Optimal control of multi-axis robotic system using Particle Swarm Optimization

M. Aftab, Momin Shadab, P. Santosh, Parwaiz Sadaf, Javed Taili
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引用次数: 2

Abstract

Optimization is a mathematical technique that concerns the finding of maxima or minima of functions in some feasible region. There is no business or industry which is not involved in solving optimization problems. A variety of optimization techniques compete for the best solution. Particle Swarm Optimization (PSO) is a relatively new, modern and powerful method of optimization that has been empirically shown to perform well on many of these optimization problems. This paper presents an application of `Particle Swarm Optimization (PSO)' for tuning the parameters of PID controller to achieve system stability. Here the system is two axis robotic arm. PSO algorithm will search for best possible PID controller parameters within its three dimension space of Kp, Ki, Kd. Optimization of peak overshoot (Mp) and settling time (ts) is achieved by using a multi-purpose performance criterion (MPPC). The system is stabilized by minimizing the performance criterion. This paper presents simulation results using MATLAB Simulink to demonstrate tuning of PID controller using PSO along with MPPC gives minimum Mp and ts.
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基于粒子群算法的多轴机器人系统最优控制
优化是一种数学技术,涉及在可行区域内寻找函数的最大值或最小值。没有哪个企业或行业不涉及解决优化问题。各种优化技术相互竞争以获得最佳解决方案。粒子群优化(PSO)是一种相对较新的、现代的、强大的优化方法,已被经验证明在许多此类优化问题上表现良好。本文提出了一种应用粒子群算法(PSO)对PID控制器的参数进行整定以达到系统稳定的方法。这里的系统是两轴机械臂。粒子群算法将在Kp, Ki, Kd的三维空间中搜索可能的最佳PID控制器参数。利用多用途性能准则(MPPC)实现了峰值超调量(Mp)和稳定时间(ts)的优化。通过最小化性能标准来稳定系统。本文给出了MATLAB Simulink的仿真结果,证明了使用PSO和MPPC对PID控制器进行整定可以得到最小的Mp和ts。
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