Optimal Control Of Linear Systems Research Articles

Optimal control of linear systems with balanced reduced-order models: Perturbation approximations

In this article we study balanced model reduction of linear systems for feedback control problems. Specifically, we focus on linear quadratic regulators with collocated inputs and outputs, and we consider perturbative approximations of the dynamics in the case that the Hankel singular values corresponding to the hardly controllable and observable states go to zero. To this end, we consider different perturbative scenarios that depend on how the negligible states scale with the small Hankel singular values, and derive the corresponding limit systems as well as approximate expressions for the optimal feedback controls. Our approach that is based on a formal asymptotic expansion of an algebraic Riccati equations associated with the Pontryagin maximum principle and that is validated numerically shows that model reduction based on open-loop balancing can also give good closed-loop performance.

Predictive Control for Linear and Hybrid Systems [Bookshelf

This book offers new insight in the realm of predictive control. The authors demonstrate how predictive control can be implemented in applications that were thought to be impossible when they started their collaboration about two decades ago. The book not only contains mature, and tried and tested, material but also the fundamentals necessary for understanding recent methods that have been receiving considerable interest in academia and industry. Depending on your perspective, predictive control can be seen as a subset or superset of classical optimal control or as something completely different. Here, predictive control is viewed as a modern optimal control method that builds on classical optimal control results, when possible. The main thesis in predictive control is that most practical control problems are naturally formulated as a sequence of infinite-dimensional, constrained, nonlinear optimal control problems, which are often impossible to solve. For example, in most applications, the optimal controller is not simply a linear, finite-dimensional dynamical system, as in proportional-integral-derivative, linear-quadratic Guassian, or H-infinity control, even if the model of the controlled system is linear and finite dimensional. The optimal control policy can be discontinuous, even if all the functions used to define the optimal control problem are smooth. Researchers in predictive control aim to understand these and other fundamental problems that naturally arise in applications, while finding practical and approximate solutions with appropriate guarantees. The book's emphasis is on the use of multiparametric programming to compute the explicit solution to the FHOCPs. It is divided into five parts: I) Basics of Optimization, II) Multiparametric Programming, III) Optimal Control, IV) Constrained Optimal Control of Linear Systems, and V) Constrained Optimal Control of Hybrid Systems. If readers started at the beginning and worked all the way to the end, they will have been introduced to a variety of fundamental and interesting topics in optimization, geometry, control, and dynamical systems theory that could be applied in a number of areas. In terms of a target audience, the book is suitable for both advanced undergraduate engineering students and in some cases, engineering postgraduates and above.

Optimal control of linear systems with fractional derivatives

Abstract Problem of time-optimal control of linear systems with fractional Caputo derivatives is examined using technique of attainability sets and their support functions. A method to construct a control function that brings trajectory of the system to a strictly convex terminal set in the shortest time is elaborated. The proposed method uses technique of set-valued maps and represents a fractional version of Pontryagin’s maximum principle. A special emphasis is placed upon the problem of computing of the matrix Mittag-Leffler function, which plays a key role in the proposed methods. A technique for computing matrix Mittag-Leffler function using Jordan canonical form is discussed, which is implemented in the form of a MATLAB routine. Theoretical results are supported by examples, in which the optimal control functions, in particular of the “bang-bang” type, are obtained.

Optimal Control of Linear Systems With Limited Control Actions: Threshold-Based Event-Triggered Control

We consider a finite-horizon linear-quadratic optimal control problem where only a limited number of control messages are allowed for sending from the controller to the actuator. To restrict the number of control actions computed and transmitted by the controller, we employ a threshold-based event-triggering mechanism that decides whether or not a control message needs to be calculated and delivered. Due to the nature of threshold-based event-triggering algorithms, finding the optimal control sequence requires minimizing a quadratic cost function over a nonconvex domain. In this paper, we first provide an exact solution to this nonconvex problem by solving an exponential number of quadratic programs. To reduce computational complexity, we then propose two efficient heuristic algorithms based on greedy search and the alternating direction method of multipliers technique. Later, we consider a receding horizon control strategy for linear systems controlled by event-triggered controllers, and we further provide a complete stability analysis of receding horizon control that uses finite-horizon optimization in the proposed class. Numerical examples testify to the viability of the presented design technique.

Adaptation of the control synthesized by the ACAR method to the control based on the implementation of the maximum principle

A special case of synthesizing the electromechanical control system by the maximum method and using the Analytical Construction method of Aggregate Regulators (ACAR) is considered in the article. For the basis the task of synthesizing the optimal for speed electromechanical positioning system was chosen, while the moment of resistance to movement linearly depended on the output coordinate of the system, that is, on the angle of the engine rotor rotation. Synthesis of the optimal system for speed makes it possible to increase the efficiency of the entire production process in many production tasks, and the synthesis of the optimal linear control system based on the maximum principle is a fairly well-formalized problem. Here it should be noted that the procedure for synergistic synthesis of the optimal control system has no such formalization. An approach that brings together the solutions obtained by these two methods, which makes it possible to increase the efficiency of the ACAR method by adding some features of the methodology for synthesizing optimal systems by introducing nonlinearity of the “saturation” type is proposed in the article. The results obtained made it possible to formulate the following basic scientific proposition: the synthesis of a control system based on the synergetic approach makes it possible to obtain a system close to optimal (quasi-optimal, but after the modification of the synergetic synthesis method itself.) Here we also formulate the hypothesis of a connection between the time constants, using the ACAR method, with the optimal control switching time determined in the maximum method.

Comparison theorems for conjugate points in sub-Riemannian geometry

We prove sectional and Ricci-type comparison theorems for the existence of conjugate points along sub-Riemannian geodesics. In order to do that, we regard sub-Riemannian structures as a special kind of variational problems. In this setting, we identify a class of models, namely linear quadratic optimal control systems, that play the role of the constant curvature spaces. As an application, we prove a version of sub-Riemannian Bonnet-Myers theorem and we obtain some new results on conjugate points for three dimensional left-invariant sub-Riemannian structures.

Optimal control of linear systems with large and variable input delays

This paper proposes an optimal control law for linear systems affected by input delays. Specifically we prove that when the delay functions are known it is possible to generate the optimal control for arbitrarily large delay values by using a DDE without distributed terms. The solution can be seen as a chain of predictors whose size depends on the maximum delay.

Optimal control of linear systems with interval constraints

For linear systems with interval constraints, a method for computing a time-optimal control is proposed. The method is based on transforming a quasi-optimal control. The properties and features of the quasi-optimal control are examined. A technique is described for dividing the domain of initial conditions into reachable sets over different times and for approximating each set by a family of hyperplanes. An iterative method for computing an optimal control with interval constraints is developed. The convergence of the method is proved, and a sufficient condition for the convergence of the computational process is obtained. The radius of local quadratic convergence is found. Numerical results are presented.

Numerical methods for control optimization in linear systems

Numerical methods are considered for solving optimal control problems in linear systems, namely, terminal control problems with control and phase constraints and time-optimal control problems. Several algorithms with various computer storage requirements are proposed for solving these problems. The algorithms are intended for finding an optimal control in linear systems having certain features, for example, when the reachable set of a system has flat faces.

Minimax optimal control of linear system with input-dependent uncertainty

In this paper, the quadratic minimax optimal control of linear system with input-dependent uncertainty is studied. We show that it admits a unique solution and can be approximated by a sequence of finite-dimensional minimax optimal parameter selection problems. These finite-dimensional minimax optimal parameter selection problems are further reduced to scalar optimization problems which also admit unique solutions. Thus, the original minimax optimal control problem is solved via solving a sequence of simple scalar optimization problems. Numerical experiments are presented to illustrate the developed method.

Numerical solution of some linear optimal control systems with pantograph delays

The Bezier curves are presented to solve the optimal control problem with pantographs delays. Direct algorithm for solving this problem is given. We have chosen the Bezier curves as piecewise polynomials of degree n, and determine Bezier curves on [t0, tf] by n+1 control points. Numerical examples illustrate that the algorithm is applicable, and very easy to use.

On pulse optimal control of linear systems with aftereffect

The paper deals with the problem of pulse optimal control of a linear dynamic system with aftereffect. As a functional, the degenerate quadratic functional of the most common kind is considered. The absence of control in the functional leads to the fact that the optimal control contains a pulse component. Sufficient conditions of existence of the pulse optimal control are obtained and the equations describing coefficients in the optimal control are worked out. Sufficient conditions that make it possible to integrate equations and to find the coefficients in an explicit form for the optimal control are established. A model example is considered.

Min–max optimal control of linear systems with uncertainty and terminal state constraints

In this paper, a class of min–max optimal control problems with continuous dynamical systems and quadratic terminal constraints is studied. The main contribution is that the original terminal state constraint in which the disturbance is involved is transformed into an equivalent linear matrix inequality without disturbance under certain conditions. Then, the original min–max optimal control problem is solved via solving a sequence of semi-definite programming problems. An example is presented to illustrate the proposed method.

Solution of linear optimal control systems by differential transform method

In this paper, we obtain the approximate solutions for optimal control of linear systems, which have a quadratic performance index. The differential transform method (DTM) is applied for solving the extreme conditions obtained from the Pontryagin’s maximum principle. The differential transform method is one of the approximate methods, which can be easily applied to many linear and nonlinear problems and is capable of reducing the size of computational work. Applying DTM, we construct an optimal feedback control law. The results reveal that the proposed method are very effective and simple. Comparisons are made between the results of the proposed methods, homotopy perturbation method, Adomian decomposition method and exact solutions.

Solving the Optimal Control of Linear Systems via Homotopy Perturbation Method

In this paper, Homotopy perturbation method is used to find the approximate solution of the optimal control of linear systems. In this method the initial approximations are freely chosen, and a Homotopy is constructed with an embedding parameter , which is considered as a “small parameter”. Some examples are given in order to find the approximate solution and verify the efficiency of the proposed method.

Linear quadratic optimal control system design using particle swarm optimization algorithm

Selecting appropriate weighting matrices for desired linear quadratic regulator (LQR) controller design using evolutionary algorithms is presented in this paper. Obviously, it is not easy to determine the appropriate weighting matrices for an optimal control system and a suitable systematic method is not presented for this goal. In other words, there is no direct relationship between weighting matrices and control system characteristics, and selecting these matrices is done by using trial and error based on designer’s experience. In this paper, we use the particle swarm optimization (PSO) method which is inspired by the social behavior of fish and birds in finding food sources to determine these matrices. Stable convergence characteristics and high calculation speed are the advantages of the proposed method. Simulation results demonstrate that in comparison with genetic algorithms (GAs), the PSO method is very efficient and robust in designing of optimal LQR controller. Key words: Linear quadratic regulator (LQR), weighting matrices, particle swarm optimization, genetic algorithm.

Sliding mode optimal control for linear systems

This paper addresses the optimal control problem for a linear system with respect to a Bolza–Meyer criterion with a non-quadratic non-integral term. The optimal solution is obtained as a sliding mode control, whereas the conventional linear feedback control fails to provide a causal solution. Performance of the obtained optimal controller is verified in the illustrative example against the conventional LQ regulator that is optimal for the quadratic Bolza–Meyer criterion. The simulation results confirm an advantage in favor of the designed sliding mode control.

Suboptimal control of linear systems with delays in state and input by orthonormal basis

This paper presents a method for finding the optimal control of linear systems with delays in state and input and the quadratic cost functional using an orthonormal basis for square integrable functions. The state and control variables and their delays are expanded in an orthonormal basis with unknown coefficients. Using operational matrices of integration, delay and product, the relation between coefficients of variables is provided. Then, necessary condition of optimality is driven as a linear system of algebraic equations in terms of the unknown coefficients of state and control variables. As an application of this method, the linear Legendre multiwavelets as an orthonormal basis for L 2[0, 1] are used. Two time-delayed optimal control problems are approximated to establish the usefulness of the method.

Dynamic Programming Techniques for Feedback Control

AbstractWe consider a dynamic programming approach for solving optimal control problems of sampled continuous-time systems. Robustness to bounded noise and model uncertainties is provided by formulating the problem in the framework of differential games. We use a semi-Lagrangian scheme for the computation of the value function and the state feedback control law for constrained infinite horizon optimal control problems. The approach is illustrated with the optimal control of linear systems and nonlinear systems subject to bounded disturbances.

Optimal LQG control over continuous fading channels

AbstractThis paper studies the optimal control of linear systems over continuous valued fading channels. The sensor measurements are sent over a fading wireless channel to a remote controller using the analog amplify and forward technique. The controller then computes a control signal, which is transmitted over another fading channel to the actuator. Under the assumption of full channel state information (CSI) for both wireless links, we derive the optimal LQG control law. In the case where full channel state information is not available but channel statistics are available, we present the optimal linear static estimator and controller. Numerical comparisons are made between the full CSI and statistical CSI solutions.