控制系统的响应特性取决于控制律参数，经典的 PID 方法难以实现参数的自整定。强化学习能够通过系统自身和环境的交互实现参数的自动调整，但是在控制律参数需要频繁调整的应用场合，常规的强化学习方法无法满足实时性要求，而且容易陷入局部收敛。对传统的强化学习方法加以改进后，加快了在线学习速度，提高了强化学习算法的寻优能力。仿真结果表明，该方法可以在一定范围内快速求得全局最优解，提高控制系统的自适应性，为控制系统参数的自整定提供了依据。%The response characteristics of control system depend on the control law parameter.The classic PID method is dif-ficult to achieve the parameter self-tuning.Through the interaction of system itself and the environment,parameters can be adjusted automatically by reinforcement learning.However,in the application occasions where the control law parameters requires to be ad-justed frequently,the conventional reinforcement learning methods cannot meet the real-time requirements,and is easy to fall in-to local convergence.Based on the traditional reinforcement learning methods,an improvement method which can accelerate the learning speed and improve the optimizing ability of reinforcement learning algorithm is proposed.The simulation results show that this method can get global optimal solution quickly and improve the adaptivity of the control system in a certain range.It pro-vided a basis for the improvement of control system’s parameter self-tuning.
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