Optimal Control Of Linear Systems Research Articles

Convexified Open-Loop Stochastic Optimal Control for Linear Systems With Log-Concave Disturbances

We consider open-loop solutions to the stochastic optimal control of a linear dynamical system with an additive non-Gaussian, log-concave disturbance. We propose a novel, sampling-free approach, based on characteristic functions and convex optimization, to cast the stochastic optimal control problem as a difference-of-convex program. Our method invokes higher moments, resulting in less conservatism compared to moment-based approaches. We employ piecewise affine approximations and the convex-concave procedure for efficient solution via standard conic solvers. We demonstrate that the proposed solution is competitive with sampling and moment based approaches, without compromising probabilistic constraints.

Learning-based Optimal Control for Linear Systems with Model Uncertainties

The problem of optimal control with unknown dynamics is important and challenging. Most existing learning-based methods usually do not utilize the prior knowledge of the model and choose to learn the system model from scratch, leading to high identification costs and unbounded identification time. In practice, prior knowledge of the model may be available. Therefore, in this paper, we investigate the Linear Quadratic Gaussian (LQG) control problem for an unknown system but with prior knowledge of model uncertainties. We develop an effective learning procedure to identify the optimal controller parameters, leading to the least possible cost. Specifically, we analyze the performance of the optimal observer under unknown system dynamics and reveal the relationship between the regression error and the observer performance. We propose a learning procedure to design the optimal controller incorporating prior knowledge of the system. Simulations are conducted to illustrate the effectiveness of our controller design.

Robust Optimal Control With Inexact State Measurements and Adjustable Uncertainty Sets

The efficacy of robust optimal control with adjustable uncertainty sets is verified in several domains under the perfect state information setting. This paper investigates constrained robust optimal control for linear systems with linear cost functions subject to uncertain disturbances and state measurement errors that are both residing in adjustable uncertainty sets. We first show that the class of affine feedback policies of state measurements are equivalent to the class of affine feedback policies of estimated disturbances in terms of their conservativeness. Then, we formulate and solve a robust optimal control problem with adjustable uncertainty sets by considering the disturbance feedback policies. In contrast to the conventional robust optimal control, where uncertainty sets are fixed and known a priori, the uncertainty sets themselves are regarded as decision variables in our design. In particular, given the metrics for evaluating the optimal size/shape of the polyhedral uncertainty sets, a bilinear optimization problem is formulated to decide the optimal size/shape of uncertainty sets and a corresponding optimal control policy to robustly guarantee that the system will respect its constraints for all admissible uncertainties. In addition, we introduce a convex approximation for the proposed scheme to provide a computationally efficient inner approximation of the original problem. The proposed scheme is illustrated by numerical simulation of a building temperature control problem to demonstrate its effectiveness.

Online Optimal State Feedback Control of Linear Systems Over Wireless MIMO Fading Channels

We consider the optimal control of linear systems over wireless MIMO fading channels, where the MIMO wireless fading and random access of the remote controller may cause intermittent controllability or uncontrollability of the closed-loop control system. We formulate the optimal control design over random access MIMO fading channels as an infinite horizon average cost Markov decision process (MDP), and we propose a novel state reduction technique such that the optimality condition is transformed into a time-invariant reduced-state Bellman optimality equation. We provide the closed-form characterizations on the existence and uniqueness of the optimal control solution via analyzing the reduced-state Bellman optimality equation. Specifically, in the case that the closed-loop system is almost surely controllable, we show that the optimal control solution always exists and is unique. In the case that MIMO fading channels and the random access of the remote controller destroy the closed-loop controllability, we propose a novel controllable and uncontrollable positive semidefinite (PSD) cone decomposition induced by the singular value decomposition (SVD) of the MIMO fading channel contaminated control input matrix. Based on the decomposed fine-grained reduced-state Bellman optimality equation, we further propose a closed-form sufficient condition for both the existence and the uniqueness of the optimal control solution. The closed-form sufficient condition reveals the fact that the optimal control action may still exist even if the closed-loop system suffers from intermittent controllability or almost sure uncontrollability.

Second Chebyshev wavelets (SCWs) method for solving finite-time fractional linear quadratic optimal control problems

In this paper, we present an indirect computational procedure based on the truncated second kind Chebyshev wavelets for finding the solutions of Caputo fractional time-invariant linear optimal control systems which the functional cost consists of the finite-time quadratic cost function. Utilizing the operational matrices of second Chebyshev wavelets (SCWs) of the Riemann–Liouville fractional integral, the corresponding linear two-point boundary value problem (TPBVP), obtained from the fractional Euler–Lagrange equations, is reduced to a coupled Sylvester-type matrix equation. An equivalent linear matrix form using the Kronecker product is constructed. The upper bound of the error of the SCWs approximation and the convergence of the proposed method are investigated. Low computational complexity and flexible accuracy are two important superiorities of this approach. Numerical experiments provide satisfactory results compared to the exiting techniques.

Time optimal control for linear systems on Lie groups

This paper is devoted to the study of time optimal control of linear systems on Lie groups. General necessary conditions of the existence of time minimizers are stated when the controls are unbounded. The results are applied to systems on various Lie groups.

Optimal control of linear fuzzy time-variant controlled systems

In this paper, we study linear fuzzy time-variant optimal control systems using the generalized differentiability concept and we present the general form of optimal controls and states. Some examples are provided to illustrate our results.

Linear-Cubic Locally Optimal Control of Linear Systems and Its Application for Aircraft Guidance

In the paper, the requirements for aircraft guidance methods are considered. It is noted that the existing methods of optimization of linear control systems based mainly on the minimization of quadratic functionals of quality do not allow providing the set of requirements for interception systems of aerial objects. It is proposed to use new local quadratic-biquadratic quality functionals, whose minimization makes it possible to obtain more general linear-cubic control laws that are adequate for modern requirements. The biquadratic terminal part of the functional contains fourth-degree summands. An example of the synthesis of a specific guidance method is considered and analyzed.

(1912-5594) Optimal control of linear fuzzy time-variant controlled systems

In this paper, we study linear fuzzy time-variant optimal control systems using the generalized differentiability concept and we present the general form of optimal controls and states. Some examples are provided to illustrate our results.

Online Output-Feedback Optimal Control of Linear Systems Based on Data-Driven Adaptive Learning

Abstract This paper proposes a new approach to solve the output-feedback optimal control for linear systems. A modified algebraic Riccati equation (MARE) is constructed by investigating the corresponding relationship with the state-feedback optimal control. To solve the derived MARE, an online data-driven adaptive learning is designed, where the vectorization operation and Kronecker’s product are applied to reformulate the output Lyapunov function. Consequently, only the measurable system input and output are used to derive the solution of the MARE. In this case, the output-feedback optimal control solution can be obtained in an online manner without resorting to the unknown system states. Simulation results are provided to demonstrate the efficacy of the suggested method.

Adaptive Control Strategies for Urban Network Traffic via a Decentralized Approach With User-Optimal Routing

This paper presents an adaptive linear quadratic optimal traffic control system. The control strategies are solved via a decentralized approach and complemented with a user-optimal network traffic router. The user-optimal routing algorithm assists drivers respond to prevailing traffic state and control settings and seek the quickest route toward their destinations. The proposed control system is implemented and tested over different scenario settings including a real life scenario in Central London, UK. The study reveals that the proposed system could coverage to a performance similar to its centralized counterpart with the routing algorithm even under congested conditions. This highlights the potential of decentralized control with effective travel guidance in cooperative traffic management.

Extended adaptive optimal control of linear systems with unknown dynamics using adaptive dynamic programming

AbstractThe extended infinite horizon optimal control problem of continuous time linear systems with unknown dynamics is investigated in this paper. This optimal control problem can be solved using the corresponding extended algebraic Riccati equation. A new policy iteration algorithm is proposed to approximate the solution of the extended algebraic Riccati equation when the system dynamics are known. The convergence of the proposed algorithm is proved. Based on the proposed policy iteration algorithm, an online adaptive dynamic programming (ADP) algorithm is developed to find the solution to the extended infinite horizon optimal control problem of unknown continuous time linear systems. The convergence of the online ADP algorithm is analyzed. Finally, two simulation examples are given to demonstrate the effectiveness of the developed online ADP algorithm.

Linear quadratic regulator problem governed by granular neutrosophic fractional differential equations

Quadratic cost functions estimation in the linear optimal control systems governed by differential equations (DEs) or partial differential equations (PDEs) has a well-established discipline in mathematics with many interfaces to science and engineering. During its development, the impact of uncertain phenomena to objective function and the complexity of the systems to be controlled have also increased significantly. Many engineering problems like magnetohydromechanical, electromagnetical and signal analysis for the transmission and propagation of electrical signals under uncertain environment can be dealt with. In this paper, we study the optimal control problem with operating a fractional DEs and PDEs at minimum quadratic objective function in the framework of neutrosophic environment and granular computing. However, there has been no studies appeared on the neutrosophic calculus of fractional order. Hence, we will introduce some derivatives of fractional order, including the neutrosophic Riemann–Liouville fractional derivatives and neutrosophic Caputo fractional derivatives. Next, we propose a new setting of two important problems in engineering. In the first problem, we investigate the numerical and exact solutions of some neutrosophic fractional DEs and neutrosophic telegraph PDEs. In the second problem, we study the optimality conditions together with the simulation of states of a linear quadratic optimal control problem governed by neutrosophic fractional DEs and PDEs. Some key applications to DC motor model and one-link robot manipulator model are investigated to prove the effectiveness and correctness of the proposed method.

Fuzzy Optimal Control of Linear Systems. Part 2. Program Control

Fuzzy Optimal Control of Linear Systems. Part 2. Program Control

Fuzzy Optimal Control of Linear Systems. Part 1. Positional Control

Fuzzy Optimal Control of Linear Systems. Part 1. Positional Control

Optimal Control of Linear Systems of Arbitrary Fractional Order

Abstract Optimal control problem for linear systems of arbitrary fractional order in the sense of Riemann–Liouville is treated in the paper. The technique of attainability sets and their support functions is used to obtain sufficient conditions for time-optimal control similar to that of Pontryagin’s maximum principle. Theoretical results are supported by example.

Solving state feedback control of fractional linear quadratic regulator systems using triangular functions

The present paper is useful for two reasons. First, as far as we know, the right operational matrix of the Riemann–Liouville fractional integral for triangular functions is introduced for the first time. So, hereafter, it can be applied in approximating any fractional optimal control problem in Caputo sense, over and over. Second, the rate of convergence of the triangular functions under a new-blown norm ‖.‖p,α in its corresponding space, denoted by Lp,α, is investigated. So far, the error analysis in Lp,α space has not been studied.In this article, both left and right operational matrices of the triangular functions for arbitrary fractional order integral α > 0 in Caputo sense are applied to approximate solutions of fractional linear optimal control systems which have a quadratic performance index. The necessary and sufficient optimality conditions are stated in a fractional two-point boundary value problem. This problem is converted to a set of coupled equations involving left and right Riemann–Liouville integral operators. Using these operational matrices of the triangular functions, an extension of the functions of control, state, co-state and other engaged functions is considered. This technique is a successful approach because of reducing such systems to a system of coupled Sylvester equations. Applying the definition of Kroneker product, a linear algebraic system containing the corresponding matrices of the original problem and the operational matrices of fractional order integral can be constructed. Accordingly, fractional linear quadratic optimal control can be solved indirectly. The advantage of this methodology, in addition to simplicity, is its low computational cost and its flexible precision. The simulation results confirm the reliability and validity of this method.

Data-Driven Adaptive Optimal Control for Linear Systems With Structured Time-Varying Uncertainty

In this paper, a data-driven adaptive optimal control strategy is proposed for a class of linear systems with structured time-varying uncertainty, minimizing the upper bound of a pre-defined cost function while maintaining the closed-loop stability. An off-policy data-driven reinforcement learning algorithm is presented, which uses repeatedly the online state signal on some fixed time intervals without knowing system information, yielding a guaranteed cost control (GCC) law with quadratic stability for the system. This law is further optimized through a particle swarm optimization (PSO) method, the parameters of which are adaptively adjusted by a fuzzy logic mechanism, and an optimal GCC law with the minimum upper bound of the cost function is finally obtained. The effectiveness of this strategy is verified on the dynamic model of a two-degree-of-freedom helicopter, showing that both stability and convergence of the closed-loop system are guaranteed and that the cost is minimized with much less iteration than the conventional PSO method with constant parameters.

Estimation of proximity of controls synthesized on basis of maximum principle and ADAR method

Introduction. A special case of synthesizing the same electromechanical control system by the Pontryagin maximum principle and by the synergetic synthesis method is considered. The task was to solve the synthesis problem of the time optimal electromechanical position control system; herewith the travel resistance modulus linearly depended on the output coordinate of the system. This approach to the selection of the synthesis problem was because the synthesis of time optimal systems is one of the most widespread problems, and it is solved by increasing the efficiency of the existing control systems.Materials and Methods. Synthesis of the time optimal linear control system based on the maximum principle is a widely accepted problem in the modern control theory. However, the procedure of synergistic synthesis does not have such formalization. This being the case, the paper suggests an approach that brings together these two methods, which, in our opinion, will increase the efficiency of the synergistic synthesis method through adding some features of the synthesis methodology for optimal systems.Research Results. The paper formulates two key concepts. The first one is as follows: the application of the maximum principle for an object of the DC motor class when synthesizing the positioning algorithm under the conditions of linear loading functionally dependent on the engine rotation angle allows the time optimal system to be optimized. The second concept states that synthesis of a control system based on the synergistic approach enables to obtain a system close to optimal (quasioptimal), but after modifying the synergetic synthesis method itself. A hypothesis is formulated on the possible connection between the introduced (when implementing the procedure of state space extension in the synergetic synthesis method) time constants with the optimal switching time of control defined in the maximum method.Discussion and Conclusions. The synthesis through the maximum control technique and the ADAR method is performed. In virtue of the comparison of efficiency of these methods, a hypothesis is put forward on the possible compatibility of the studied methods.

Optimal Control of Linear Systems of Arbitrary Fractional Order

Optimal control problem for linear systems of arbitrary fractional order in the sense of Riemann–Liouville is treated in the paper. The technique of attainability sets and their support functions is used to obtain sufficient conditions for time-optimal control similar to that of Pontryagin’s maximum principle. Theoretical results are supported by example.