![]() ![]() Simulation results have demonstrated the applicability of the design method. The system consists of a setpoint generator, a feedback controller, and a time-index LFFC that can learn on-line. A MACS-based LFFC system has been designed and implemented for the simulated plant. A case study using a simulated linear motor in the presence of nonlinear cogging and friction force as well as mass variations is used to illustrate the proposed method. To provide real-time features for the MACS-based LFFC system, the open robot control software (OROCOS) has been employed as development and runtime environment. A second-order B-spline neural network (BSN) is used as a function approximator for LFFC whose input-output mapping can be adapted during control and is intended to become equal to the inverse model of the plant. As a result, a MACS-based LFFC design method has been developed. The major novelty of this work is that the feedback control part is realized in a real-time periodic MACS, while the LFFC algorithm is done on-line, asynchronously, and in a separate non-real-time aperiodic MACS. Specifically, learning feedforward control (LFFC) is implemented as a pattern for incorporation in MACS. ![]() The study has aimed at integrating learning control into MACS. Using the advantages of MACS-based design approaches, a novel solution for advanced control of mechatronic systems has been developed in this paper. Multiagent control system (MACS) has become a promising solution for solving complex control problems. ![]()
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