Auto-tuning of PID Controllers for Robotic Manipulators Using PSO and MOPSO

Abstract

This work proposes two approaches to automatic tuning of PID position controllers based on different global optimization strategies. The chosen optimization algorithms are PSO and MOPSO, i. e. the problem is handled as a single objective problem in the first implementation and as a multiobjective problem in the second one. The auto-tuning is performed without assuming any previous knowledge of the robot dynamics. The objective functions are evaluated depending on real movements of the robot. Therefore, constraints guaranteeing safe and stable robot motion are necessary, namely: a maximum joint torque constraint, a maximum position error constraint and an oscillation constraint. Because of the practical nature of the problem in hand, constraints must be observed online. This requires adaptation of the optimization algorithm for reliable observance of the constraints without affecting the convergence rate of the objective function. Finally, experimental results of a 3-DOF robot for different trajectories and with different settings show the validity of the two approaches and demonstrate the advantages and disadvantages of every method.

Developing a New Robust Swarm-Based Algorithm for Robot Analysis

“…Reference [8] proposed the novel PID based strategy PSO (PBSPSO) algorithm based on the PID controller which was found to improve convergence while reducing stagnation of the PSO algorithm. A new variant of PSO with cross-over operation (PSOCO) was introduced by [9] which improved divergent search abilities of the PSO while avoiding stagnation by implementing a new learning model for the particles' velocity formula and two cross-over operations, while [10] used PSO and multi-objective PSO to develop a two-stage auto-tuning technique for PID controllers. Automation of logistics, maintenance/support, storage, and others have led to rapid improvements in the vehicle routing problem (VRP) algorithm.…”

“…If the actual values of T read from sensors attached to the robot's end-effector is given in A, then the fitness function (Fitness) would be as described in Equations (10) and (11). Where k [1, 2, .…”

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“…Generally, the two most popular approaches to obtain PID parameters are classical and optimization. 11…”

“…Generally, the two most popular approaches to obtain PID parameters are classical and optimization. 11 Control engineers frequently utilize analytical and empirical methodologies which are part of classical approaches available in literature. Ziegler-Nichols (ZN) and Cohen-Coon are two examples of the tuning techniques that are often published in this research area.…”