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I n the name of God Evolutionary Based Nonlinear Multivariable Control System Design Presented by: M. Eftekhari Supervisor : DR. S. D. Katebi Dept. of.

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Presentation on theme: "I n the name of God Evolutionary Based Nonlinear Multivariable Control System Design Presented by: M. Eftekhari Supervisor : DR. S. D. Katebi Dept. of."— Presentation transcript:

1 I n the name of God Evolutionary Based Nonlinear Multivariable Control System Design Presented by: M. Eftekhari Supervisor : DR. S. D. Katebi Dept. of Computer Science and Engineering Shiraz University

2 Contents Introduction Nonlinear systems Multi-objective optimization Robust Control Nonlinear Multivariable systems Implementation Results Conclusions Future works

3 Nonlinear Control Most practical dynamic systems exhibit nonlinear behavior. Most practical dynamic systems exhibit nonlinear behavior. The theory of nonlinear systems is not as well advanced as the linear systems theory. The theory of nonlinear systems is not as well advanced as the linear systems theory. A general and coherent theory dose not exist for nonlinear design and analysis. A general and coherent theory dose not exist for nonlinear design and analysis. Nonlinear systems are dealt with on the case by case bases. Nonlinear systems are dealt with on the case by case bases.

4 Nonlinear Design Most Nonlinear Design techniques are based on: Most Nonlinear Design techniques are based on: Linearization of some form Linearization of some form Quasi–Linearization Quasi–Linearization Linearization around the operating conditions Linearization around the operating conditions

5 Extension of linear techniques Rosenbrock: extended Nyquist techniques to MIMO Systems in the form of Inverse Nyquist Array Rosenbrock: extended Nyquist techniques to MIMO Systems in the form of Inverse Nyquist Array MacFarlane: extended Bode to MIMO in the form of characteristic loci MacFarlane: extended Bode to MIMO in the form of characteristic loci Soltine: extends feedback linearization Soltine: extends feedback linearization Astrom: extends Adaptive Control Astrom: extends Adaptive Control Katebi: extends SIDF to Inverse Nyquist Array Katebi: extends SIDF to Inverse Nyquist Array Others….. Others…..

6 Contents Introduction Nonlinear systems Multi-objective optimization Robust Control Nonlinear Multivariable systems Implementation Results Conclusions Future works

7 Types of Nonlinearities Implicit: friction changes with speed in a nonlinear manner Implicit: friction changes with speed in a nonlinear manner Explicit Explicit Single-valued : eg. dead-zone, hard limit, saturation in op Amp. Single-valued : eg. dead-zone, hard limit, saturation in op Amp. Multi-valued Multi-valued eg. Hysteresis in mechanical systems eg. Hysteresis in mechanical systems

8 Methods for nonlinear systems Design Build Prototype and test (expensive) Build Prototype and test (expensive) Computer simulation (trial and error) Computer simulation (trial and error) Closed form Solutions (only for rare cases) Closed form Solutions (only for rare cases) Lyapunov’s Direct Method (only Stability) Lyapunov’s Direct Method (only Stability) Series–Expansion solution (only implicit) Series–Expansion solution (only implicit) Linearization around the operating conditions (only small changes) Linearization around the operating conditions (only small changes) Quasi–Linearization: (Describing Function) Quasi–Linearization: (Describing Function)

9 Exponential Input Describing Function (EIDF) One particular form of Describing function is EIDF One particular form of Describing function is EIDF Assuming an exponential waveform at the input of a single value nonlinear element and minimizing the integral-squared error Assuming an exponential waveform at the input of a single value nonlinear element and minimizing the integral-squared error Then Then Where applicable, EIDF facilitate the study of the transient response in nonlinear systems

10 EIDF Derivation Single value nonlinear element Error Error ISE ISE

11 Example of EIDF

12 Contents Introduction Nonlinear systems Multi-objective optimization Robust Control Nonlinear Multivariable systems Implementation Results Conclusions Future works

13 Multi-Objective Optimization MOO Optimization deals with the problem of searching feasible solutions over a set of possible choices to optimize certain criteria Optimization deals with the problem of searching feasible solutions over a set of possible choices to optimize certain criteria. MOO implies that there are more than one criterion and they must be treated simultaneously MOO implies that there are more than one criterion and they must be treated simultaneously

14 Formulation of MOO Single objective Single objective Straight forward extension to MOO Straight forward extension to MOO

15 Solution Of MOO Several numerical techniques Several numerical techniques Gradient based Steepest decent Non-gradient based Hill-climbing nonlinear programming numerical search (Tabu, random,..) We focus on Evolutionary techniques GA,GP, EP, ES

16 GA at a glance

17 Wide rang Applications of MOO Design, modeling and planning Design, modeling and planning Urban transportation. Urban transportation. Capital budgeting Capital budgeting Forest management Forest management Reservoir management Reservoir management Layout and landscaping of new cities Layout and landscaping of new cities Energy distribution Energy distribution Etc… Etc…

18 MOO and Control Design Any Control systems design can be formulated as MOO Any Control systems design can be formulated as MOO Ogata, 1950s; optimization of ISE, ISTE (analytic ) Ogata, 1950s; optimization of ISE, ISTE (analytic ) Zakian, 1960s;optimazation of time response parameters (numeric); Zakian, 1960s;optimazation of time response parameters (numeric); Clark, 1970s, LQR, LQG (analytic) Clark, 1970s, LQR, LQG (analytic) Doyle and Grimble, 1980s, (analytic) Doyle and Grimble, 1980s, (analytic) MacFarlane, 1990s, loop shaping (grapho- analytic) MacFarlane, 1990s, loop shaping (grapho- analytic) Whidborn,2000s, suggest GA for solution of all the above Whidborn,2000s, suggest GA for solution of all the above

19 Contents Introduction Nonlinear systems Multi-objective optimization Robust Control Nonlinear Multivariable systems Implementation Results Conclusions Future works

20 Uncertainty Structured (parametric): is caused by undesired parameter changes. Structured (parametric): is caused by undesired parameter changes. Unstructured: is due to un-modeled dynamics Unstructured: is due to un-modeled dynamics Uncertainty can influence and degrade the plant operation in 2 ways Uncertainty can influence and degrade the plant operation in 2 ways

21 Influence of Uncertainty Influence of Uncertainty Multiplicative: G(s)=G 0 (s)(1+w m (s).Δ(s)) Multiplicative: G(s)=G 0 (s)(1+w m (s).Δ(s)) G(s): Perturbed Transfer Function G 0 (s): Nominal Transfer Function Δ(s): disturbance (perturbation) W m (s): weighting, upper bound Additive: G(s)=G 0 (s)+w a (s).Δ(s) Additive: G(s)=G 0 (s)+w a (s).Δ(s) A good design must be robust in the presence of uncertainty and undesired perturbation A good design must be robust in the presence of uncertainty and undesired perturbation of the plant parameters

22 Measure of performance and robustness A SISO block diagram A SISO block diagram Sensitivity is: Sensitivity is: Complementary Sensitivity is: Complementary Sensitivity is: T+S=I T+S=I CG R(t) y(t) n d

23 Robust Performance & robust stability Robust Performance Robust Performance Robust stability Robust stability Objective: Objective: Find W 1 and W2 such that objective is satisfied under constraints. Find W 1 and W2 such that objective is satisfied under constraints.S+T=I W1 and w2 effect a trade-off between S and T W1 and w2 effect a trade-off between S and T

24 =H ∞ Norm Defined as; Defined as; H ∞ Norm: H ∞ Norm:where

25 Contents Introduction Nonlinear systems Multi-objective optimization Robust Control Nonlinear Multivariable systems Implementation Results Conclusions Future works

26 A general MIMO nonlinear System Close loop Transfer function Close loop Transfer function

27 Nonlinear Multivariable systems Block diagram of 2-input 2-output feedback system. Belongs to a special configuration with a class of separable, single value Nonlinear system Block diagram of 2-input 2-output feedback system. Belongs to a special configuration with a class of separable, single value Nonlinear system C11G11 C22G22 C12 G12 C21G21 N11 N22 N12 N21

28 Problems The behavior of multi-loop nonlinear systems is not as well understood as the single-loop systems The behavior of multi-loop nonlinear systems is not as well understood as the single-loop systems Generally, extensions of single-loop techniques can result in methods that are valid for multi-loop systems Generally, extensions of single-loop techniques can result in methods that are valid for multi-loop systems Cross coupling and Loop interaction pose major difficulties in MIMO Cross coupling and Loop interaction pose major difficulties in MIMO Little is known about Stability and Robustness Little is known about Stability and Robustness for the case of MIMO nonlinear systems

29 Absolute Stability Rosenbrock 1965, purposed Multivariable Circle Criterion as a test for Bounded Input Bounded Output Rosenbrock 1965, purposed Multivariable Circle Criterion as a test for Bounded Input Bounded Outputstability. If applied for controller design, it will result in a very conservative controller If applied for controller design, it will result in a very conservative controller Several similar methods for the absolute stability test exist, but non suitable for design. Several similar methods for the absolute stability test exist, but non suitable for design.

30 Contents Introduction Nonlinear systems Multi-objective optimization Robust Control Nonlinear Multivariable systems Implementation Results Conclusions Future works

31 Design procedure Replace: Nonlinear elements  EIDFS The structure of controller is chosen Time domain objectives are formulated Another objective to ensure RP and RS MOGA is applied to solve MOO End Start Rise time, settling time,…

32 Uncertainty exist due to EIDF linearization Assuming linear model is accurate Assuming linear model is accurate Uncertainty exist due to linearizing the nonlinear element Uncertainty exist due to linearizing the nonlinear element Modeling the Additive Uncertainty Modeling the Additive Uncertainty Where K is a vector sampled from EIDF gains. n is an integer. Where K is a vector sampled from EIDF gains. n is an integer.

33 Using H ∞ Weighted Sensitivity Using H ∞ Weighted Sensitivity = uncertain plant = Nominal plant = uncertain plant = Nominal plant

34 Time Domain objectives Find a set of M admissible points Find a set of M admissible points Such that; Such that; is real number, p is a real vector and is real number, p is a real vector and is real function of P (controller parameter) and is real function of P (controller parameter) and t (time) t (time) Any value of p which satisfies the above inequalities characterizes an acceptable design

35 Time domain specifications In a control systems represents functionals In a control systems represents functionals Such as: Such as: Rise time, settling time, overshoot, steady state error, loops interaction (For multivariable systems), ISE, ITSE. Rise time, settling time, overshoot, steady state error, loops interaction (For multivariable systems), ISE, ITSE. For a given time response which is provided by the SIMULINK, these are calculated numerically based on usual formula For a given time response which is provided by the SIMULINK, these are calculated numerically based on usual formula

36 Frequency Domain Objectives may represent any specification in the frequency domain such as bandwidth, GM, PM etc. may represent any specification in the frequency domain such as bandwidth, GM, PM etc. In order to make the design more robust, we used the following In order to make the design more robust, we used the following measure of stability (noise rejection) measure of stability (noise rejection) performance index (disturbance rejection) performance index (disturbance rejection)

37 Mixed Time and Frequency Domain optimization Using ITSE or ISE as performance Indices, With RP and RS Constraints. Using ITSE or ISE as performance Indices, With RP and RS Constraints. Other objectives have also been testd Other objectives have also been testd Eg. Weighted sum

38 Contents Introduction Nonlinear systems Multi-objective optimization Robust Control Nonlinear Multivariable systems Implementation Results Conclusions Future works

39 Example 1 A 2 by 2 Uncompensated System A 2 by 2 Uncompensated System

40 Nonlinear elements are replaced by the EIDF gain and the place of the compensator is decided

41 Design in time domain Structure of the compensator is now decide Structure of the compensator is now decide We started with simplest diagonal and constant controllers We started with simplest diagonal and constant controllers The desired time domain specifications are now given to the MOGA program The desired time domain specifications are now given to the MOGA program MOGA is initialized randomly and the parameter limits are set MOGA is initialized randomly and the parameter limits are set MOGA searches the space of the controller parameters to find at least one set that satisfy all the specified objectives MOGA searches the space of the controller parameters to find at least one set that satisfy all the specified objectives

42 The evolved controller and its performance

43 Name of objectives Desired objectives Resulted objectives Rise time1 1511.0318 Rise time 2 21.1691 Over shoot1 0.50.0397 Over shoot2 0.50.1916 settling11512.1895 settling2156.9533 Steady state1 0.10 Steady state2 0.10 Interaction 1  2 5% 0.89 % Interaction 2  1 5% 0.03 % Design criterion in time domain are met

44 Time responses

45 Conflicting objectives It is observed after 50 generation of MOGA with a population size of 50 It is observed after 50 generation of MOGA with a population size of 50 That although trade-off have been made between the objectives That although trade-off have been made between the objectives But due to conflict, all the required design criterion are not met But due to conflict, all the required design criterion are not met Alternative: we decided to use a more sophisticated controller Alternative: we decided to use a more sophisticated controller

46 Diagonal dynamic compensator

47 Design criterion in time domain are met Name of objectives Desired objectives Resulted objectives Rise time1 21.1668 Rise time 2 20.7428 Over shoot1 0.20.0635 Over shoot2 0.20.0179 settling132.9561 settling232.1597 Steady state1 0.010 Steady state2 0.010 Interaction 1  2 5%0.23% Interaction 2  1 5%0.09%

48 Responses

49 Making the design robust In addition to the time domain specifications In addition to the time domain specifications The criterion for robust stability and robust performance is now added to the objectives The criterion for robust stability and robust performance is now added to the objectives The controllers are modified by MOGA to take care of these additional objectives The controllers are modified by MOGA to take care of these additional objectives In addition to robust stability to the design, the overall performance has also improved. In addition to robust stability to the design, the overall performance has also improved.

50 Responses Name of objectives Resulted objectives Rise time1 1.2855 Rise time 2 0.9770 Over shoot1 0 Over shoot2 0.0008 settling11.4152 settling22.0983 Steady state1 0 Steady state2 0 Interaction 1  2 0.23% Interaction 2  1 0.09%

51 Plant uncertainty taken into account Still a more comprehensive controller is required Still a more comprehensive controller is required

52 Responses from time and frequency domain objectives for uncertain plant Time Domain objectivesMixed Time and Frequency domain objectives

53 Characteristics of responses Name of objectives Desired objectives Resulted objectives Rise time1 21.4751 Rise time 2 20.8598 Over shoot1 0.20 Over shoot2 0.20.0004 settling131.6522 settling231.8500 Steady state1 0.010 Steady state2 0.010 Interaction 1  2 1%0% Interaction 2  1 1%0% Name of objectives Desired objectives Resulted objectives Rise time1 21.3897 Rise time 2 20.8895 Over shoot1 0.20 Over shoot2 0.20.001 settling131.6474 settling232.2815 Steady state1 0.010 Steady state2 0.010 Interaction 1  2 1%0.1% Interaction 2  1 1%0%

54 Analysis and Synthesis EIDF accuracy is investigated EIDF accuracy is investigated Robust stability and robust performances Robust stability and robust performances are examined are examined Convergence of MOGA and aspects of local minima is also look into. Convergence of MOGA and aspects of local minima is also look into.

55 EIDF Accuracy The response of compensated system with The response of compensated system with EIDF in place and the actual nonlinearities are compared When the basic assumption of exponential input is satisfied EIDF is very accurate When the basic assumption of exponential input is satisfied EIDF is very accurate

56 Test for Robust stability The compensated system is subjected to a disturbance The compensated system is subjected to a disturbance It is seen that instability do not occur and system recovers to its optimal operating conditions It is seen that instability do not occur and system recovers to its optimal operating conditions

57 Test for Robust performance The system is subjected to step input of differing magnitude, the time domain specifications do not change The system is subjected to step input of differing magnitude, the time domain specifications do not change

58 MOGA Observations Observations 1. The range of controller parameters should be chosen carefully (domain knowledge is useful) 2. The Parameters of MOGA such as X-over and mutation rates should be initially of nominal vale (Pc=0.7, Pm=0.01) 3. If a premature convergence occurs then these values have to be investigated

59 Contents Introduction Nonlinear systems Multi-objective optimization Robust Control Nonlinear Multivariable systems Implementation Results Conclusions Future works

60 Conclusions A new technique based on MOGA for design of controller for MIMO nonlinear systems were described A new technique based on MOGA for design of controller for MIMO nonlinear systems were described The EIDF linearization facilitate the time response synthesis and the extension of robustness in the frequency domain to the MIMO nonlinear systems The EIDF linearization facilitate the time response synthesis and the extension of robustness in the frequency domain to the MIMO nonlinear systems As the MOGA design progresses the designer obtain more knowledge about the system As the MOGA design progresses the designer obtain more knowledge about the system Based on the domain knowledge the designer is able to effect trade off between the conflicting objectives and also modifies the structure of the controller, if and when necessary. Based on the domain knowledge the designer is able to effect trade off between the conflicting objectives and also modifies the structure of the controller, if and when necessary.

61 Conclusions Time domain approach is more explicit with regards to the system time performance Time domain approach is more explicit with regards to the system time performance By combining the time and the frequency domain objectives design robustness is guaranteed By combining the time and the frequency domain objectives design robustness is guaranteed Taking ISTE or ISE as objectives, Taking ISTE or ISE as objectives, subject to S { "@context": "http://schema.org", "@type": "ImageObject", "contentUrl": "http://images.slideplayer.com/13/4142183/slides/slide_61.jpg", "name": "Conclusions Time domain approach is more explicit with regards to the system time performance Time domain approach is more explicit with regards to the system time performance By combining the time and the frequency domain objectives design robustness is guaranteed By combining the time and the frequency domain objectives design robustness is guaranteed Taking ISTE or ISE as objectives, Taking ISTE or ISE as objectives, subject to S

62 Conclusion The design technique described is compared [Katebi] and contrasted with other methods for MIMO nonlinear systems The design technique described is compared [Katebi] and contrasted with other methods for MIMO nonlinear systems The approach was shown to be effective and has several advantages over other techniques The approach was shown to be effective and has several advantages over other techniques 1. The easy formulation of MOGA 2. Provides degree of freedom for the designer 3. Acceptable computational demand 4. Accurate and multiple solutions 5. Very suitable for the powerful MATLAB environment Several other examples with different linear and nonlinear model have been solved and will be included in the thesis Several other examples with different linear and nonlinear model have been solved and will be included in the thesis

63 Contents Introduction Nonlinear systems Multi-objective optimization Robust Control Nonlinear Multivariable systems Implementation Results Conclusions Future works

64 Future Research Different MIMO nonlinear configuration exist, further works may be undertaken for other configuration Different MIMO nonlinear configuration exist, further works may be undertaken for other configuration The class of nonlinearity considered here only encompass the memoryless (single value) elements. The class of nonlinearity considered here only encompass the memoryless (single value) elements. As the EIDF is not applicable to the multi-valued nonlinearities, theoretical works are required to extend the design to those class on nonlinearities. As the EIDF is not applicable to the multi-valued nonlinearities, theoretical works are required to extend the design to those class on nonlinearities. Several explicit parallel version of MOGA exist, Several explicit parallel version of MOGA exist, For higher dimensional systems parallel algorithms may become necessary. For higher dimensional systems parallel algorithms may become necessary. Application of other evolutionary algorithms such as EP, ES, GP and swarm optimization is another line of further research Application of other evolutionary algorithms such as EP, ES, GP and swarm optimization is another line of further research

65 Question Time


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