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Reinforcement Q-learning for optimal tracking control of linear discrete-time systems with unknown dynamics
In this paper, a novel approach based on the Q-learning algorithm is proposed to solve the infinite-horizon
linear quadratic tracker (LQT) for unknown discrete-time systems in a causal manner. It is assumed
that ...
Optimal adaptive leader-follower consensus of linear multi-agent systems: Known and unknown dynamics
of policy iteration and optimal adaptive control techniques to solve the leader follower consensus problem under known and unknown dynamics. Simulation results verify the effectiveness of the proposed methods....
Optimal Tracking Control for Linear Discrete-time Systems Using Reinforcement Learning
optimal control solution are obtained simultaneously by solving
the augmented ARE. To find the solution to the augmented
ARE online, policy iteration as a class of reinforcement learning
algorithms, is employed. This algorithm is implemented...
Adaptive Optimal Control of Partially-unknown Constrained-input Systems using Policy Iteration with Experience Replay
nonquadratic
performance functional. An online policy iteration algorithm that uses integral
reinforcement knowledge is developed to learn the solution to the optimal control problem
online without knowing the full dynamics model...
Policy Iteration Algorithm Based on Experience Replay to Solve H∞ Control Problem of Partially Unknown Nonlinear Systems
In this paper, an online adaptive optimal control algorithm based on policy iteration (PI) is developed to solve the H∞ control problem of partially unknown nonlinear continuous-time (CT) systems. The convergence of existing PI algorithms...
A policy iteration approach to online optimal control of continuous-time constrained-input systems
This paper is an effort towards developing an online learning algorithm to find the optimal control
solution for continuous-time (CT) systems subject to input constraints. The proposed method is based on
the policy iteration (PI...