Example of Single-Parameter Maximum Seeking

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Presentation transcript:

Extremum Seeking Control for Real-Time Optimization Miroslav Krstic UC San Diego IEEE Advanced Process Control Applications for Industry Workshop Vancouver, 2007

Example of Single-Parameter Maximum Seeking Plant

Example of Single-Parameter Maximum Seeking

Topics - Theory History Single parameter ES, how it works, and stability analysis by averaging Multi-parameter ES ES in discrete time ES with plant dynamics and compensators for performance improvement Internal model principle for tracking parameter changes Slope seeking Limit cycle minimization via ES

Topics - Applications PID tuning Internal combustion (HCCI) engine fuel consumption minimization Compressor instabilities in jet engines Combustion instabilities Formation flight Fusion reflected RF power Thermoacoustic coolers Beam matching in particle accelerators Flow separation control in diffusers Autonomous vehicles without position sensing

History Leblanc (1922) - electric railways Early Russian literature (1940’s) - many papers Drapper and Li (1951) - application to IC engine spark timing tuning Tsien (1954) - a chapter in his book on Engineering Cybernetics Feldbaum (1959) - book Computers in Automatic Control Systems Blackman (1962 chap. in book by Westcott) - nice intuitive presentation of ES Wilde (1964) - a book Chinaev (1969) - a handbook on self-tuning systems Papers by[Morosanov], [Ostrovskii], [Pervozvanskii], [Kazakevich], [Frey, Deem, and Altpeter], [Jacobs and Shering], [Korovin and Utkin] - late 50s - early 70’s Meerkov (1967, 1968) - papers with averaging analysis Sternby (1980) - survey Astrom and Wittenmark (1995 book) - rates ES as one of the most promising areas for adaptive control

Recent Developments Krstic and Wang (2000, Automatica) - stability proof for single-parameter general dynamic nonlinear plants Choi, Ariyur, Wang, Krstic - discrete-time, limit cycle minimization, IMC for parameter tracking, etc. Rotea; Walsh; Ariyur - multi-parameter ES Ariyur - slope seeking Tan, Nesic, Mareels (2005) - semi-global stability of ES Other approaches: Guay, Dochain, Titica, and coworkers; Zak, Ozguner, and coworkers; Banavar, Chichka, Speyer; Popovic, Teel; etc. Applications not presented in this workshop: Electromechanical valve actuator (Peterson and Stephanopoulou) Artificial heart (Antaki and Paden) Exercise machine (Zhang and Dawson) Shape optimization for magnetic head in hard disk drives (UCSD) Shape optimization of airfoils and automotive vehicles (King, UT Berlin)

ES Book

Tutorial Topics Covered in the Book Introduction, history, single-parameter stability analysis Plant dynamics, compensators, and IMC for tracking parameter changes Limit cycle minimization via ES Multi-parameter ES ES in discrete time Slope seeking Compressor instabilities in jet engines Combustion instabilities Formation flight Anti-skid braking Bioreactor Thermoacoustic coolers Internal combustion engines Flow separation control in diffusers Beam matching in particle accelerators PID tuning Autonomous vehicles without position sensing

Basic Extremum Seeking - Static Map Plant

How Does It Work? Plant Estimation error: Loc. Analysis - neglect quadratic terms:

How Does It Work? Plant

How Does It Work? Plant Demodulation:

How Does It Work? Plant Since then

How Does It Work? Plant high frequency terms - attenuated by integrator

How Does It Work? Plant Stable because

Stability Proof by Averaging Plant Full nonlinear time-varying model:

Stability Proof by Averaging Plant Average system: Average equilibrium:

Stability Proof by Averaging Plant Jacobian of the average system:

Stability Proof by Averaging Plant Theorem. For sufficiently large w there exists a unique exponentially stable periodic solution of period 2p/w and it satisfies Speed of convergence proportional to 1/w, a2, k,

Stability Proof by Averaging Plant Output performance:

Based on contributions by: Nick Killingsworth PID Tuning Using ES Based on contributions by: Nick Killingsworth

Background & Motivation Proportional-Integral-Derivative (PID) Control Consists of the sum of three control terms - Proportional term: - Integral term: - Derivative term: Often poorly tuned (Astrom [1995], etc.) e(t) = r(t) – y(t) r(t) reference signal y(t) measured output

Background – PID + - We use a two degree of freedom controller The derivative term only acts on y(t) This avoids large control effort when there is a step change in the reference signal + -

Extremum Seeking Algorithm Tuning Scheme Continuous Time Step function r(t) u(t) y(t) J(qk) G - qk Extremum Seeking Algorithm Discrete Time

Extremum Seeking Simple - three lines of code

Extremum Seeking Tuning Scheme Implementation Run Step response experiment with ZN PID parameters Calculate J

Extremum Seeking Tuning Scheme Implementation Run Step response experiment with ZN PID parameters Calculate J Calculate next set of PID parameters using discrete ES tuning method

Extremum Seeking Tuning Scheme Implementation Run Step response experiment with ZN PID parameters Calculate J Calculate next set of PID parameters using discrete ES tuning method Run another step response experiment with new PID parameters

Extremum Seeking Tuning Scheme Implementation Run Step response experiment with ZN PID parameters Calculate J Calculate next set of PID parameters using discrete ES tuning method Run another step response experiment with new PID parameters Repeat 2-4 set number of times or until J falls below a set value Repeat

Implementation – Cost Function Cost Function J(qk) Used to quantify the controller’s performance Constructed from the output error of the plant and the control effort during a step response experiment Has discrete values at the completion of each step response experiment where T is the total sample time of each step response experiment q is a vector containing the PID parameters:

Implementation – Cost Function Cost Function J(qk) t0 is the time up until which zero weightings are placed on the error. This shifts the emphasis of the PID controller from the transient phase of the response to that of minimizing the tracking error after the initial transient portion of the response to

Example Plants Four systems with dynamics typical of some industrial plants have been used to test the ES PID tuning method Time delay Large time delay Single pole of order eight Unstable zero

Results Ziegler-Nichols values used as initial conditions in the ES tuning algorithm Results compared to three other popular PID tuning methods: - Ziegler-Nichols (ZN) - Internal model control (IMC) - Iterative feedback tuning (IFT, Gevers, ‘94, ‘98)

Results - a) Evolution of Cost Function b) Evolution of PID Parameters c) Step Response of output d) Step Response of controller

Results - a) Evolution of Cost Function b) Evolution of PID Parameters c) Step Response of output d) Step Response of controller

Results - a) Evolution of Cost Function b) Evolution of PID Parameters c) Step Response of output d) Step Response of controller

Results - a) Evolution of Cost Function b) Evolution of PID Parameters c) Step Response of output d) Step Response of controller

Results – Cost Function Comparison Step Response of output The following cost functions were minimized using ES:

Results – Cost Function Comparison Step Response of output The following cost functions were minimized using ES:

Results – Cost Function Comparison Step Response of output The following cost functions were minimized using ES:

Results – Cost Function Comparison Step Response of output The following cost functions were minimized using ES:

Results – Cost Function Comparison Step Response of output The following cost functions were minimized using ES:

Actuator Saturation Saturation of 1.6 applied to control signal for plant G1 ES and IMC compared with and without the addition of an anti windup scheme Tracking anti-windup scheme

Actuator Saturation Step response of output Control signal during step response

Effects of Noise Band-limited white noise has been added to output Power spectral density = 0.0025 Correlation time = 0.2 Independent noise signal for each iteration Simulations on plant G1

Effects of Noise a) Evolution of Cost Function b) Evolution of PID Parameters c) Step Response of output d) Step Response of controller

Selecting Parameters of ES Scheme Must select a, perturbation step size g, adaptation gain w, perturbation frequency h, high-pass filter cut-off frequency Looks like have more parameters to pick than we started out with! However, ES tuning is less sensitive to parameters than PID controller.

Selecting Parameters of ES Scheme ES Tuning Parameters K Ti Td 1.01 31.5 7.16 1.00 31.1 7.6 31.3 7.54 31.0 7.65

Selecting Parameters of ES Scheme Need to select an adaptation gain g and perturbation amplitude a for EACH parameter to be estimated In the case of a PID controller, q = [K, Ti, Td], so we need three of each. The modulation frequency is determined by: where 0 < a < 1 The highpass filter (z-1)/(z+h) is designed with 0<h<1 with the cutoff frequency well below the modulation frequency . Convergence rate is directly affected by choice of a and g, as well as by cost function shape near minimizer.

Example of ES-PID tuner GUI

Punch Line ES yields performance as good as the best of the other popular tuning methods Can handle some nonlinearities and noise. The cost function can be modified such that different performance attributes are emphasized

Control of HCCI Engines Based on contributions by: Nick Killingsworth (UCSD), Dan Flowers and Sal Aceves (Livermore Lab), and Mrdjan Jankovic (Ford)

HCCI = ? HCCI = Homogeneous Charge Compression Ignition Low NOx emissions like spark-ignition engines High efficiency like Diesel engines More promising in near term than fuel cell/hydrogen engines

HCCI Engine Applications Distributed power generation Automotive hybrid powertrain

What is the difference between Spark Ignition, Diesel, and HCCI engines?

Direct injection engine Categories of Engines Compression Ignition Spark ignition Homogeneous charge HCCI Spark ignition engine Inhomogeneous charge Diesel Direct injection engine

Spark Ignition Engine Basic engine thermodynamics: engine efficiency increases as the compression ratio and =cp/cv (ratio of specific heats) increase g = 1.4 for air g = 1.35 for fuel and air mixture Engine Efficiency SI engines 1 3 5 7 9 11 13 15 17 19 compression ratio 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 engine indicated efficiency g=1.3 g=1.4

Diesel Engine Highly efficient because they compress only air ( is high) and are not restricted by knock (compression ratio is high) g = 1.4 for air g = 1.35 for fuel and air mixture Engine Efficiency 1 3 5 7 9 11 13 15 17 19 compression ratio 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 engine indicated efficiency g=1.3 g=1.4 Diesel engines SI engines

Diesel and HCCI engines Compression ratio not restricted by “knock” (autoignition of gas ahead of flame in spark ignition engines) a efficiency comparable to Diesel Diesel and HCCI engines g = 1.4 for air g = 1.35 for fuel and air mixture Engine Efficiency 1 3 5 7 9 11 13 15 17 19 compression ratio 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 engine indicated efficiency g=1.3 g=1.4 SI engines

HCCI Engine Potential for high efficiency (Diesel-like) Low NOx and PM (unlike Diesel) BUT, no direct trigger for ignition - requires feedback to control the timing of ignition!

Experiment at Livermore Lab Caterpillar 3406 natural gas spark ignited engine converted to HCCI Set up for stationary power generation (not automotive)

Actuators Combustion timing (output) is very sensitive to intake temperature (input) Heated Intake Air Cooled Mixing “Tees”(x 6) Controlled Intake Temperature to Individual Cylinders Cold Manifold Hot Manifold Valve Actuators

Overall Architecture: Sensors and Software User interface Valve Position Real-Time Controller PC running Labview RT OS Cylinder Pressure Crank Angle Position

ES used to MINIMIZE FUEL CONSUMPTION of HCCI engine by tuning combustion timing setpoint CA50 e CA50 + Extremum Seeking m fuel . T intake HCCI Engine S PI - CA50 SP

ES delays the combustion timing 6 crank angle degrees, reducing fuel consumption by > 10% The PI controller found using ES is applied and tested during a load disturbance. The load starts at 21 kW and is increased via a step change to 25 kW around 30 seconds. The dip in the rpm is a result of the extra load being applied to the engine. PI: 1600 rpm, load = 21-25-21kW, theta = [2.53,0.005,0] Kf = 0

Larger adaptive gain: ES finds same minimizer, but much more quickly The PI controller found using ES is applied and tested during a load disturbance. The load starts at 21 kW and is increased via a step change to 25 kW around 30 seconds. The dip in the rpm is a result of the extra load being applied to the engine. PI: 1600 rpm, load = 21-25-21kW, theta = [2.53,0.005,0] Kf = 0

Axial Flow (Jet Engine-Like) Compressor Control Problem Statement time Pressure Rise Experimental Results Active controls for rotating stall only reduce the stall oscillations but they do not bring them to zero nor do they maximize pressure rise. Extremum seeking to optimize compressor operating point. Extremum seeking stabilizes the maximum pressure rise. bleed valve Pressure rise Caltech COMPRESSOR Smaller, lighter compressors; higher payload in aircraft Motivation Active controls for compressor rotating stall ensure that the descent into rotating stall is not abrupt, but they do not eliminate it completely, nor do they ensure that the pressure rise is maximal possible. Extremum seeking tunes the compressor operating point so that it be kept at the stall inception point where stall oscillations are absent and the pressure rise is the maximal physically possible. This tuning ensures that the operating point tracks variations in the environment, pilot commands, and aging. The maximization of pressure allows the reduction of compressor size, and an increase in the thrust-to-weight ratio in aircraft. The time trace shows an extremum seeking transient. The compressor starts from a regime where the stall controller is already active but it is not optimal, so rotating stall, although moderate, is present. The extremum seeker forces the system completely out of rotating stall but it also keeps it from sliding down the stable side of the compressor characteristic into low values of pressure rise. It keeps stall at zero and the pressure at its maximum. The oscillations present after the transient are just measurement noise. The experiment shown was performed on an axial-flow compressor at Caltech, in the laboratory of Prof. Richard Murray. The complete block diagram of the system shows that a stall controller implemented through air injector is in the inner regulation loop, while the outer, optimization loop, is the extremum seeker. The method uses sinusoidal probing to estimate the gradient of the compressor operating map, and drives the compressor to an operating point where the gradient is zero. Extremum seeking is implemented via a bleed valve (low bandwidth, cheap). Professor Krstic has patented this compressor optimization scheme through University of California. EXTREMUM SEEKER Air Injection Stall Controller washout filter sin wt

Combustion Instability Control Problem Statement time ext. seeking suppresses oscillations Experiment on UTRC 4MW combustor Rayleigh criterion-based controllers, which use phase-shifted pressure measurements and fuel modulation, have emerged as prevalent The length of the phase needed varies with operating conditions. The tuning method must be non-model based. Tuning allows operation with minimum oscillations at lean conditions Reduced engine size, fuel consumption and NOx emissions Motivation sin wt Pressure washout filter COMBUSTOR Phase-Shifting Controller Frequency/ amplitude observer fuel Thermoacoustic oscillations are common in combustion chambers operating at high power or in a lean (low emission) regime. Active control that uses fuel modulation as actuation and a phase-shifted measurement of pressure has emerged as the most practical way of mitigating those oscillations. This approach is directly related to the classical Rayleigh’s instability criterion. The length of the phase needed varies with operating conditions and cannot be determined a priori using a model. The extremum seeking was used in this program to tune the phase of the phase-shifting controller designed by Dr. Andrzej Banaszuk. He performed the experiments on an industrial size combustor at UTRC. The time trace shows how the bulk mode of the pressure oscillations gets dramatically supressed after turning on the extremum seeker. The complete block diagram of the system shows that the phase shifting controller performs the task of local regulation and the extremum seeker performs its optimization in an outer loop. The extremum seeking scheme estimates the gradient of the map of oscillation amplitudes and drives the phase shift to a value where the gradient of the map is zero, i.e., the optimal phase shift. Extremum seeking minimizes oscillations under varying operating conditions, pilot commands, aging. It allow the reduction in engine size (increased thrust/weight ratio), in fuel consumption, and in NOx emissions. phase EXTREMUM SEEKER

Formation Flight Engine Output Minimization Classical linear decoupled aircraft model with conventional controls—deviation from trim conditions of aircraft states are small Order of the model is 16, states are the conventional rigid body parameters, plus the actuators The reference flight condition used for linearization is cruise at M=0.77, 40000 ft and a weight of 650 klb Aircraft dynamics in free flight is the basis for modeling aircraft dynamics in the wake Experimental wake data of the C-5 used for generating simulation model All forces and moments are assumed to depend only upon relative positions through the feedback mechanism shown in this diagram Specifically, upwash-induced longitudinal forces and pitching moment are modeled by averaging the upwash along the wingspan Upwash-induced rolling moment is calculated with modified strip theory and Sidewash-induced sideforce, yawing and rolling moment are modeled by assuming a uniform sidewash equal to that at the centerline Aerodynamic interference results from flying in formation and is modeled as three nonlinear static maps … As shown in this block diagram, to which I will go back in a few moments Tune reference inputs yref and zref to the autopilot of the wingman to maximize its downward pitch angle or to minimize its engine output

Simulation of C-5 Galaxy transport airplane for a brief encounter of “clear air turbulence” Dryden Vertical and Lateral Turbulence ES is switched to autopilot after 3 seconds of CAT, enough to detect it by nz increase

Thermoacoustic Cooler (M. Rotea) Electric energy Acoustic energy Heat pumping Standing sound wave creates the refrigeration cycle Resonance tube Stack Hot-end heat exchangers Electro-dynamic driver Cold-end heat exchangers Pressurized He-Ar mixture 3 2 Heat Pumping 1-2: adiabatic compression and displacement 2-3: isobaric heat transfer (gas to solid) 3-4: adiabatic expansion and displacement 4-1: isobaric heat transfer (solid to gas) Solid surface (stack plate) Gas particle in a standing wave 1 4 QL QH

Thermoacoustic Cooler Moving piston (varying resonator’s stiffness) Heat exchangers Helmholtz resonator Neck (mass) Volume (stiffness) Electro-dynamic Driver Tuning Variables Piston position (acoustic impedance) Driver frequency

ES with PD compensator + POS Command + + + FREQ Command Cooling Power LPF Integrator PD + + PD Integrator LPF + FREQ Command Cooling Power Calculation Tunable Cooler HPF

Experiment – Fixed Operating Condition Cooling Performance with ESC POS in. FREQ Hz POWER W 4 141 22.65   142 29.92 143 35.67 144 28.63 145 21.25 5 15.89 34.12 39.68 146 35.12 148 19.34 6 140 4.95 9.00 18.55 23.86 35.99 147 41.28 38.00 149 30.36 150 19.36 7 16.34 33.34 41.21 40.70 151 34.69 153 19.63 8 32.16 152 35.60 31.74 50 100 150 200 250 300 20 40 60 Cooling Power (Watt) 2 4 6 8 Piston Position (in) 140 145 Driving Frequency (Hz) Time (sec) ESC ON The first test was done for Esc with fixed conditions, which means all other operating parameters are fixed, e.g.,mean pressure, flow rates, inlet temperatures of 2nd loop, gas mixture are not changed throughout the process. Before we did the test, the static map was measured. Here x is the piston position measured from the motor side, f is the driving freq, and Qc_dot is the cooling power. The static map shows that the optimal cooling power is about 41 Watt, located around 6~7 inch and 147~149 Hz ESC quickly finds optimum operating point (41.3W, 147Hz, 6.2in)

Experiment – Varying Operating Condition 50 100 150 200 250 300 350 400 Cooling Power (Watt) 4 6 8 10 12 Piston Position (in) 140 145 155 Driving Frequency (Hz) 20 40 60 80 Time (sec) Flow Rate (ml/s) Cold Side Hot Side ESC ON Flow Rate Change ESC tracks optimum after cold-side flow rate is increased

ES for the Plasma Control in the Frascati Fusion Reactor Contribution by Luca Zaccarian (U. Rome, Tor Vergata)

Optimization Objective Framework: Additional Radio Frequency heating injected in the plasma by way of Lower Hybrid (LH) antennas: plasma reflects some power Goal: Optimize coupling between the Lower Hybrid antenna and tha plasma, during the LH pulse

Reflected Power Map Reflected power: Convex fcn of edge density Convex fcn of edge position Possible approaches to optimize: 1. Move the antenna (too slow!) 2. Move the plasma (viable – adopted here)

Probing not Allowed - Modified ES Scheme Knob Extracted Input sinusoid Extracted Output sinusoid

Experimental results with medium gain K = 300 Safety saturation limits performance Control action is quite aggressive.

Experimental results with lower gain K = 200 (Antenna has been moved) Graceful convergence to the minimum reflected power

Gain too high - instability K = 350 Instability Gain is too large

Experiments - Summary Input/output plane representation: K = 300: saturation prevents reaching the minimum K = 200: graceful convergence to minimum (slight overshoot) K = 350: gain too high – all the curve is explored