lecture 25. Dynamics of Constrained Systems, Dynamic Control. John T. Wen. April 26, 2007
Robotics & Automation Lecture 25 Dynamics of Constrained Systems, Dynamic Control John T. Wen April 26, 2007 Last Time Order N Forward Dynamics (3-sweep algorithm) Factorization perspective: causal-anticausal
More informationControl. CSC752: Autonomous Robotic Systems. Ubbo Visser. March 9, Department of Computer Science University of Miami
Control CSC752: Autonomous Robotic Systems Ubbo Visser Department of Computer Science University of Miami March 9, 2017 Outline 1 Control system 2 Controller Images from http://en.wikipedia.org/wiki/feed-forward
More informationNonlinear PD Controllers with Gravity Compensation for Robot Manipulators
BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 4, No Sofia 04 Print ISSN: 3-970; Online ISSN: 34-408 DOI: 0.478/cait-04-00 Nonlinear PD Controllers with Gravity Compensation
More informationFeedback Control Systems
ME Homework #0 Feedback Control Systems Last Updated November 06 Text problem 67 (Revised Chapter 6 Homework Problems- attached) 65 Chapter 6 Homework Problems 65 Transient Response of a Second Order Model
More informationPRECISION CONTROL OF LINEAR MOTOR DRIVEN HIGH-SPEED/ACCELERATION ELECTRO-MECHANICAL SYSTEMS. Bin Yao
PRECISION CONTROL OF LINEAR MOTOR DRIVEN HIGH-SPEED/ACCELERATION ELECTRO-MECHANICAL SYSTEMS Bin Yao Intelligent and Precision Control Laboratory School of Mechanical Engineering Purdue University West
More informationAcknowledgements. Control System. Tracking. CS122A: Embedded System Design 4/24/2007. A Simple Introduction to Embedded Control Systems (PID Control)
Acknowledgements A Simple Introduction to Embedded Control Systems (PID Control) The material in this lecture is adapted from: F. Vahid and T. Givargis, Embedded System Design A Unified Hardware/Software
More informationSurvey of Methods of Combining Velocity Profiles with Position control
Survey of Methods of Combining Profiles with control Petter Karlsson Mälardalen University P.O. Box 883 713 Västerås, Sweden pkn91@student.mdh.se ABSTRACT In many applications where some kind of motion
More informationUNIVERSITY OF BOLTON SCHOOL OF ENGINEERING BSC (HONS) MECHATRONICS TOP-UP SEMESTER 1 EXAMINATION 2017/2018 ADVANCED MECHATRONIC SYSTEMS
ENG08 UNIVERSITY OF BOLTON SCHOOL OF ENGINEERING BSC (HONS) MECHATRONICS TOP-UP SEMESTER EXAMINATION 07/08 ADVANCED MECHATRONIC SYSTEMS MODULE NO: MEC600 Date: 7 January 08 Time: 0.00.00 INSTRUCTIONS TO
More informationAdaptive Robust Tracking Control of Robot Manipulators in the Task-space under Uncertainties
Australian Journal of Basic and Applied Sciences, 3(1): 308-322, 2009 ISSN 1991-8178 Adaptive Robust Tracking Control of Robot Manipulators in the Task-space under Uncertainties M.R.Soltanpour, M.M.Fateh
More informationCourse Summary. The course cannot be summarized in one lecture.
Course Summary Unit 1: Introduction Unit 2: Modeling in the Frequency Domain Unit 3: Time Response Unit 4: Block Diagram Reduction Unit 5: Stability Unit 6: Steady-State Error Unit 7: Root Locus Techniques
More informationLecture 12. Upcoming labs: Final Exam on 12/21/2015 (Monday)10:30-12:30
289 Upcoming labs: Lecture 12 Lab 20: Internal model control (finish up) Lab 22: Force or Torque control experiments [Integrative] (2-3 sessions) Final Exam on 12/21/2015 (Monday)10:30-12:30 Today: Recap
More informationECE 388 Automatic Control
Lead Compensator and PID Control Associate Prof. Dr. of Mechatronics Engineeering Çankaya University Compulsory Course in Electronic and Communication Engineering Credits (2/2/3) Course Webpage: http://ece388.cankaya.edu.tr
More informationLaboratory 11 Control Systems Laboratory ECE3557. State Feedback Controller for Position Control of a Flexible Joint
Laboratory 11 State Feedback Controller for Position Control of a Flexible Joint 11.1 Objective The objective of this laboratory is to design a full state feedback controller for endpoint position control
More informationRobot Manipulator Control. Hesheng Wang Dept. of Automation
Robot Manipulator Control Hesheng Wang Dept. of Automation Introduction Industrial robots work based on the teaching/playback scheme Operators teach the task procedure to a robot he robot plays back eecute
More informationReglerteknik, TNG028. Lecture 1. Anna Lombardi
Reglerteknik, TNG028 Lecture 1 Anna Lombardi Today lecture We will try to answer the following questions: What is automatic control? Where can we nd automatic control? Why do we need automatic control?
More informationObjective: To study P, PI, and PID temperature controller for an oven and compare their performance. Name of the apparatus Range Quantity
Objective: To study P, PI, and PID temperature controller for an oven and compare their. Apparatus Used: Name of the apparatus Range Quantity 1. Temperature Controller System 1 PID Kp (0-10) Kd(0-20) Ki(0-0.02)
More informationControl of Electromechanical Systems
Control of Electromechanical Systems November 3, 27 Exercise Consider the feedback control scheme of the motor speed ω in Fig., where the torque actuation includes a time constant τ A =. s and a disturbance
More informationCHBE320 LECTURE XI CONTROLLER DESIGN AND PID CONTOLLER TUNING. Professor Dae Ryook Yang
CHBE320 LECTURE XI CONTROLLER DESIGN AND PID CONTOLLER TUNING Professor Dae Ryook Yang Spring 2018 Dept. of Chemical and Biological Engineering 11-1 Road Map of the Lecture XI Controller Design and PID
More informationRobust Control of Robot Manipulator by Model Based Disturbance Attenuation
IEEE/ASME Trans. Mechatronics, vol. 8, no. 4, pp. 511-513, Nov./Dec. 2003 obust Control of obot Manipulator by Model Based Disturbance Attenuation Keywords : obot manipulators, MBDA, position control,
More informationAutomatic Generation Control. Meth Bandara and Hassan Oukacha
Automatic Generation Control Meth Bandara and Hassan Oukacha EE194 Advanced Controls Theory February 25, 2013 Outline Introduction System Modeling Single Generator AGC Going Forward Conclusion Introduction
More informationPositioning Servo Design Example
Positioning Servo Design Example 1 Goal. The goal in this design example is to design a control system that will be used in a pick-and-place robot to move the link of a robot between two positions. Usually
More informationPower Rate Reaching Law Based Second Order Sliding Mode Control
International OPEN ACCESS Journal Of Modern Engineering Research (IJMER) Power Rate Reaching Law Based Second Order Sliding Mode Control Nikam A.E 1. Sankeshwari S.S 2. 1 P.G. Department. (Electrical Control
More informationTuning of Internal Model Control Proportional Integral Derivative Controller for Optimized Control
Tuning of Internal Model Control Proportional Integral Derivative Controller for Optimized Control Thesis submitted in partial fulfilment of the requirement for the award of Degree of MASTER OF ENGINEERING
More informationPerformance of Feedback Control Systems
Performance of Feedback Control Systems Design of a PID Controller Transient Response of a Closed Loop System Damping Coefficient, Natural frequency, Settling time and Steady-state Error and Type 0, Type
More informationAnswers for Homework #6 for CST P
Answers for Homework #6 for CST 407 02P Assigned 5/10/07, Due 5/17/07 Constructing Evans root locus diagrams in Scilab Root Locus It is easy to construct a root locus of a transfer function in Scilab.
More informationFUZZY LOGIC CONTROL Vs. CONVENTIONAL PID CONTROL OF AN INVERTED PENDULUM ROBOT
http:// FUZZY LOGIC CONTROL Vs. CONVENTIONAL PID CONTROL OF AN INVERTED PENDULUM ROBOT 1 Ms.Mukesh Beniwal, 2 Mr. Davender Kumar 1 M.Tech Student, 2 Asst.Prof, Department of Electronics and Communication
More informationRobotics. Dynamics. University of Stuttgart Winter 2018/19
Robotics Dynamics 1D point mass, damping & oscillation, PID, dynamics of mechanical systems, Euler-Lagrange equation, Newton-Euler, joint space control, reference trajectory following, optimal operational
More informationChapter 2. Classical Control System Design. Dutch Institute of Systems and Control
Chapter 2 Classical Control System Design Overview Ch. 2. 2. Classical control system design Introduction Introduction Steady-state Steady-state errors errors Type Type k k systems systems Integral Integral
More informationFEEDBACK CONTROL SYSTEMS
FEEDBAC CONTROL SYSTEMS. Control System Design. Open and Closed-Loop Control Systems 3. Why Closed-Loop Control? 4. Case Study --- Speed Control of a DC Motor 5. Steady-State Errors in Unity Feedback Control
More informationRobust Controller Design for Speed Control of an Indirect Field Oriented Induction Machine Drive
Leonardo Electronic Journal of Practices and Technologies ISSN 1583-1078 Issue 6, January-June 2005 p. 1-16 Robust Controller Design for Speed Control of an Indirect Field Oriented Induction Machine Drive
More informationEEL2216 Control Theory CT1: PID Controller Design
EEL6 Control Theory CT: PID Controller Design. Objectives (i) To design proportional-integral-derivative (PID) controller for closed loop control. (ii) To evaluate the performance of different controllers
More informationProcess Solutions. Process Dynamics. The Fundamental Principle of Process Control. APC Techniques Dynamics 2-1. Page 2-1
Process Dynamics The Fundamental Principle of Process Control APC Techniques Dynamics 2-1 Page 2-1 Process Dynamics (1) All Processes are dynamic i.e. they change with time. If a plant were totally static
More informationIterative Controller Tuning Using Bode s Integrals
Iterative Controller Tuning Using Bode s Integrals A. Karimi, D. Garcia and R. Longchamp Laboratoire d automatique, École Polytechnique Fédérale de Lausanne (EPFL), 05 Lausanne, Switzerland. email: alireza.karimi@epfl.ch
More informationEE3CL4: Introduction to Linear Control Systems
1 / 17 EE3CL4: Introduction to Linear Control Systems Section 7: McMaster University Winter 2018 2 / 17 Outline 1 4 / 17 Cascade compensation Throughout this lecture we consider the case of H(s) = 1. We
More informationAcceleration Feedback
Acceleration Feedback Mechanical Engineer Modeling & Simulation Electro- Mechanics Electrical- Electronics Engineer Sensors Actuators Computer Systems Engineer Embedded Control Controls Engineer Mechatronic
More informationChapter 12. Feedback Control Characteristics of Feedback Systems
Chapter 1 Feedbac Control Feedbac control allows a system dynamic response to be modified without changing any system components. Below, we show an open-loop system (a system without feedbac) and a closed-loop
More informationSRV02-Series Rotary Experiment # 1. Position Control. Student Handout
SRV02-Series Rotary Experiment # 1 Position Control Student Handout SRV02-Series Rotary Experiment # 1 Position Control Student Handout 1. Objectives The objective in this experiment is to introduce the
More informationObserver Based Friction Cancellation in Mechanical Systems
2014 14th International Conference on Control, Automation and Systems (ICCAS 2014) Oct. 22 25, 2014 in KINTEX, Gyeonggi-do, Korea Observer Based Friction Cancellation in Mechanical Systems Caner Odabaş
More informationIntroduction to centralized control
ROBOTICS 01PEEQW Basilio Bona DAUIN Politecnico di Torino Control Part 2 Introduction to centralized control Independent joint decentralized control may prove inadequate when the user requires high task
More informationOptimization of PI Parameters for Speed Controller of a Permanent Magnet Synchronous Motor by using Particle Swarm Optimization Technique
Optimization of PI Parameters for Speed Controller of a Permanent Magnet Synchronous Motor by using Particle Swarm Optimization Technique Aiffah Mohammed 1, Wan Salha Saidon 1, Muhd Azri Abdul Razak 2,
More informationSliding Mode Control: A Comparison of Sliding Surface Approach Dynamics
Ben Gallup ME237 Semester Project Sliding Mode Control: A Comparison of Sliding Surface Approach Dynamics Contents Project overview 2 The Model 3 Design of the Sliding Mode Controller 3 4 Control Law Forms
More informationControl of Manufacturing Processes
Control of Manufacturing Processes Subject 2.830 Spring 2004 Lecture #18 Basic Control Loop Analysis" April 15, 2004 Revisit Temperature Control Problem τ dy dt + y = u τ = time constant = gain y ss =
More informationCHAPTER 3 TUNING METHODS OF CONTROLLER
57 CHAPTER 3 TUNING METHODS OF CONTROLLER 3.1 INTRODUCTION This chapter deals with a simple method of designing PI and PID controllers for first order plus time delay with integrator systems (FOPTDI).
More informationSpeed Control of Torsional Drive Systems with Backlash
Speed Control of Torsional Drive Systems with Backlash S.Thomsen, F.W. Fuchs Institute of Power Electronics and Electrical Drives Christian-Albrechts-University of Kiel, D-2443 Kiel, Germany Phone: +49
More informationPassivity-based Control of Euler-Lagrange Systems
Romeo Ortega, Antonio Loria, Per Johan Nicklasson and Hebertt Sira-Ramfrez Passivity-based Control of Euler-Lagrange Systems Mechanical, Electrical and Electromechanical Applications Springer Contents
More informationAlireza Mousavi Brunel University
Alireza Mousavi Brunel University 1 » Control Process» Control Systems Design & Analysis 2 Open-Loop Control: Is normally a simple switch on and switch off process, for example a light in a room is switched
More informationLecture 10: Proportional, Integral and Derivative Actions
MCE441: Intr. Linear Control Systems Lecture 10: Proportional, Integral and Derivative Actions Stability Concepts BIBO Stability and The Routh-Hurwitz Criterion Dorf, Sections 6.1, 6.2, 7.6 Cleveland State
More informationIntroduction to centralized control
Industrial Robots Control Part 2 Introduction to centralized control Independent joint decentralized control may prove inadequate when the user requires high task velocities structured disturbance torques
More informationSolutions for Tutorial 5 Dynamic Behavior of Typical Dynamic Systems
olutions for Tutorial 5 Dynamic Behavior of Typical Dynamic ystems 5.1 First order ystem: A model for a first order system is given in the following equation. dy dt X in X out (5.1.1) What conditions have
More informationVehicle longitudinal speed control
Vehicle longitudinal speed control Prof. R.G. Longoria Department of Mechanical Engineering The University of Texas at Austin February 10, 2015 1 Introduction 2 Control concepts Open vs. Closed Loop Control
More informationWhere we ve been, where we are, and where we are going next.
6.302.0x, January 2016 Notes Set 2 1 MASSACHVSETTS INSTITVTE OF TECHNOLOGY Department of Electrical Engineering and Computer Science 6.302.0x Introduction to Feedback System Design January 2016 Notes Set
More informationApplication of singular perturbation theory in modeling and control of flexible robot arm
Research Article International Journal of Advanced Technology and Engineering Exploration, Vol 3(24) ISSN (Print): 2394-5443 ISSN (Online): 2394-7454 http://dx.doi.org/10.19101/ijatee.2016.324002 Application
More informationDr Ian R. Manchester Dr Ian R. Manchester AMME 3500 : Review
Week Date Content Notes 1 6 Mar Introduction 2 13 Mar Frequency Domain Modelling 3 20 Mar Transient Performance and the s-plane 4 27 Mar Block Diagrams Assign 1 Due 5 3 Apr Feedback System Characteristics
More informationImproving the Control System for Pumped Storage Hydro Plant
011 International Conference on Computer Communication and Management Proc.of CSIT vol.5 (011) (011) IACSIT Press, Singapore Improving the Control System for Pumped Storage Hydro Plant 1 Sa ad. P. Mansoor
More informationDesign of Decentralised PI Controller using Model Reference Adaptive Control for Quadruple Tank Process
Design of Decentralised PI Controller using Model Reference Adaptive Control for Quadruple Tank Process D.Angeline Vijula #, Dr.N.Devarajan * # Electronics and Instrumentation Engineering Sri Ramakrishna
More informationIn-Process Control in Thermal Rapid Prototyping
In-Process Control in Thermal Rapid Prototyping Nadya Peek in MAS.864, May 2011 May 16, 2011 Abstract This report compares thermal control algorithms for curing carbon fibre and epoxy resin composite parts
More informationTemperature Controllers
Introduction This handout examines the performance of several systems for controlling the temperature of an oven by adjusting the heater power a much harder task than it might first appear. The problem
More informationIntegrator Windup
3.5.2. Integrator Windup 3.5.2.1. Definition So far we have mainly been concerned with linear behaviour, as is often the case with analysis and design of control systems. There is, however, one nonlinear
More informationRhythmic Robot Arm Control Using Oscillators
Rhythmic Robot Arm Control Using Oscillators Matthew M. Williamson MIT AI Lab, 545 Technology Square, Cambridge, MA 2139 http://www.ai.mit.edu/people/matt Abstract This paper presents an approach to robot
More informationControl Theory. Noel Welsh. 26 October Noel Welsh () Control Theory 26 October / 17
Control Theory Noel Welsh 26 October 2010 Noel Welsh () Control Theory 26 October 2010 1 / 17 Announcements Assignments were due on Monday, except for one team that has an extension. Marking will be delayed
More information