Optimal Control Design

Transcription

1 Optimal Control Design Prof. Radhakant Padhi Dept. of Aerospace Engineering Indian Institute of Science - Bangalore Acknowledgement: Indian Institute of Science Founded in 1909 more than 100 years old Founded by J. N. Tata (in consultation with Swami Vivekananda) land was donated by Mysore king. Deemed University in 1958 More than 40 departments Ranked No.1 in India for higher education Only institute in India among best 100 in global ranking For further information, please visit 20 September 2016 Prof. Radhakant Padhi, IISc-Bangalore 2 1

2 Collaboration & Research Funding Defense R&D Organisation (DRDO) Missile Complex (ASL, RCI, DRDL, ANURAG) ARDE CAIR Indian Space Research Organisation (ISRO) VSSC ISAC Air Force Research Lab (AFRL), USA Private Aerospace Companies Coral Digital Technologies Team Indus (Axiom Research Lab) 20 September 2016 Prof. Radhakant Padhi, IISc-Bangalore 3 Research Areas in ICGEL: Guidance and Control of Missiles MPSP and it variants are used to develop optimal guidance algorithm for better performance. Examples: Impact Angle Constrained Guidance of Tactical Missiles Integrated Guidance and Control for Missiles for Ballistic Missile Defence Integrated Control Guidance and Estimation Lab (ICGEL) & Aerospace Systems Lab (ASL) Contact: Prof. Radhakant Padhi E. mail: padhi@aero.iisc.ernet.in Nonlinear, Optimal & Adaptive Control Dynamic Inversion & Neuro-Adaptive Designs Single Network Adaptive Critic (SNAC) Guidance and control of UAVs Guidance and Control for automatic landing. Stereo Vision based reactive collision avoidance using ultra low-cost cameras Nonlinear differential geometric guidance for collision avoidance Nonlinear & Neuro- Adaptive Control of High-Perf. Aircrafts A new robust nonlinear approach is developed for better control of high performance (large L/D) aircrafts, which are unstable in nature. Dept. of Aerospace Engineering Indian Institute of Science, Bangalore Model Predictive Static Programming (MPSP) Online Modified (OM) Design for Enhanced Robustness State Estimation for Feedback Guidance & Control Formation Flying and Attitude Control of Satellites Robust Formation flying of satellites using online modified real-time optimal control Robust large attitude maneuvers of satellites in presence of significant modelling errors Feedback Control for Customized Automatic Drug Delivery Drug is delivered as per patient s condition (not in open loop) - Fast recovery & Reduced side effects Demonstrated for blood cancer, diabetes regulation & Milk-fever of cows Optimal Process Control Current Team (2016) 13 Ph.D. Students, 1 Master Student 2 Project Associates, 2 Project Assistants (many more in the past) 2

3 Acknowledgement: Graduated Students & Other Co-workers Mangal Kothari (Faculty in IIT-Kanpur) Arnab Maity (Faculty in IIT-Bombay) Sk. Faruque Ali (Faculty in IIT-Madras) Gurunath Gurala (Faculty in IISc-Bangalore) Harshal Oza (Faculty in Ahmedabad Univ., Ahmedabad) Prasiddha Nath Dwivedi (Scientist in DRDO, Hyderabad) Prem Kumar (Scientist in DRDO, Hyderabad) Girish Joshi (Former scientist in ISRO, doing his Ph.D. in USA) Kapil Sachan (currently a Ph.D. student) Avijit Banerjee (currently a Ph.D. student) Omkar Halbe (Working in EADS) Charu Chawla (Working in a Pvt. Company) and many more! 20 September 2016 Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 5 Outline Lecture 1 Generic Overview of Optimal Control Theory Lecture 2 Real-time Optimal Control using MPSP Lecture 3 Solution of Challenging Practical Problems using MPSP Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 6 3

4 Lecture 1 An Overview of Optimal Control Design Prof. Radhakant Padhi Dept. of Aerospace Engineering Indian Institute of Science - Bangalore Why Optimal Control? Summary of Benefits A variety of difficult real-life problems can be formulated in the framework of optimal control. State and control bounds can be incorporated in the control design process explicitly. Incorporation of optimal issues lead to a variety of advantages, like minimum cost, maximum efficiency, non-conservative design etc. Trajectory planning issues can be incorporated into the guidance and control design. Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 8 4

5 Role of Optimal Control Question: What is R(s)? How to design it?? Unfortunately, books remain completely silent on this! Optimization (Optimal Control) Optimization (Optimal Control) Mission Objectives Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 9 A Tribute to Pioneers of Optimal Control 1700s Bernoulli, Newton Euler (Student of Bernoulli) Lagrange years later s Pontryagin Bellman Kalman Bernoulli Pontryagin Euler Bellman Lagrange Kalman Newton Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 10 5

6 An Interesting Observation Euler (1726) - Lagrange - Fourier - Dirichlet - Lipschitz - Klein [1A] Euler (1726) - Lagrange - Poisson - Dirichlet - Lipschitz - Klein [1B] Gauss (1799) - Gerling - Pluecker - Klein [2] >> Klein - Lindeman - Hilb - Baer - Liepman - Bryson - Speyer - Bala - Padhi [3] Gauss (1799) - Bessel - Scherk - Kummer - Prym - Rost - Baer - Liepman - Bryson - Speyer - Bala - Padhi [4] Prof. Radhakant Padhi, AE Dept., IISc- Bangalore 11 Optimal control formulation: Key components An optimal control formulation consists of: Performance index that needs to be optimized Appropriate boundary (initial & final) conditions Hard constraints Soft constraints Path constraints System dynamics constraint (nonlinear in general) State constraints Control constraints Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 12 6

7 Optimal Control Problem Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 13 Meaningful Performance Index Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 14 7

8 Meaningful Performance Index Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 15 Optimum of a Functional Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 16 8

9 Fundamental Theorem of Calculus of Variations Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 17 Fundamental Lemma Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 18 9

10 Optimal Control Problem Performance Index (to minimize / maximize): Path Constraint: Boundary Conditions: Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 19 Necessary Conditions of Optimality Augmented PI Hamiltonian First Variation Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 20 10

11 Necessary Conditions of Optimality First Variation Individual terms Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 21 Necessary Conditions of Optimality 0 Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 22 11

12 Necessary Conditions of Optimality First Variation Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 23 Necessary Conditions of Optimality First Variation Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 24 12

13 Necessary Conditions of Optimality: Summary State Equation Costate Equation Optimal Control Equation Boundary Condition Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 25 Necessary Conditions of Optimality: Some Comments State and Costate equations are dynamic equations. If one is stable, the other turns out to be unstable! Optimal control equation is a stationary equation Boundary conditions are split: it leads to Two-Point- Boundary-Value Problem (TPBVP) State equation develops forward whereas Costate equation develops backwards. It is known as Curse of Complexity in optimal control Traditionally, TPBVPs demand computationally-intensive iterative numerical procedures, which lead to open-loop control structure. Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 26 13

14 General Boundary/Transversality Condition General condition: Special Cases: Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 27 Example 1: A Toy Problem Prof. Radhakant Padhi Dept. of Aerospace Engineering Indian Institute of Science - Bangalore 14

15 Example Problem: Solution: Costate Eq. Optimal control Eq. Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 29 Example Boundary Conditions Define Solution Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 30 15

16 Example Use the boundary condition at Use the boundary condition at Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 31 Example Four equations and four unknowns: Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 32 16

17 Example Solution for State and Costate Solution for Optimal Control Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 33 Example 2: Orbit Transfer Problem Prof. Radhakant Padhi Dept. of Aerospace Engineering Indian Institute of Science - Bangalore 17

18 Example (Maximum Radius Orbit Transfer at a Given Time) Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 35 Example (Maximum Radius Orbit Transfer at a Given Time) Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 36 18

19 System Dynamics and B.C. System dynamics Boundary conditions Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 37 Performance index Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 38 19

20 Necessary Condition Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 39 Necessary Condition Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 40 20

21 A Classical Numerical Approach for Solving Optimal Control Problems: Gradient Method Prof. Radhakant Padhi Dept. of Aerospace Engineering Indian Institute of Science - Bangalore Gradient Method Assumptions: State equation satisfied Costate equation satisfied Boundary conditions satisfied Strategy: Satisfy the optimal control equation Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 42 21

22 Gradient Method Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 43 Gradient Method After satisfying the state & costate equations and boundary conditions, we have Select This leads to Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 44 22

23 Gradient Method We select This lead to Note: Eventually, Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 45 Gradient Method: Procedure Assume a control history (not a trivial task) Integrate the state equation forward Integrate the costate equation backward Update the control solution This can either be done at each step while integrating the costate equation backward or after the integration of the costate equation is complete Repeat the procedure until convergence Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 46 23

24 Gradient Method: Selection of Select so that it leads to a certain percentage reduction of Let the percentage be Then This leads to Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 47 Dynamic Programming and Hamilton Jacobi Jacobi Bellman (HJB) Theory Prof. Radhakant Padhi Dept. of Aerospace Engineering Indian Institute of Science - Bangalore 24

25 Fundamental Philosophy Motivation / Objective To obtain a state feedback optimal control solution Fundamental Theorem Any part of an optimal trajectory is an optimal trajectory! Optimal path B C A Non-optimal path Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 49 Optimal Control Problem Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 50 25

26 Hamilton Jacobi Bellman (HJB) Equation Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 51 Hamilton Jacobi Bellman (HJB) Equation contd. Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 52 26

27 Hamilton Jacobi Bellman (HJB) Equation contd. Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 53 Hamilton Jacobi Bellman (HJB) Equation contd. Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 54 27

28 Hamilton Jacobi Bellman (HJB) Equation contd. Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 55 Hamilton Jacobi Bellman (HJB) Equation contd. Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 56 28

29 Hamilton Jacobi Bellman (HJB) Equation contd. Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 57 Summary of HJB Equation Define optimized cost function V as: Then V(t) must satisfy: HJB equation Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 58 29

30 Dynamic Programming: Some Relevant Results Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 59 Dynamic Programming: Some Relevant Results Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 60 30

31 Example: A Benchmark Toy Problem Prof. Radhakant Padhi Dept. of Aerospace Engineering Indian Institute of Science - Bangalore Example Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 62 31

32 Example Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 63 Example Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 64 32

33 Example-2 Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 65 Dynamic Programming: Some Important Facts Dynamic programming is a powerful technique in the sense that if the HJB equation is solved, it leads to a state feedback form of optimal control solution. HJB equation is both necessary and sufficient for the optimal cost function. At least one of the control solutions that results from the solution of the HJB equation is guaranteed to be stabilizing. Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 66 33

34 Dynamic Programming: Some Important Facts The resulting PDE of the HJB equation is extremely difficult to solve in general. Dynamic Programming runs into a huge Computational and storage requirements for reasonably higher dimensional problems. This is a severe restriction of dynamic programming technique, which Bellman termed as curse of dimensionality. Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 67 Books on Optimal Control Design R. Padhi, Applied Optimal Control, Wiley, Manuscript Under Preparation (expected in 2018). D. S. Naidu, Optimal Control Systems, CRC Press, D. E. Kirk, Optimal Control Theory: An Introduction, Prentice Hall, A. E. Bryson and Y-C Ho, Applied Optimal Control, Taylor and Francis, A. P. Sage and C. C. White III, Optimum Systems Control (2nd Ed.), Prentice Hall, Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 68 34

35 Survey Papers on Classical Methods for Optimal Control Design H. J. Pesch (1994), A Practical Guide to the Solution of Real-Life Optimal Control Problems, Control and Cybernetics, Vol.23, No.1/2, 1994, pp R. E. Larson (1967), A Survey of Dynamic Programming Computational Procedures, IEEE Transactions on Automatic Control, December, pp M. Athans (1966), The Status of Optimal Control Theory and Applications for Deterministic Systems, IEEE Trans. on Automatic Control, Vol. AC-11, July 1966, pp Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 69 Thanks for the Attention.!! Prof. Radhakant Padhi, AE Dept., IISc-Bangalore 70 35