LQR and H 2 control problems

Similar documents
Spectral factorization and H 2 -model following

Disturbance Decoupling and Unknown-Input Observation Problems

Stabilization and Self bounded Subspaces

Discussion on: Measurable signal decoupling with dynamic feedforward compensation and unknown-input observation for systems with direct feedthrough

Education in Linear System Theory with the Geometric Approach Tools

The geometric approach and Kalman regulator

Zeros and zero dynamics

Linear Control Theory. in Geometric Terms

Contents. 1 State-Space Linear Systems 5. 2 Linearization Causality, Time Invariance, and Linearity 31

Geometric Control of Patterned Linear Systems

The generalised continuous algebraic Riccati equation and impulse-free continuous-time LQ optimal control

Federico Barbagli' Giovanni Marro2 Domenico Prattichizz~~

c 2003 Society for Industrial and Applied Mathematics

Topic # Feedback Control Systems

Almost disturbance decoupling: static state feedback solutions with maximal pole assignment

Linear Quadratic Zero-Sum Two-Person Differential Games Pierre Bernhard June 15, 2013

The Kalman-Yakubovich-Popov Lemma for Differential-Algebraic Equations with Applications

5. Observer-based Controller Design

6. Linear Quadratic Regulator Control

EL2520 Control Theory and Practice

6.241 Dynamic Systems and Control

Semidefinite Programming Duality and Linear Time-invariant Systems

only nite eigenvalues. This is an extension of earlier results from [2]. Then we concentrate on the Riccati equation appearing in H 2 and linear quadr

Stability, Pole Placement, Observers and Stabilization

José C. Geromel. Australian National University Canberra, December 7-8, 2017

Module 08 Observability and State Estimator Design of Dynamical LTI Systems

Linear Quadratic Zero-Sum Two-Person Differential Games

2 The Linear Quadratic Regulator (LQR)

Module 03 Linear Systems Theory: Necessary Background

ME 234, Lyapunov and Riccati Problems. 1. This problem is to recall some facts and formulae you already know. e Aτ BB e A τ dτ

Linear Algebra. P R E R E Q U I S I T E S A S S E S S M E N T Ahmad F. Taha August 24, 2015

Stationary trajectories, singular Hamiltonian systems and ill-posed Interconnection

Multivariable Control. Lecture 03. Description of Linear Time Invariant Systems. John T. Wen. September 7, 2006

USE OF SEMIDEFINITE PROGRAMMING FOR SOLVING THE LQR PROBLEM SUBJECT TO RECTANGULAR DESCRIPTOR SYSTEMS

Denis ARZELIER arzelier

Control Design of a Distributed Parameter. Fixed-Bed Reactor

Balancing of Lossless and Passive Systems

Descriptor system techniques in solving H 2 -optimal fault detection problems

Motivations and Historical Perspective

Observability. It was the property in Lyapunov stability which allowed us to resolve that

Steady State Kalman Filter

BALANCING-RELATED MODEL REDUCTION FOR DATA-SPARSE SYSTEMS

A QMI APPROACH TO THE ROBUST FAULT DETECTION AND ISOLATION PROBLEM. Emmanuel Mazars, Zhenhai Li, Imad M Jaimoukha. Imperial College London

LQR, Kalman Filter, and LQG. Postgraduate Course, M.Sc. Electrical Engineering Department College of Engineering University of Salahaddin

Decentralized control with input saturation

1 Continuous-time Systems

Control Systems. Laplace domain analysis

LINEAR QUADRATIC OPTIMAL CONTROL BASED ON DYNAMIC COMPENSATION. Received October 2010; revised March 2011

The disturbance decoupling problem (DDP)

Krylov Subspace Methods for Nonlinear Model Reduction

System Identification by Nuclear Norm Minimization

DESIGN OF OBSERVERS FOR SYSTEMS WITH SLOW AND FAST MODES

6 OUTPUT FEEDBACK DESIGN

Modeling and Analysis of Dynamic Systems

State Regulator. Advanced Control. design of controllers using pole placement and LQ design rules

Simultaneous global external and internal stabilization of linear time-invariant discrete-time systems subject to actuator saturation

Time-Invariant Linear Quadratic Regulators!

ẋ n = f n (x 1,...,x n,u 1,...,u m ) (5) y 1 = g 1 (x 1,...,x n,u 1,...,u m ) (6) y p = g p (x 1,...,x n,u 1,...,u m ) (7)

ECEN 605 LINEAR SYSTEMS. Lecture 8 Invariant Subspaces 1/26

Here represents the impulse (or delta) function. is an diagonal matrix of intensities, and is an diagonal matrix of intensities.

Time-Invariant Linear Quadratic Regulators Robert Stengel Optimal Control and Estimation MAE 546 Princeton University, 2015

Model reduction via tangential interpolation

Stochastic and Adaptive Optimal Control

Nonlinear Observers. Jaime A. Moreno. Eléctrica y Computación Instituto de Ingeniería Universidad Nacional Autónoma de México

MTNS 06, Kyoto (July, 2006) Shinji Hara The University of Tokyo, Japan

On the Stabilization of Neutrally Stable Linear Discrete Time Systems

Chapter 3. LQ, LQG and Control System Design. Dutch Institute of Systems and Control

Infinite horizon control and minimax observer design for linear DAEs

1 Introduction QUADRATIC CHARACTERIZATION AND USE OF OUTPUT STABILIZABLE SUBSPACES 1

OPTIMAL CONTROL AND ESTIMATION

Lecture 9. Introduction to Kalman Filtering. Linear Quadratic Gaussian Control (LQG) G. Hovland 2004

Prashant Mhaskar, Nael H. El-Farra & Panagiotis D. Christofides. Department of Chemical Engineering University of California, Los Angeles

POLE PLACEMENT. Sadegh Bolouki. Lecture slides for ECE 515. University of Illinois, Urbana-Champaign. Fall S. Bolouki (UIUC) 1 / 19

Control Systems. Dynamic response in the time domain. L. Lanari

Static Output Feedback Stabilisation with H Performance for a Class of Plants

A New Algorithm for Solving Cross Coupled Algebraic Riccati Equations of Singularly Perturbed Nash Games

Disturbance Attenuation for a Class of Nonlinear Systems by Output Feedback

The Behavioral Approach to Systems Theory

16.31 Fall 2005 Lecture Presentation Mon 31-Oct-05 ver 1.1

Synthesis via State Space Methods

Criterions on periodic feedback stabilization for some evolution equations

Linear Quadratic Gausssian Control Design with Loop Transfer Recovery

Discrete and continuous dynamic systems

On feedback stabilizability of time-delay systems in Banach spaces

Module 02 CPS Background: Linear Systems Preliminaries

Return Difference Function and Closed-Loop Roots Single-Input/Single-Output Control Systems

Gramians based model reduction for hybrid switched systems

MATH4406 (Control Theory) Unit 6: The Linear Quadratic Regulator (LQR) and Model Predictive Control (MPC) Prepared by Yoni Nazarathy, Artem

SOLVING ALGEBRAIC RICCATI EQUATION FOR SINGULAR SYSTEM BASED ON MATRIX SIGN FUNCTION

OPTIMAL CONTROL. Sadegh Bolouki. Lecture slides for ECE 515. University of Illinois, Urbana-Champaign. Fall S. Bolouki (UIUC) 1 / 28

Dichotomy, the Closed Range Theorem and Optimal Control

Homogeneous Linear Systems of Differential Equations with Constant Coefficients

Control Systems. Time response. L. Lanari

Suppose that we have a specific single stage dynamic system governed by the following equation:

Quadratic Stability of Dynamical Systems. Raktim Bhattacharya Aerospace Engineering, Texas A&M University

Model reduction for linear systems by balancing

Learn2Control Laboratory

Modern Control Systems

X 2 3. Derive state transition matrix and its properties [10M] 4. (a) Derive a state space representation of the following system [5M] 1

arxiv: v1 [math.oc] 2 Sep 2016

Transcription:

LQR and H 2 control problems Domenico Prattichizzo DII, University of Siena, Italy MTNS - July 5-9, 2010 D. Prattichizzo (Siena, Italy) MTNS - July 5-9, 2010 1 / 23

Geometric Control Theory for Linear Systems Block 1: Foundations [10:30-12.30]: Talk 1: Motivation and historical perspective, G. Marro [10:30-11:00] Talk 2: Invariant subspaces, L. Ntogramatzidis [11:00-11:30] Talk 3: Controlled invariance and invariant zeros, D. Prattichizzo [11:30-12:00] Talk 4: Conditioned invariance and state observation, F. Morbidi [12:00-12:30] Block 2: Problems and applications [15:30-17.30]: Talk 5: Stabilization and self-bounded subspaces, L. Ntogramatzidis [15:30-16:00] Talk 6: Disturbance decoupling problems, L. Ntogramatzidis [16:00-16:30] Talk 7: LQR and H 2 control problems, D. Prattichizzo [16:30-17:00] Talk 8: Spectral factorization and H 2 -model following, F. Morbidi [17:00-17:30] D. Prattichizzo (Siena, Italy) MTNS - July 5-9, 2010 2 / 23

Outline Statement of the problem Hamiltonian formulation Geometric insight in the Hamiltonian system Solving the cheap LQ problem in a geometric setting D. Prattichizzo (Siena, Italy) MTNS - July 5-9, 2010 3 / 23

LQR problems Consider and the index to be minimized J(x,u) = = 0 0 ẋ(t) = Ax(t)+B u(t) x(0) = x 0 y(t) = C x(t)+du(t) y T (t)y(t)dt = 0 [ xt (t) u T (t) ][ C T C C T D D T C D T D ( x T (t)qx(t)+2x T (t)s u(t)+u T (t)r u(t) ) dt with Q = C T C, S = C T D and R = D T D. Usually S = 0. The problem is Regular when R > 0 kerd = {0} Singular when R 0 kerd {0} Cheap when R = 0 kerd = IR m ][ ] x(t) dt u(t) D. Prattichizzo (Siena, Italy) MTNS - July 5-9, 2010 4 / 23

The main contributions LQR in a geometric setting The LQR problem for singular cases was investigated by Hautus, Silvermann, Willems using distributions. Saberi and Sannuti (1987) investigated the cheap (R = 0) and singular (R 0) problems with the special coordinate basis. Saberi, Sannuti and Stoorvogel (1992 and 1995) proposed a solution based on LMIs. Contribution The geometric techniques are applied directly to the Hamiltonian system. The cheap LQ problem admits solution with an algebraic feedback only if the initial condition is contained in a special subspace. This talk is based on Prattichizzo, Ntogramatzidis and G. Marro (Automatica, 2008) D. Prattichizzo (Siena, Italy) MTNS - July 5-9, 2010 5 / 23

Statement of the problem - Cheap LQ problem Consider the LTI system Σ with the assumptions: (A1) (A, B) is stabilizable ẋ(t) = Ax(t)+Bu(t), x(0) = x 0 y(t) = C x(t) (A2) Σ has no invariant zeros on j IR Determine the set of initial conditions x 0 such that a static state feedback matrix K exists s.t. A B K is stable the corresponding state trajectory minimizes J(x,u) = 1 2 0 y T (t)y(t)dt = 1 2 0 x T (t)c T C x(t)dt D. Prattichizzo (Siena, Italy) MTNS - July 5-9, 2010 6 / 23

Hamiltonian formulation Consider the Hamiltonian function H(t) := 1 2 xt (t)c T C x(t)+λ T (t)(ax(t)+bu(t)) and derive the state, costate equations and stationary condition as ẋ(t) = λ(t) = 0 = ( ) H(t) T = Ax(t)+Bu(t) λ(t) ( ) H(t) T = C T C x(t) A T λ(t) x(t) ( ) H(t) T = B T λ(t) u(t) D. Prattichizzo (Siena, Italy) MTNS - July 5-9, 2010 7 / 23

The Hamiltonian system The optimal state trajectory and control law for our problem satisfy the Hamiltonian system: [ẋ(t) ] [ ][ ] [ ] A 0 x(t) B = + u(t) λ(t) C T C A T λ(t) 0 ỹ(t) = [ 0 B ][ ] x(t) T = 0 λ(t) The matrices above are denoted by Â, ˆB and Ĉ, while (Â, ˆB,Ĉ) is denoted by ˆΣ. Problem: find u(t) = K x(t) such that for x(0) = x 0 ỹ(t)=0 for all t 0 x(t) 0 for t. D. Prattichizzo (Siena, Italy) MTNS - July 5-9, 2010 8 / 23

Geometric approach: notations and properties The geometric approach setting will require the following notations VΣ : the maximum (A,imB)-controlled invariant subspace of (A,B,C) contained in the null space of C. SΣ : the minimum (A,kerC)-conditioned invariant subspace containing the image of B. F is a friend of V Σ if (A+B F)V Σ V Σ At least one friend exists for every controlled invariant. D. Prattichizzo (Siena, Italy) MTNS - July 5-9, 2010 9 / 23

Geometric approach The Hamiltonian system is ˆx(t) = ˆx(t)+ ˆB u(t) 0 = Ĉ ˆx(t) ˆx = [ x λ ] The problem is equivalent to a disturbance decoupling problem in a state-costate domain. The cheap LQR has solutions iff an internally stabilizable output nulling subspace of ˆΣ exists whose projection on the state space of Σ contains x 0. Classical DDP conditions ˆV R kerĉ and modes on ˆV R must be stable. D. Prattichizzo (Siena, Italy) MTNS - July 5-9, 2010 10 / 23 x 0 ˆV R 2n

Solving the cheap LQR problem 1 compute V ˆΣ; 2 compute a matrix ˆF such that (Â+ˆBˆF)V ˆΣ V ˆΣ 3 compute ˆV R, the maximum internally stable (Â+ˆBˆF)-invariant contained in V ˆΣ; 4 if x 0 P(ˆV R ) the problem admits a solution F; if not, the problem has no solution. Next What about the geometric structure and dimension of ˆV R? D. Prattichizzo (Siena, Italy) MTNS - July 5-9, 2010 11 / 23

Further notation Σ is left invertible iff V Σ S Σ ={0}. Σ is right invertible iff V Σ +S Σ =IRn. Adjoint system Let Σ T = (A T,C T,B T ) be the adjoint of Σ = (A,B,C). Σ is left invertible iff Σ T is right invertible Σ is right invertible iff Σ T is left invertible Time-reversed system Σ 1 = ( A, B,C) is the time-reversed system associated with Σ. V Σ =V Σ 1 and S Σ =S Σ 1 Σ is left invertible iff Σ 1 is left invertible Σ is right invertible iff Σ 1 is right invertible D. Prattichizzo (Siena, Italy) MTNS - July 5-9, 2010 12 / 23

A geometric insight in the Hamiltonian system ˆΣ is the series connection of Σ and Σ T u(t) Σ v(t) Σ T y(t) ˆΣ Σ left invertible = ˆΣ is left and right invertible. Left invertibility of ˆΣ [IEEE TAC, 2002]. Right invertibility of ˆΣ comes from equivalence of ˆΣ T and through a coordinate transformation. ˆΣ 1 The set of invariant zeros of ˆΣ consists of all the invariant zeros of ˆΣ along with their opposite, hence they are all coupled by pairs (z, z). D. Prattichizzo (Siena, Italy) MTNS - July 5-9, 2010 13 / 23

How the second block is functionally driven u(t) Σ v(t) Σ T y(t) ˆΣ Let U be a basis matrix of the subspace of functional controllability C S Σ, and denote by the symbol Σ the new system (A T,C T U,B T ). w(t) U v(t) Σ T y(t) System Σ is left and right invertible, and V Σ =V Σ T =V Σ T and dims Σ = dims Σ = r D. Prattichizzo (Siena, Italy) MTNS - July 5-9, 2010 14 / 23

Relating geometric subspaces of Σ and ˆΣ u(t) Σ v(t) Σ T y(t) ˆΣ Denote by S and by S two basis matrices for SΣ and, respectively. S Σ The largest input containing subspace S ˆΣ of ˆΣ has the following structure: [ S ˆΣ S = im 0 S ] and dims ˆΣ = 2r D. Prattichizzo (Siena, Italy) MTNS - July 5-9, 2010 15 / 23

Main result ˆV ˆS x 0 r n r 2n If (A,B,C) is left invertible, then ˆΣ is left and right invertible and V ˆΣ S ˆΣ = IR 2n. = being dims ˆΣ = 2r, it follows that dimv ˆΣ = 2(n r). Modes in V ˆΣ are the zeros of ˆΣ that are all coupled by pairs (z, z). Modes in V ˆΣ are stable and unstable by pairs. D. Prattichizzo (Siena, Italy) MTNS - July 5-9, 2010 16 / 23

Recall: Statement of the problem - Cheap LQ problem Consider the LTI system Σ with the assumptions: (A1) (A, B) is stabilizable ẋ(t) = Ax(t)+Bu(t), x(0) = x 0 y(t) = C x(t) (A2) Σ has no invariant zeros on j IR Determine the set of initial conditions x 0 such that a static state feedback matrix K exists s.t. A B K is stable the corresponding state trajectory minimizes J(x,u) = 0 y T (t)y(t)dt = 0 x T (t)c T C x(t)dt D. Prattichizzo (Siena, Italy) MTNS - July 5-9, 2010 17 / 23

Recall: Disturbance decoupling [ẋ(t) ] [ = λ(t) A 0 C T C A T ][ ] x(t) + λ(t) ] ỹ(t) = [ 0 B T ][ x(t) λ(t) [ ] B u(t) 0 = 0 Problem: find u(t) = K x(t) such that for x(0) = x 0 ỹ(t)=0 for all t 0 x(t) 0 for t. [ This can ] be achieved if and only if x 0 is such that λ 0 exists so as that x0 belongs to an internally stabilizable output-nulling subspace of ˆΣ. λ 0 D. Prattichizzo (Siena, Italy) MTNS - July 5-9, 2010 18 / 23

Resolvent subspace V ˆΣ S ˆΣ x 0 r n r 2n If (A,B,C) is left invertible V ˆΣ S ˆΣ = IR 2n Modes in V ˆΣ are stable and unstable by pairs. The dimension of V R to which x 0 must belong for the solvability of the cheap problem is n r. D. Prattichizzo (Siena, Italy) MTNS - July 5-9, 2010 19 / 23

The main results Let Σ be left invertible. An (n r)-dimensional internally stabilizable output nulling subspace ˆV R of ˆΣ exists, such that all its n r poles, all unassignable, are stable. Let Σ be left invertible. The cheap problem is solvable if and only if x 0 P(ˆV R ) i.e., if and only if x 0 belongs to the projection of ˆV R on the state-space of Σ. The static feedback Let ˆF R a friend of ˆV R. Partition a basis matrix ˆV R of ˆV R and ˆF R as [ ] VX ˆV R =, ˆFR = [ ] F X F Λ V Λ the state-feedback matrix K solving the problem is given by K = (F X +F Λ V Λ V + X ) D. Prattichizzo (Siena, Italy) MTNS - July 5-9, 2010 20 / 23

Cheap vs. regular problems The dimension of V R is n r where r is the dimension of S Σ. Then r = 0 and n r < n there always exists x 0 V R for which the cheap LQ problem does not admit solutions with static feedback. Regular problems. If the problem is regular, kerd = {0}, then dims = 0andtherealwaysisasolutionforany x 0. x 0 ˆV R S ˆΣ r n r 2n D. Prattichizzo (Siena, Italy) MTNS - July 5-9, 2010 21 / 23

Extension to non left invertible systems Consider the auxiliary system (A+B F,BU,C), where 1. F is such that (A+B F)VΣ V Σ, and all the eigenvalues of (A+BF) restricted to VΣ S Σ (which are free) are stable 2. U is a basis matrix of (B 1 V Σ ) The system thus obtained is left invertible. Now, let K F be the optimal state-feedback matrix for the auxiliary system. Matrix K := UK F F is one of the many solutions of the original problem. Different solutions correspond to different choices of matrix F. D. Prattichizzo (Siena, Italy) MTNS - July 5-9, 2010 22 / 23

Concluding remarks A new approach to the solution of the cheap linear quadratic regulator problem has been presented for continuous-time systems. This method is based on a geometric characterization of the structure of the Hamiltonian system. This insight is achieved by using the standard tools of the geometric approach, without resorting to changes of basis and reduced order algebraic Riccati equations. By recasting the cheap LQ problem as a perfect decoupling problem in the Hamiltonian system, we derive: 1 the dimension of the subspace of the admissible initial conditions 2 a basis matrix of this subspace, which is a controlled invariant solving a standard decoupling problem with stability for the Hamiltonian system 3 an optimal state to input feedback matrix F, which is derived as a straightforward function of a friend of the above controlled invariant D. Prattichizzo (Siena, Italy) MTNS - July 5-9, 2010 23 / 23