分布式模型預(yù)測(cè)控制算法相關(guān)研究
[Abstract]:With the continuous development of science and technology, modern industrial processes show the characteristics of complex structure, large scale, strong coupling of energy and materials between subsystems, and so on. Distributed model predictive control (Distributed Model Predictive Control, DMPC) is an effective method to solve large-scale system control problems. The advantages of DMPC are: (1) reducing the computational burden of each subsystem; (2) improving the scalability of the system under multiple controllers; (3) strong fault-tolerant ability of the system. The main design goal of DMPC algorithm is to achieve the best control performance under the simple system communication mode and the minimum communication burden. At the same time, the convergence of the algorithm and the stability of the system are guaranteed. Aiming at the related problems of DMPC algorithm and controller design, this paper focuses on the design of DMPC fast algorithm, the disassembly of large system structure and the performance evaluation of MPC control system. The results are as follows: 1. An optimal structure decomposition method based on genetic algorithm (GA) is proposed to solve the problem of large scale system disassembly in distributed predictive control (DPC). The method consists of two new disassembly indexes corresponding to two stages of decomposition, including input grouping (Input Clustering Decomposition, ICD) and input and output pairing (Input-Output Pairing Decomposition IOPD). ICD can be used to eliminate input coupling between subsystems). At the same time, it can balance the computational burden between subsystems. IOPD is to find the right pairing between input and output. The optimization problem corresponding to ICD and IOPD is solved by GA. 2. To solve the problem of DMPC distributed algorithm design, a DMPC algorithm based on SVD decomposition is proposed to effectively reduce the communication burden between subsystems. In this method, the centralized MPC online quadratic optimization problem is transferred to conjugate space without constraint. Each subsystem can solve its own optimal control input independently and in parallel, and the global optimal input can be generated by merging the solutions of each subsystem. This method can also be extended to the constrained case. The obtained unconstrained solution is checked in conjugate space in parallel, and then the solution corresponding to the small singular value is removed according to the size of the singular value. Finally, the optimal solution with constraints. 3. A fast DMPC algorithm based on efficient set method (active-set) is proposed to solve DMPC online optimization problem. The algorithm solves a constrained distributed efficient set quadratic programming problem by using offline inverse of Hessian matrix. According to the size of the unconstrained solution, a two-mode optimization strategy is proposed to speed up the on-line computation. The algorithm can stop the iteration ahead of time, ensure the stability of the system, and be easy to implement. Finally, a warm start strategy using the DMPC optimal value at the previous time can further accelerate the iterative convergence rate of the algorithm. A distributed model prediction algorithm is proposed for the design of series structure DMPC algorithm. The algorithm utilizes the characteristics that the output of each subsystem of the series structure is only related to the upper runaway and its own system input. The traditional iterative DMPC algorithm is improved to obtain a non-iterative hierarchical DMPC algorithm. 5. Aiming at the problem of MPC performance evaluation and improvement, a method to improve the economic performance of MPC control system on line is proposed. According to the data collected online, the iterative learning method is used to continuously adjust the parameters of the MPC controller on line, so as to improve the economic performance of the MPC controller on line. This paper also discusses the possibility of extending this method to distributed MPC systems.
【學(xué)位授予單位】:浙江大學(xué)
【學(xué)位級(jí)別】:博士
【學(xué)位授予年份】:2015
【分類(lèi)號(hào)】:TP13
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