Servo System Control Using SPC and ASPC This paper designs a Subspace Predictive Controller (SPC) and an Adaptive SPC (ASPC) to control the angular position of a servomechanism. The results are obtained experimentally using Lab Volt servo kit. The performance of proposed controllers is compared to standard Model Predictive Control (MPC). As SPC and ASPC are based on nonparametric Subspace Identification (SID), they avoid the need to design an explicit state observer and hence they ease the process of tuning the controller. The design ASPC aims to compensate the mismatch between the real system and the nonparametric model by updating the controller with new closed loop inputoutput measurement.

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.