针对样机建立简化的下肢外骨骼模型，应用D-H参数法进行动力学分析，并通过实验测得关节角度后进行拟合作为控制器输入。为了解决机器人的轨迹跟踪问题，利用传统PID控制拥有较好的跟随效果，但存在响应和寻参速度慢等问题；结合粒子群算法后虽然寻参速度加快，仍出现收敛精度低以及易陷入局部最优解的问题，因此设计了一种基于混沌映射型改进粒子群算法的PID控制。结果表明，改进后随机性增强，寻参速度加快，跟踪误差更小；并采用Simscape将关节角度进行可视化仿真，结合实验多方面验证控制效果。

A simplified lower-limb exoskeleton model was established for the prototype, and the D-H parameter method was used to perform dynamic analysis. Joint angles were measured experimentally and used as inputs for the controller. To address the robot's trajectory tracking problem, traditional PID control was employed, showing good tracking performance but slow response and parameter tuning speed. Although PSO(particle swarm optimization) accelerated the parameter tuning, issues with low convergence accuracy and local optimum traps persisted. Therefore, a PID control based on a chaotic-mapping improved PSO algorithm was designed. The results show that the randomness was enhanced, the parameter tuning speed was increased, and the tracking error was reduced. Simscape was used for visual simulation of joint angles, and the control performance was further validated through various experiments.