Intelligent model predictive control of flexible link robotic manipulators.

摘要

This thesis develops and evaluates an intelligent model predictive control (IMPC) strategy for motion control of a flexible link robotic manipulator through analysis, computer simulation, and physical experimentation.; The developed IMPC is based on a two-level hierarchical control architecture. This control structure is used to combine the advantages of the conventional model predictive control (MPC) with those of knowledge-based soft control techniques. The upper level of the structure is a fuzzy-rule based intelligent decision-making system. The lower level consists of two modules: a real-time system identification module (which adjusts the model parameters and accommodates payload changes of the manipulator), and a model predictive control (MPC) module (which develops control inputs based on the linear model generated by the system identification module). The upper-level intelligent fuzzy rule-based tuner interacts with the lower level modules. Based on the desired system performance, the state feedback signals, and the knowledge base, the upper-level fuzzy tuner automatically adjusts the tuning parameters of the MPC controller. It is also able to adjust the model structure of the system-identification module, if necessary, to accommodate large model errors, and will increase the robustness of the controller.; An explicit, complete, and accurate nonlinear dynamic model of the system is developed using the assumed mode method. More realistic boundary conditions, which represent the balance of moments and shear forces separately, at the ends of each link, are used for the dynamic model development of the system. A computationally efficient multi-stage MPC algorithm with guaranteed stability is developed as well. This algorithm is used by the MPC module to enable real-time implementation of the overall scheme. A fuzzy knowledge base for tuning the MPC controller is developed based on analysis, computer simulations and experimental testing of the prototype flexible-link manipulator system (FLMS). A fuzzy tuner is designed based on this fuzzy knowledge base. The performance of the developed IMPC scheme is evaluated using computer simulations and experiments of the prototype FLMS. The results show that IMPC can more effectively control the motion of a flexible link robot manipulator when compared with conventional MPC.