Auto-Tuning of GPC weights based on Particle Swarm Optimization applied to a Manipulator End-Effector Trajectory Tracking

Felipe J. S. Vasconcelos, Iury de Amorim Gaspar Filgueiras, W. Correia
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引用次数: 1

Abstract

Manipulators are becoming more and more common to perform many tasks, whose accomplishment is often related to the applied control law. Regarding to this issue, control theory is helpful as the proper controller choice may turn the manipulator into a handy tool. Within this context this work presents an automatic tuning method for the Generalized Predictive Controller (GPC) in order to tracking the trajectory of a manipulator end-effector. The strategy employs Particle Swarm Optimization (PSO) to properly determine GPC cost function weights at each iteration that lead to zero error tracking. The proposed controller is compared to classical approaches for three different trajectories with results showing a better performance and tracking accuracy for the proposed approach.
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基于粒子群优化的GPC权值自整定在机械臂末端执行器轨迹跟踪中的应用
机械手越来越普遍地执行许多任务,其完成往往与所应用的控制律有关。对于这个问题,控制理论是有帮助的,因为适当的控制器选择可以使机械手成为一个方便的工具。在此背景下,本文提出了一种广义预测控制器(GPC)的自动整定方法,以跟踪机械臂末端执行器的轨迹。该策略采用粒子群算法(PSO)在每次迭代中正确确定GPC代价函数的权重,从而实现零误差跟踪。将所提出的控制器与经典方法进行了三种不同轨迹的比较,结果表明所提出的方法具有更好的性能和跟踪精度。
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