Control System Design


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1 ELEC ENG 4CL4: Control System Design Notes for Lecture #1 Monday, January 6, 2003
2 Instructor: Dr. Ian C. Bruce Room CRL229, Ext Office Hours: TBA Teaching Assistants: Kamran Mustafa Jennifer Ko Room CRL205, Ext Office Hours: TBA Web Site:
3 Proposed Course Content: Introduction to the Principles of Feedback (1 hour) ContinuousTime Signals and Systems (1 hour) Review of the Laplace Transform, and the evaluation of time and frequency responses from poles and zeros Analysis of SingleInput SingleOutput (SISO) Control Loops (2 hrs) Sensitivity Functions Root Locus, Frequency Response Techniques Relative Stability Robustness Classical ProportionalIntegralDerivative (PID) Control (5 hours) ZieglerNichols Methods LeadLag compensators Industrial application: Control of a distillation column
4 Course Content (cont.): Performance Limitations (4 hours) Sensors and actuators Bode's integral constraints Examples of design tradeoffs Models for SampledData Control Systems (2 hours) Advanced SISO Control Open loop inversion and affine parameterization of stabilizing controllers (2 hours) Optimization based designs (2 hours) Linear state space models (4 hours) Controller synthesis via state space methods (3 hours) Introduction to nonlinear control (4 hours)
5 Course Content (cont.): MultiInput MultiOutput (MIMO) Control (8 hours) Models for multivariable systems Stability, Frequency response, robustness Decentralized control Optimal multivariable control systems Stateestimate feedback Linear Quadratic Regulator (LQR) Achieving integral action in LQR synthesis Industrial Applications (Total Course = 39 hours)
6 Textbook: G. C. Goodwin, S. F. Graebe, and M. E. Salgado, "Control System Design," Prentice Hall, (New and used copies available at Titles Bookstore.) Chapters 2 and 3 will only be covered briefly in lectures, but you should read these chapters thoroughly.
7 Prerequisites: Courses: ELEC ENG 3TP4 Competencies: Linear signals and systems analysis, esp. Laplace transform, differential equations, statespace models Matrix algebra Matlab/Simulink
8 Proposed Assessment: Weekly Homework (10%) Labs (15%) Project (15%) Midterm (20%) Final (40%) You will be notified in advance if any changes are to be made to this assessment scheme.
9 Lectures: There will be 39 onehour lectures (3 per week) in BSBB103 on: Mondays 11:30am12:30pm, Wednesdays 11:30am12:30pm, and Fridays 1:302:30pm. Lecture notes in PDF format will be posted on the course web site before each lecture.
10 Tutorials: There will be 1 onehour tutorial per week. The class is split into two sections for tutorials: Section T1 is in LSB130A on Wednesdays 10:3011:30am Section T2 is in ABB270 on Fridays 10:3011:30am
11 Labs: There will be 5 threehour labs in T every second week on Mondays 2:305:30pm. The two tutorial sections (T1 and T2) will alternate weeks for labs. Lab descriptions will be made available on this web site the week before the lab, and lab reports are to be submitted at the tutorial the week after the lab. The majority of the labs will be Matlab/Simulink based simulations that can also be performed outside of lab hours, but the TAs will only provide assistance during lab hours.
12 Weekly Homework: There will be 10 weekly homework assignments. Homework assignments will be given out at the end of the tutorial each week, and homework reports are to be submitted at the start of the tutorial the following week. TBA Project:
13 Policy Reminders: The Faculty of Engineering is concerned with ensuring an environment that is free of all adverse discrimination. If there is a problem, that cannot be resolved by discussion among the persons concerned, individuals are reminded they should contact the Departmental Chair, the Sexual Harassment Officer or the Human Rights Consultant, as soon as possible. Students are reminded that they should read and comply with the Statement on Academic Ethics and the Senate Resolutions on Academic Dishonesty as found in the Senate Policy Statements distributed at registration and available at the senate office.
14 Chapter 2 Introduction to the Principles of Feedback Topics to be covered include: An industrial motivational example; A statement of the fundamental nature of the control problem; The idea of inversion as the central ingredient in solving control problems; Evolution from open loop inversion to closed loop feedback solutions.
15 We will see that feedback is a key tool that can be used to modify the behaviour of a system. This behaviour altering effect of feedback is a key mechanism that control engineers exploit deliberately to achieve the objective of acting on a system to ensure that the desired performance specifications are achieved.
16 A motivating industrial example We first present a simplified, yet essentially authentic, example of an industrial control problem. The example, taken from the steel industry, is of a particular nature, however the principal elements of specifying a desired behaviour, modeling and the necessity for tradeoff decisions are generic.
17 Photograph of Bloom Caster
18 Process schematic of an Industrial Bloom Caster
19 Continuous caster. Typical bloom (left) and simplified diagram (right) tundish with molten steel t l control valve mould primary cooling w continuously withdrawn, semisolid strand
20 Operators viewing the mould
21 The cast strip in the secondary cooling chamber
22 Performance specifications The key performance goals for this problem are: Safety: Clearly, the mould level must never be in danger of overflowing or emptying as either case would result in molten metal spilling with disastrous consequences. Profitability: Aspects which contribute to this requirement include: Product quality Maintenance Throughput
23 Modeling To make progress on the control system design problem, it is first necessary to gain an understanding of how the process operates. This understanding is typically expressed in the form of a mathematical model. q h* h( t) v( t) σ ( t) q in out ( t) ( t) : : : : : : commanded level of steel in mould actual level of steel in mould valve position casting speed inflow of matter into the mould outflow of matter from the mould
24 Model as simple tank Molten Steel Tundish Valve Mould Level Cooling Water
25 Block diagram of the simplified mould level dynamics, sensors and actuators These variables are related as shown below: Casting speed measurement Inflow from control valve + Outflow due to casting speed Measured mould level + Mould level + Meas. noise
26 Feedback and Feedforward We will find later that the core idea in control is that of inversion. Moreover, inversion can be conveniently achieved by the use of two key mechanisms (namely, feedback and feedforward).
27 Figure 2.4: Model of the simplified mould level control with feedforward compensation for casting speed Suggested Control Strategy: Commanded mould level + K 1 Casting speed measurement K Inflow from control valve Outflow due to casting speed + Mould level Meas. noise Measured mould level + Note that this controller features joint feedback and a preemptive action (feedforward).
28 A first indication of tradeoffs On simulating the performance of the above control loop for K=1 and K=5, see Figure 2.5, we find that the smaller controller gain (K=1) results in a slower response to a change in the mould level setpoint. On the other hand, the larger controller gain (K=5), results in a faster response but also increases the effects of measurement noise as seen by the less steady level control and by the significantly more aggressive valve movements.
29 Figure 2.5: A first indication of tradeoffs: Increased responsiveness to setpoint changes also increases sensitivity to measurement noise and actuator wear. 1.4 Mould level K=5 K= Valve command K=1 0 K= Time [s]
30 Question We may ask if these tradeoffs are unavoidable or whether we could improve on the situation by such measures as: better modelling more sophisticated control system design This will be the subject of the rest of our deliberations. (Aside: Actually the tradeoff is fundamental as we shall see presently).
31 Definition of the control problem Abstracting from the above particular problem, we can introduce: Definition 2.1: The central problem in control is to find a technically feasible way to act on a given process so that the process behaves, as closely as possible, to some desired behaviour. Furthermore, this approximate behaviour should be achieved in the face of uncertainty of the process and in the presence of uncontrollable external disturbances acting on the process.
32 Prototype solution to the control problem via inversion One particularly simple, yet insightful way of thinking about control problems is via inversion. To describe this idea we argue as follows: say that we know what effect an action at the input of a system produces at the output, and say that we have a desired behaviour for the system output, then one simply needs to invert the relationship between input and output to determine what input action is necessary to achieve the desired output behaviour.
33 Figure 2.6: Conceptual controller The above idea is captured in the following diagram: d r + f 1 u f + + y Conceptual controller Plant
34 We will actually find that the inverse solution given on the last slide holds very generally. Thus, all controllers implicitly generate an inverse of the process, in so far that this is feasible. However, the details of controllers will differ with respect to the mechanism used to generate the required approximate inverse.
35 High gain feedback and inversion We next observe that there is a rather intriguing property of feedback, namely that it implicitly generates an approximate inverse of dynamic transformations, without the inversion having to be carried out explicitly. r u y h Plant + z f Figure 2.7: Realisation of conceptual controller The loop implements an approximate inverse of f ο, i.e. u = f r, if r  h 1 u r
36 Chapter 2 Specifically, u f r h z r h u = = or u f r u h = 1 Hence r f u h r f u = Provided is small, i.e. is high gain. u h 1 h
37 The above equation is satisfied if h 1 u is large. We conclude that an approximate inverse is generated provided we place the model of the system in a high gain feedback loop.
38 Example 2.3 Assume that a plant can be described by the model dy( t) + 2 dt y( t) = u( t) and that a control law is required to ensure that y(t) follows a slowly varying reference. One way to solve this problem is to construct an inverse for the model which is valid in the low frequency region. Using the architecture in Figure 2.7, we obtain an approximate inverse, provided that h ο has large gain in the low frequency region.
39 Figure 2.8: Tank level control using approximate inversion Simulating the resultant controller gives the results below: 2 Ref. and plant output r(t) y(t) Time [s]
40 From open to closed loop architectures Unfortunately, the above methodology will not lead to a satisfactory solution to the control problem unless: the model on which the design of the controller has been based is a very good representation of the plant, the model and its inverse are stable, and disturbances and initial conditions are negligible. We are thus motivated to find an alternative solution to the problem which retains the key features but which does not suffer from the above drawbacks.
41 Figure 2.9: Open loop control with builtin inverse r(t) + Feedback gain A u(t) Plant y(t) Model Open loop controller Figure 2.10: Closed loop control r(t) + e(t) u(t) A y(t) Feedback gain Plant
42 The first thing to note is that, provided the model represents the plant exactly, and that all signals are bounded (i.e. the loop is stable), then both schemes are equivalent, regarding the relation between r(t) and y(t). The key differences are due to disturbances and different initial conditions. In the open loop control scheme the controller incorporates feedback internally, i.e. a signal at point A is fed back.
43 In the closed loop scheme, the feedback signal depends on what is actually happening in the plant since the true plant output is used. We will see later that this modified architecture has many advantages including: insensitivity to modelling errors; insensitivity to disturbances in the plant (that are not reflected in the model).
44 For example, if a plant disturbance leads to a nonzero error e(t), in Figure 2.10, then high gain feedback will result in a very large control action u(t). This may lie outside the available input range and thus invalidate the solution. Chapter 2 Tradeoffs involved in choosing the feedback gain The preliminary insights of the previous two sections would seem to imply that all that is needed to generate a controller is to put high gain feedback around the plant. This is true in so far that it goes. However, nothing in life is cost free and this also applies to the use of high gain feedback.
45 Another potential problem with high gain feedback is that it is often accompanied by the very substantial risk of instability. Instability is characterised by self sustaining (or growing) oscillations. As an illustration, the reader will probably have witnessed the high pitch whistling sound that is heard when a loudspeaker is placed too close to a microphone. This is a manifestation of instability resulting from excessive feedback gain. Tragic manifestations of instability include aircraft crashes and the Chernobyl disaster in which a runaway condition occurred.
46 Yet another potential disadvantage of high loop gain was hinted at in the mould level example. There we saw that increasing the controller gain lead to increased sensitivity to measurement noise. (Actually, this turns out to be generically true).
47 In summary, high loop gain is desirable from many perspectives but it is also undesirable when viewed from other perspectives. Thus, when choosing the feedback gain one needs to make a conscious tradeoff between competing issues.
48 The previous discussion can be summarised in the following statement: High loop gain gives approximate inversion which is the essence of control. However, in practice, the choice of feedback gain is part of a complex web of design tradeoffs. Understanding and balancing these tradeoffs is the essence of control system design.
49 Measurements Finally, we discuss the issue of measurements (i.e. what it is we use to generate the feedback signal). A more accurate description of the feedback control loop including sensors is shown in Figure 2.11.
50 Figure 2.11: Closed loop control with sensors r(t) + Controller u(t) Plant A y(t) y m (t) Measurement and signal transmission system
51 Desirable attributes of sensors Reliability. It should operate within the necessary range. Accuracy. For a variable with a constant value, the measurement should settle to the correct value. Responsiveness. If the variable changes, the measurement should be able to follow the changes. Slow responding measurements can, not only affect the quality of control but can actually make the feedback loop unstable. Loop instability may arise even though the loop has been designed to be stable assuming an exact measurement of the process variable.
52 Noise immunity. The measurement system, including the transmission path, should not be significantly affected by exogenous signals such as measurement noise. Linearity. If the measurement system is not linear, then at least the nonlinearity should be known so that it can be compensated. Non intrusive. The measuring device should not significantly affect the behaviour of the plant.
53 Figure 2.12: Typical feedback loop In summary, a typical feedback loop (including sensor issues) is shown below. Referenceof Desired value output Controller Control signal Actuators Disturbances System Actual output Measurements Sensors Measurement noise
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