Online Adaptive Optimal Control Algorithm Based on Synchronous Integral Reinforcement Learning With Explorations
In this paper, we present a novel algorithm named synchronous integral Q-learning, which is based on synchronous policy iteration, to solve the continuous-time infinite horizon optimal control problems of input-affine system dynamics. The integral reinforcement is measured as an excitation signal in this method to estimate the solution to the Hamilton-Jacobi-Bellman equation. Moreover, the proposed method is completely model-free, i.e. no a priori knowledge of the system is required. Using policy iteration, the actor and critic neural networks can simultaneously approximate the optimal value function and policy. The persistence of excitation condition is required to guarantee the convergence of the two networks. Unlike in traditional policy iteration algorithms, the restriction of the initial admissible policy is relaxed in this method. The effectiveness of the proposed algorithm is verified through numerical simulations.
Code (0)
등록된 구현이 없습니다.
Tasks
Q-Learningreinforcement-learningReinforcement Learning (RL)Similar Papers 제목 키워드 기반
Optimal Torque Control of Permanent Magnet Synchronous Motors Using Adaptive Dynamic Programming
In this study, a new approach based on adaptive dynamic programming (ADP) is proposed to control permanent magnet synchronous motors (PMSMs). The objective of this paper is to control the torque and consequently the spee…
Model-free Nearly Optimal Control of Constrained-Input Nonlinear Systems Based on Synchronous Reinforcement Learning
In this paper a novel model-free algorithm is proposed. This algorithm can learn the nearly optimal control law of constrained-input systems from online data without requiring any a priori knowledge of system dynamics. B…
Reinforcement Learning (RL)Adaptive Actor-Critic Based Optimal Regulation for Drift-Free Uncertain Nonlinear Systems
In this paper, a continuous-time adaptive actor-critic reinforcement learning (RL) controller is developed for drift-free nonlinear systems. Practical examples of such systems are image-based visual servoing (IBVS) and w…
Reinforcement Learning (RL)ADDIS: an adaptive discarding algorithm for online FDR control with conservative nulls
Major internet companies routinely perform tens of thousands of A/B tests each year. Such large-scale sequential experimentation has resulted in a recent spurt of new algorithms that can provably control the false discov…
Data-Driven Subsynchronous Oscillation Suppression for Renewable Energy Integrated Power Systems Based on Koopman Operator
Recently, subsynchronous oscillations (SSOs) have emerged frequently worldwide, with the high penetration of renewable power generation in modern power systems. The SSO introduced by renewables has become a prominent new…
Model Predictive Control