5. Conclusions
In this paper, the ABFSM has successfully applied to the motion control of the auto-warehousing system. In the AFIC, the fuzzy logic inference is introduced to provide a means to approximate the ideal controller for the crane system when its dynamics are complex to be comprehended. To alleviate the burden of fuzzy rule construction, only the information from sliding surface is exploited as the input such that the conciseness and translucency of the control system can be upgraded. On the other hand, the BCC aims to compensate the approximation error of the AFIC. To achieve the efficient motion control, a distance–speed reference curve for each direction is designed before the beginning of control process according to the specifications of the crane system. Thus, a powerful yet easy-to-use methodology is given for online gain determination when optimal or sup-optimal fuzzy system is difficult to obtain. Two main contributions of the paper are 1) the stability of closed-loop adaptive fuzzy controller can be guaranteed by the means of Lyapunov function, and 2) using the concept of sliding-mode control and B-spline functions, the proposed ABFSM possesses the merit of being easily undertaken by a microprocessor. To validate the performance of the proposed approach, the ABFSM is applied to the auto-warehousing crane motion control under various loading conditions for x, y, and z directions, respectively. Through the simulation results and illustrations, the feasibility and the robustness of the proposed ABFSM are verified.
