腰部外骨骼机器人线性自抗扰控制器参数优化

针对腰部外骨骼机器人线性自抗扰控制器参数难以调整的问题，本文提出一种基于天牛须搜索的改进粒子群算法(PSO)。建立腰部外骨骼机器人模型，采用线性自抗扰控制器，进一步引入改进的PSO对其进行参数优化。该算法通过混沌初始化种群，提高粒子执行效率；采用非线性策略调整惯性因子和学习因子，加强粒子的搜索能力；引入天牛须搜索算法与PSO结合，并采用自适应权重，使得粒子可对周边环境进行较好地判断，避免粒子陷入局部最优。分别通过6个测试函数和建立系统评价指标进行仿真实验，结果表明所提出的算法有更好的收敛精度，优化后的控制器具有更好的控制性能。

Parameters Optimization of the Linear Active Disturbance Rejection Controller for Lumbar Exoskeleton Robot

Aiming at the difficulty of tuning the parameters of the linear active disturbance rejection controller for the lumbar exoskeleton robot， an improved particle swarm optimization algorithm (PSO) based on the beetle antennae search is proposed. First， this study establishes a lumbar exoskeleton robot model， then designs the linear active disturbance rejection controller， and finally introduces the improved PSO to optimize its parameters. The algorithm initializes the population through chaos to improve the execution efficiency of particles and adopts nonlinear strategies to adjust inertia factors and learning factors to strengthen the search ability of particles. The long beetle search algorithm combined with PSO and using the adaptive weighting factor is introduced so that particles can judge the surrounding environment and avoid particles from falling into a local optimum. The simulation experiments are performed through six test functions and the establishment of system evaluation indicators. The results show that the proposed algorithm has good convergence accuracy， and the optimized controller has good control performance.