Optimal Tuning of Cascade Controllers for Feed Drive Systems using Particle Swarm Optimization Cascade control configurations are one of the widely used control solutions for improving dynamic response of the feed drive systems in the manufacturing industry. However, optimal tuning of cascade controllers in presence of hard nonlinearities such as backlash and friction is still a complex and timeconsuming task. This paper presents a computational procedure for tuning PPI cascade controller by using particle swarm optimization (PSO) for a feed drive system of machine tools in the presence of friction and backlash. The minimizing of the maximum position error during the reversal of the axes is used as an objective function for optimization. The performance of the PSO method is compared in simulations and realtime experiments with the fine tune (FT) method, which is one of the standard methods applied in industry. Both, simulation and realtime experimental studies carried out on a test platform with 8070 Fagor controller show a remarkable improvement in the performance of the cascade control system using the proposed PSObased strategy.

AutoTuning of GPC weights based on Particle Swarm Optimization applied to a Manipulator EndEffector Trajectory Tracking 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 endeffector. 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.