基于参数寻优的自学习算法在两轮机器人控制上的应用

针对两轮机器人现有控制算法的弊端,基于自学习参数寻优算法设计自适应控制器.该控制器结构简单,无需依赖精确数学模型,经过多次学习训练,即可获得最优控制参数.将该控制器应用于两轮机器人的平衡控制中,并与线性二次型(linear quadratic regulator,LQR)最优控制器进行对比,仿真结果验证了该算法的正确性、有效性,凸显出较强的鲁棒性和仿生学习性;将该控制器应用于两轮机器人物理系统,取得了良好的控制效果.

Application of Self-learning Algorithm Based on Parameter Optimization in Control of Two Wheeled Robot

To overcome the disadvantages of the existing control algorithm of two-wheeled robot( TWR) , the adaptive controller was designed by using the self-learning parameter optimization algorithm. The controller has a simple structure and does not need to rely on the accurate mathematical model, and the optimal control parameters can be obtained through learning and training many times. The controller was applied to the balance control of the two-wheeled robot, and was compared with the linear quadratic regulator ( LQR ) optimal controller. The simulation results verify the correctness and validity of the algorithm, highlights the strong robustness and bionics habits. The controller was applied to the physical system of two-wheeled robot, and achieved good control effect.