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Model Predictive Control Part 5

Tham khảo tài liệu 'model predictive control part 5', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | A new kind of nonlinear model predictive control algorithm enhanced by control lyapunov functions 73 It is not difficult to show that H GPMN satisfies inequality 24 of Theorem I thus it can be used as w1 z in controller 25 to bring the advantages of H GPMN controller to the robust controller in section 4.1. 4.3 H GPMN-ENMPC As far as the external disturbances are concerned nominal model based NMPC where the prediction is made through a nominal certain system model is an often used strategy in reality. And the formulation of it is very similar to non-robust NMPC so dose the GPMN-ENMPC. RGPMN I Fig. 4. Structure of new designed RNRHC controller However for disturbed nonlinear system like Eq. 23 GPMN-ENMPC algorithm can hardly be used in real applications due to weak robustness. Thus in this subsection we will combine it to the robust controller from sub-section 4.1 and sub-section 4.2 to overcome the drawbacks originated from both GPMN-ENMPC algorithm and the robust controller 25 and 35 . The structure of the new parameterized H GPMN-ENMPC algorithm based on controller 25 and 35 is as Fig. 4. Eq. 36 is the new designed H GPMN-ENMPC algorithm. Compared to Eq. 14 it is easy to find out that the control input in the H GPMN-ENMPC algorithm has a pre-defined structure given in section 4.1 and 4.2. 74 Model Predictive Control .H 1 u u x 0 A 0 arg min J x u ueU A t T J x u J l x v u r dr s.t. x f x g x u u t uH x 0 36 5. Practical Considering Both GPMN-ENMPC algorithm and H GPMN-ENMPC algorithm can be divided into two processes including the implementation process and the optimization process as Fig.5. Optimized parameter 9 Implementation process Optimization process Computing control input based on Ho GPMN scheme Computing the optimal parameter 9 by solving an optimal control problem Fig. 5. The process of HJGPMN-ENMPC The implementation process and the optimization process in Fig. 5 are independent. In implementation process the H GPMN scheme is used to ensure the closed