# CDS 101/110a: Lecture 10-1 Robust Performance

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1 CDS 11/11a: Lecture 1-1 Robust Performance Richard M. Murray 1 December 28 Goals: Describe how to represent uncertainty in process dynamics Describe how to analyze a system in the presence of uncertainty Review the main principles and tools for the course Reading: Åström and Murray, Feedback Systems, Ch 12 Advanced: Lewis, Chapter 9 CDS 21: DFT, Chapters 4-6

2 Control = Sensing + Computation + Actuation Actuate Gas Pedal Compute Control Action Goals: Stability, Performance, Robustness Nyquist Diagram Sense Vehicle Speed Stability: bounded inputs produce bounded outputs Need to check all input/output pairs (Gang of Four/Six) Necessary and sufficient condition: check for nonzero solutions around feedback loop Performance: desirable system response Step response: rise time, settling time, overshoot, etc Frequency response: tracking signals over given range Robustness: stability/ performance in presence of uncertainties Need to check stability for set of disturbances & system models CDS 11/11, 3 Nov 8 Richard M. Murray, Caltech CDS 2

3 Modeling Uncertainty Noise and disturbances Model the amount of noise by its signal strength in different frequency bands Can model signal strength by peak amplitude, average energy, and other norms Typical example: Dryden gust models (filtered white noise) Parametric uncertainty Unknown parameters or parameters that vary from plant to plant Typically specified as tolerances on the basic parameters that describe system Unmodeled dynamics High frequency dynamics (modes, etc) can be excited by control loops Use bounded operators to account for effects of unmodeled modes: Pµ(s) Δ CDS 11/11, 3 Nov 8 Richard M. Murray, Caltech CDS 3

4 Unmodeled Dynamics Model unknown dynamics through bounded transfer function Simplest case: additive uncertainty P (s) =P (s)+ (s), (s) <W 2 (s) Δ(s) is unknown, but bounded in magnitude Magnitude bound can depend on frequency; typically have a good model at low frequency Different types of uncertainty can be used depending on where uncertainty enters Multiplicative: good for unknown gain or actuator dynamics Feedback: good for leakage effects (eg, in electrical circuits) Gain Phase [deg] !1 1!2!9! Frequency [rad/s] Additive: P + Multiplicative: P (1 + δ) Feedback: P/(1 + fb P ) CDS 11/11, 3 Nov 8 Richard M. Murray, Caltech CDS 4

5 Stability in the Presence of Uncertainty Characterize stability in terms of stability margin sm Stability margin = distance on Nyquist plot to -1 point Stability margin = 1/Ms (Ms = maximum sensitivity) M s = max S(iω) = max 1 1+L, s m = min ( 1) L = min 1+L =1/M s For robustness analysis, stability margin is more useful than classical gain and phase margins Robust stability: verify no new net encirclements occur Assume that nominal system is stable New loop transfer function: L =(P + )C = L + C No net encirclements as long as C < 1+L Can rewrite as bound on allowable perturbation δ = < 1+PC = P or < 1 C T P T If condition is satisfied, then sm will never cross to zero => no new net encirclements CDS 11/11, 3 Nov 8 Richard M. Murray, Caltech CDS 5

6 Example: Cruise Control Question: how accurate does our model have to be Start with very simple model (25 m/s, 4th gear) 1.38 P (s) = C(s) = s s Ignores details of engine dynamics, sensor delays, etc Model unknown dynamics as additive or multiplicative uncertainty (can convert bounds from one to the other) System will remain stable as long as < 1+PC = P C T δ = P < 1 T CDS 11/11, 3 Nov 8 Richard M. Murray, Caltech CDS Remarks Conditions show why we can use simple models for designs Models must be accurate near critical point (eg, crossover) 6

7 Robust Performance Using Sensitivity Functions Performance conditions What happens to performance in the presence of uncertainty? Start by looking at the sensitivity of transfer functions to process model G yd = P 1+PC = PS dg yd dp = 1 (1 + PC) 2 = SP P (1 + PC) = S G yd P Δ Other measures dg yr G yr = S dp P. G yd + yd P + 1+(P + )C P 1+PC + 1+PC = G yd + S dg yd G yd = S dp P. dg un G un = T dp P. Sensitivity functions actually come from this analysis CDS 11/11, 3 Nov 8 Richard M. Murray, Caltech CDS 7

8 Example: Operational Amplifier Basic amplifier with unmodeled (high frequency) dynamics Start with low frequency model: G(s) G(s)+R 2 /R G(s) = b s + a, α = R 2 R 1 G v2 v 1 = R 2 R 1 Typical parameters b a 1 Robust stability: see how much uncertainty we can handle δ = < 1 αb T = P T s + a + αb Robust performance: effect on tracking, disturbance rej dg yr G yr = S dp P dg yd G yd = T dp P S = Note: disturbance enters in different location s + a s + a + αb Magnitude (db) S 1 5 Frequency (rad/sec) 1/T T CDS 11/11, 3 Nov 8 Richard M. Murray, Caltech CDS 8

9 Alternative Formulation for Robust Performance Δ W 2 d z disturbance signal output signal d P z Δ W 1 W 2 uncertainty block performance weight K W 2 uncertainty weight Goal: guaranteed performance in presence of uncertainty u 2 = u(t) 2 dt z 2 γ d 2 for all 1 H = max ω H(iω) Compare energy in disturbances to energy in outputs Use frequency weights to change performance/uncertainty descriptions Can I get X level of performance even with Y level of uncertainty? CDS 11/11, 3 Nov 8 Richard M. Murray, Caltech CDS 9

10 Design for Robust Performance (ala DFT) Basic idea: interpret conditions on Nyquist Performance: keep sensitivity function small W 1 S < 1 = W 1 < 1+L W1 serves as performance weighting function Stability: avoid additional encirclements W 2 δ < 1 = W 2 L < 1+L, δ < 1 T W2 serves as uncertainty weighting function Individual conditions provide robust stability and (nominal) performance Design loop shape to satisfy robust stability and performance conditions Nominal performance: Theorem: robust performance if circles W don t intersect on Nyquest plot: 1 < 1+L for all ω Robust stability: W 1 + W 2 L < 1+L for all ω W 2 L < 1+L for all Holds for multiplicative uncertainty + Missing: robust performance... weighted sensitivity (cf DFT) CDS 11/11, 3 Nov 8 Richard M. Murray, Caltech CDS 1

11 Tools for Analyzing and Synthesizing Controllers Robust Multi-Variable Control Theory Generalizes gain/phase margin to MIMO systems Uses operator theory to handle uncertainty, performance Uses state space theory to performance computations (LMIs) Analysis Tools H gains for multi-input, multi-output systems µ analysis software - Allow structured uncertainty descriptions (fairly general) - Computes upper and lower bounds on performance - Wide usage in aerospace industry SOSTOOLS: Nonlinear extensions Synthesis Tools LQR/LQG + H loop shaping ; modern tools for control engineers µ synthesis software; tends to generate high order controllers Model reduction software for reducing order of plants, controllers Δ P K CDS 11/11, 3 Nov 8 Richard M. Murray, Caltech CDS 11

12 Example: Robust Cruise Control 4 Bode Diagram 2 Magnitude (db) Frequency (rad/sec) Phase (deg) Magnitude (db) P P Bode Diagram L L C C Magnitude (db) Theorem Performance: Robust Stability: Bode Diagram Frequency (rad/sec) Frequency (rad/sec) CDS 11/11, 3 Nov 8 Richard M. Murray, Caltech CDS 12

13 Course Summary: Two Main Principles of Control Design of Dynamics through Feedback Feedback allows the dynamics of a system to be modified Key idea: interconnection gives closed loop that modifies natural behavior Tools: eigenvalue assignment, loop shaping Robustness to Uncertainty through Feedback Feedback allows high performance in the presence of uncertainty Key idea: accurate sensing to compare actual to desired, correction through computation and actuation Tools: stability margins, sensitivity functions Δ CDS 11/11, 3 Nov 8 Richard M. Murray, Caltech CDS 13

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