Optimal Output Tracking Control Problem Research Articles

H∞$$ {H}_{\infty } $$ optimal output tracking control for Markov jump systems: A reinforcement learning‐based approach

AbstractIn this paper, the optimal output tracking control problem for Markov jump systems is investigated, where the two cases with known or completely unknown transition probabilities are both considered. Based on game theory and performance, quadratic cost is considered, where a discount parameter is introduced into the quadratic cost in order to track unstable systems and eliminate the assumption that the noise energy is bounded. The game coupled algebraic Riccati equation and the corresponding controller are presented by dynamic programming. The stochastic stability of the tracking error system is further investigated. Moreover, iterative and reinforcement learning‐based algorithms are proposed for solving the optimal tracking controller with known or completely unknown transition probabilities, respectively. Finally, some numerical simulations on a DC motor are performed to validate the effectiveness of the proposed results.

Optimal output tracking control of linear discrete-time systems with unknown dynamics by adaptive dynamic programming and output feedback

This paper discusses the output-feedback-based model-free optimal output tracking control problem of discrete-time systems with completely unknown system models under mild assumptions. The only information that allows utilisation is the system output and the reference output. To overcome these challenges, the paper aims to solve a model-free optimal output regulation problem for achieving optimal output tracking control; solving an optimal output regulation problem is equivalent to solving a linear quadratic regulation (LQR) problem and a constrained static optimisation problem. The state reconstruction is first given to represent the system state in terms of the input and output sequences. A data-driven value iteration (VI) algorithm is then proposed to iteratively approximate the solution to the discrete-time algebraic Riccati equation (ARE) of the corresponding LQR problem on the basis of input and output data. Next, based on the iterative solutions with respect to the ARE, a model-free solution is provided for the corresponding constrained static optimisation problem. Finally, an alternative reference system equivalent to the original reference system is established to avoid the requirement of having the knowledge of the reference system dynamics and the reference state. A numerical example is employed to demonstrate the effectiveness of the proposed control scheme.

Information fusion estimation-based path following control of quadrotor UAVs subjected to Gaussian random disturbance

Random disturbance has a detrimental effect on the reliability and safety of quadrotor unmanned aerial vehicles (UAVs). This paper proposes an anti-Gaussian random disturbance control method for the path following of a quadrotor UAV. The quadrotor system is linearized and divided into two subsystems, i.e., a translational subsystem and a rotational subsystem, and hierarchical strategy is used to design the overall control architecture. In order to suppress the negative effects of Gaussian random disturbances and simplify the design process of linear–quadratic optimal output tracking control problem, a new information fusion estimation based robust control named Gaussian information fusion control (GIFC) scheme is proposed. The convergence of the output tracking errors of GIFC system is proved via Lyapunov theory. The proposed GIFC control scheme is employed for position and attitude controller designs to enhance the robustness of the quadrotor system to Gaussian random perturbations. Finally numerical simulation experiments illustrate the effectiveness and robustness of the proposed control strategy.

Output Tracking Control Based on Adaptive Dynamic Programming With Multistep Policy Evaluation

In this paper, the optimal output tracking control problem of discrete-time nonlinear systems is considered. First, the augmented system is derived and the tracking control problem is converted to the regulation problem with a discounted performance index, which relies on the solution of the Bellman equation. It is known that policy iteration and value iteration are two classical algorithms for solving the Bellman equation. Through analysis of the two algorithms, it is found that policy iteration converges fast while requires an initial admissible control policy, and value iteration avoids the requirement of an initial admissible control policy but converges slowly. To achieve the tradeoff between policy iteration and value iteration, the multistep heuristic dynamic programming (MsHDP) is proposed by using multistep policy evaluation scheme. The convergence of MsHDP algorithm is proved by demonstrating that it converges to the solution of the Bellman equation. Subsequently, neural network-based actor-critic structure is developed to implement the MsHDP algorithm. The effectiveness and advantages of the developed MsHDP method are validated through comparative simulation studies.

Optimal Output Tracking Control for Nonlinear Systems with External Disturbances Based on Stability Degree Constraint

This paper considers the optimal output tracking control problem for nonlinear systems affected by external disturbances based on stability degree constraint. The objective is to find an optimal output tracking controller, by which the cost function minimum and the state with the optimal having a higher mean-square convergence rate can be obtained. An optimal output tracking law is derived from a Riccati equation and Matrix equations. We give the existence and uniqueness conditions of the control law. Finally, a practical example is given to illustrate the effectiveness of the theory.

Observer-based optimal output tracking for discrete-time systems with multiple state and input delays

The optimal output tracking control (OOTC) problem for a class of discrete-time systems with state and input delays is addressed. An augmented system is constructed such that the OOTC problem can be transformed into a two-point boundary value (TPBV) problem with both advance and delay terms from the necessary optimality conditions. The successive approximating method recently developed is extended to obtain an approximate solution of the TPBV problem, which is then used to obtain a feedforward and feedback tracking controller. An observer is designed for the uncertain reference input such that the feedforward controller is physically realizable. Simulations show the results are effective even with long time-delays.

Series-based approximate approach of optimal tracking control for nonlinear systems with time-delay

The optimal output tracking control (OTC) problem for nonlinear systems with time-delay is considered. Using a series-based approximate approach, the original OTC problem is transformed into iteration solving linear two-point boundary value problems without time-delay. The OTC law obtained consists of analytical linear feedback and feedforward terms and a nonlinear compensation term with an infinite series of the adjoint vectors. By truncating a finite sum of the adjoint vector series, an approximate optimal tracking control law is obtained. A reduced-order reference input observer is constructed to make the feedforward term physically realizable. Simulation examples are used to test the validity of the series-based approximate approach.