Application of cerebellar model articulation controller network to learning optimization control in conveyor-serviced production station
小脑模型关节控制器网络在传送带给料生产加工站学习优化控制中的应用

This paper is concerned with the optimization of the look-ahead distance for a conveyor-serviced production station(CSPS) to improve the efficiency of operations. The optimal control process for CSPS is modeled by a semi-Markov decision process(SMDP). Since the standard Q-learning is difficult to deal with the continuous variable optimal look-ahead control problem of CSPS directly, Cerebellar Model Articulation Controller(CMAC) for Q-values function approximation is combined with the online learning technology, and some online Q-learning and model-free online policy iteration algorithms are provided. Simulation results show that the proposed algorithms improve the learning speed and the precision of optimization.