In this paper, a PSO-based model approximation technique was investigated for use in the control of a three degrees of freedom PUMA-type robot arm via numerical simulations. Particle swarm optimization (PSO) is an attractive, efficient, and simple tool for model improvement. It is shown that even very primitive, fast, and simple versions of evolutionary computation-based methods can produce considerable improvement in their operation. However, any amendment of the model can improve the controller’s operation by affecting its range and speed of convergence. They neither identify nor improve the parameters of the available model. Fixed point iteration-based adaptive controllers can work without the exact model form but immediately yield precise trajectory tracking. Lyapunov function-based classic methods, which assume exact analytical model forms, guarantee asymptotic stability by cautious and slow parameter tuning. The consequences of these effects can be compensated by adaptive techniques and by the improvement of the available model. The analytical form of the available model often contains only approximate parameters and can be physically incomplete. Model-based controllers suffer from the effects of modeling imprecisions.
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