Introduction to Systems Theory and Control Systems


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1 Introduction to Systems Theory and Control Systems Paula Raica Department of Automation Dorobantilor Str., room C21, tel: Baritiu Str., room C14, tel: Technical University of ClujNapoca
2 Course organization Lectures: 2h/week, room D21 (Baritiu) Lab exercises: 4h / 2 weeks, Dorobatilor Str., yellow building on the right. Lab C01: ground floor Taught by: Paula Raica (lectures) Lab: Group When Where Who Friday C01 Paula Raica Thursday C01 Iulia Clitan Tuesday C01 Iulia Clitan Thursday 812 C01 Alexandru Codrean (odd) Zoltan Nagy (even) Tuesday C01 Paula Raica (odd) Zoltan Nagy (even)
3 Systems Theory. Grading Point accumulation Exams (see Course calendar and grading ): lab tests  including homework assignments (optional) midterm exam (optional) final exam Control Challenge Lab work policy Prerequisites: Differential equations, Linear algebra, Laplace transform, Complex numbers
4 Course objective The general objective of the course is to introduce the fundamental principles of linear system modeling, analysis and feedback control and to evaluate feedback control systems with desired behavior.
5 Systems Theory System : A set or arrangement of entities so related or connected so as to form a unity or organic whole. (Iberall) Systems theory: interdisciplinary field which studies systems. Founded by Ludwig von Bertalanffy, William Ross Ashby and others between the 1940s and the 1970s on principles from physics, biology and engineering Grew into numerous fields including philosophy, sociology, organizational theory, management and economics among others. Cybernetics is a related field, sometimes considered as a part of systems theory.
6 Control Systems Engineering Understanding systems Control of systems Modeling and control of modern, complex, interrelated systems traffic control systems, chemical processes, robotic systems industrial automation systems. Control Systems Engineering is based on the foundations of feedback theory and linear systems analysis.
7 Historical Background 300 B.C, Greece : development of the float regulator mechanisms, mid 1860s, J.C.Maxwell: the first formal study of the Theory of Control, mid 1890s, E.J. Routh and A.M. Lyapunov: Routh Stability Test and the Lyapunov Stability Criteria 1930s, H. Nyquist (Bell Telephone Laboratories): applied frequency analysis to control systems design 1930, H.W.Bode: designed electronic amplifiers using the concepts of feedback control 1950s onwards: control theory evolved with new mathematical techniques applied and computer technology.
8 Discipline of Control Systems Multidisciplinary field. Covers Mechanical Engineering, Chemical Engineering, Electrical and Electronic Engineering, Environmental, Civil, Physics, Economics and Finance, Artificial Intelligence and Computer Science Taught in the main stream Engineering and Physics courses ComputerControlled Systems: complex field in control engineering. Concepts overlap with the branch of Physics and Electrical Engineering known as Digital Signal Processing (DSP) and Communication Systems
9 Dynamical system A dynamical system is a system whose behavior changes over time, often in response to external stimulation or forcing. Inputs (cause) = quantities that are acting on the system from the environment Outputs (effect) = the results of the input acting on the system. Inputs, outputs = signals
10 Example: Helicopter Figure: Helicopter Inputs: the power produced by the engines the pilot control inputs the wind = disturbance Outputs the actual position (coordinates x, y, z) orientation (roll, pitch, yaw) velocity
11 Terminology Control of an inverted pendulum Figure: Elements in a control system Block diagram. Input. Output. Plant (Process). Measurement. Signals. Setpoint (Reference value). Comparator. Compensator. Actuator. Disturbance. Openloop. Closedloop. Negative Feedback.
12 Closedloop control system for HDD Figure: A hard disk control system
13 Examples Oven temperature control (closedloop) Washing machine: (openloop) Central heating: (closedloop) vs. radiator( openloop) Automobile steering control system Desired course of travel Error Driver Steering mechanism Automobile Actual course of travel Actual direction of travel Desired direction of travel Measurement, visual and tactile
14 Applications Figure: Maglev Figure: Traffic control
15 Applications
16 Applications Figure: Chemical industry, energy
17 Applications
18 Course contents Mathematical models of linear time invariant systems systems. Transfer functions, statespace models, block diagram models Analysis of linear continuous systems. Characteristics and performance. Stability of linear continuous systems. System analysis using root locus. Frequency response. Bode diagrams. Controller design. Leadlag compensation. PID control. State feedback Sampleddata systems. Digital control systems.
19 Bibliography R.C.Dorf, R.Bishop, Modern Control Systems, AddisonWesley, 2011; K.Ogata, Modern Control Engineering, Prentice Hall, K.Dutton, S. Thompson, B. Barraclough, The Art of Control Engineering, AddisonWesley, 1997 M. Hăngănuţ, Teoria sistemelor, UTCluj, 1996 T. Coloşi, Elemente de teoria sistemelor si reglaj automat, UTCluj, 1981
20 Introduction to Control System Modeling Paula Raica Department of Automation Dorobantilor Str., room C21, tel: Baritiu Str., room C14, tel: Technical University of ClujNapoca Introduction to Control System Modeling
21 Introduction A mathematical model is an equation or set of equations which adequately describes the behavior of a system. Two approaches to finding the model: Lumpedparameter modeling: for each element a mathematical description is established from the physical laws. System identification: an experiment can be carried out and a mathematical model can be found from the results. The important relationship is that between the manipulated inputs and measurable outputs. u(t) input Dynamic System y(t) output Introduction to Control System Modeling
22 Lumpedparameter models The systems studied in this course are: Examples. Linear  must obey the principle of superposition Stationary (or time invariant)  the parameters inside the element must not vary with time. Deterministic  The outputs of the system at any time can be determined from a knowledge of the system s inputs up to that time. The resistor: i(t) = 1 R v(t) The inductor: i(t) = 1 L v(t)dt or v(t) = L di(t) dt The capacitor: i(t) = C dv(t) dt Introduction to Control System Modeling
23 Examples Springmassdamper system Friction f Mass M k displacement y(t) Force r(t) M d2 y(t) dt 2 +f dy(t) +ky(t) = r(t) dt where: f is the friction coefficient, M  the mass, k  the stiffness of the linear spring. Introduction to Control System Modeling
24 Principle of superposition A system is defined as linear in terms of the system excitation and response. Additivity x 1 (t) y 1 (t) x 2 (t) y 2 (t) x 1 (t)+x 2 (t) y 1 (t)+y 2 (t) Homogeneity x(t) y(t) mx(t) my(t) Introduction to Control System Modeling
25 Linear Approximation Nonlinear system Nonlinear system y = x 2 y = mx +b Linear about an operating point x 0,y 0 for small changes x and y. When x = x 0 + x and y = y 0 + y: y 0 + y = mx 0 +m x +b and therefore y = m x Introduction to Control System Modeling
26 Linear Approximation If the dependent variable y depends upon several excitation variables x 1,x 2,...,x n : y = g(x 1,x 2,...,x n ). The Taylor series expansion about the operating point x 10,x 20,...,x n0 (the higherorder terms are neglected): y = g(x 10,x 20,...,x n0 )+ g x 1 x=x0 (x 1 x 10 )+ + g x 2 x=x0 (x 2 x 20 )+...+ g x n x=x0 (x n x n0 ) where x 0 is the operating point. Introduction to Control System Modeling
27 Example  Pendulum oscillator The torque on the mass is: T = MgLsin(x) The equilibrium condition for the mass is x 0 = 0 o. T T 0 = MgL sinx x x=x 0 (x x 0 ), where T 0 = 0. T = MgL(cos0 o )(x 0 o ) = MgLx The approximation is reasonably accurate for π/4 x π/4. Introduction to Control System Modeling
28 Linear Approximation Input x(t) and a response y(t): y(t) = g(x(t)) Taylor series expansion about the operating point x 0 : y = g(x) = g(x 0 )+ dg dx x x 0 x=x 0 + higher order terms 1! The slope at the operating point, m = dg dx x=x 0, y = g(x 0 )+ dg dx x=x 0 (x x 0 ) = y 0 +m(x x 0 ), Finally, this equation can be rewritten as the linear equation (y y 0 ) = m(x x 0 ) or y = m x Introduction to Control System Modeling
29 Example. Magnetic levitation The system: an ironcore electromagnet and the steel ball levitated by the electromagnet. Electromagnetic force F m : F m = C i2 (t) z 2 (t) Introduction to Control System Modeling
30 Example. Magnetic levitation Input: the current through the coils of the electromagnet i(t) Output: the displacement of the ball z(t) The equation of motion for the ball: Nonlinear model!! m z(t) = mg C i2 (t) z 2 (t) Introduction to Control System Modeling
31 Example. Magnetic levitation  linearization Rewrite the equation: g( z(t),z(t),i(t)) = m z(t) mg +C i2 (t) z 2 (t) = 0 Choose an operating point: ( z 0, z 0, i 0 ) such that m z 0 = mg C i2 0 z 2 0 Write the truncated Taylor series around the operating point: 0 = g( z(t),z(t),i(t)) g( z 0,z 0,i 0 )+ g z ( z 0, z 0, i 0 )( z(t) z 0 )+ + g z ( z 0, z 0, i 0 )(z(t) z 0 )+ + g i ( z 0, z 0, i 0 )(i(t) i 0 ) Introduction to Control System Modeling
32 Example. Magnetic levitation  linearization Compute the partial derivatives and evaluate them at the operating point. The Taylor series expansion is: 0 0+m ( z(t) z 0 ) 2C i2 0 z0 3 (z(t) z 0 )+2C i 0 z0 2 (i(t) i 0 ) Denote the variations around the operating point by: z(t) = z(t) z 0, z(t) = z(t) z 0 and i(t) = i(t) i 0 Linear differential equation in terms of z(t), z(t), i(t): m z(t) = 2C i2 0 z0 3 z(t) 2C i 0 z0 2 i(t) Introduction to Control System Modeling
33 To do Review: Differential equations Linear algebra Laplace transform Check the course webpage: Download the exercises (ControlEngineering.pdf) and detailed lecture notes Introduction to Control System Modeling
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