模型預測控制是一種基於模型的先進控制策略，它通過反復優化一個有限時域内的約束優化問題實時求解最優控制信號。作爲一種有效的多變量控制方法，模型預測控制在過程控制、機械人、經濟學等方面取得了巨大的成功。模型預測控制研究與發展的一個關鍵問題在於如何實現高性能非綫性和魯棒預測控制算法。實時優化是一項具有挑戰性的任務，尤其在優化問題時非凸優化的情況下，實時優化變得更爲艱巨。在模型預測控制取得發展的同時，以建立仿腦計算模型為目標的神經網絡研究也取得一些重要突破，尤其是在系統辨識和實時優化方面。神經網絡為解決模型預測控制面臨的瓶頸問題提供了有力的工具。 / 本篇論文重點討論基於神經動力學方法的模型預測控制的設計與分析。論文的主要目標在於設計高性能神經動力學算法進而提高模型預測控制的最優性與計算效率。論文包括兩大部分。第一部分討論如何在不需要求解非凸優化的前提下解決非綫性和魯棒模型預測控制。主要的解決方案是將非相信模型分解為帶有未知項的仿射模型，或將非綫性模型轉換為綫性變參數系統。仿射模型中的未知項通過極限學習機進行建模和數值補償。針對系統中的不塙定干擾，利用極小極大算法和擾動不變集方法獲取控制系統魯棒性。儅需要考慮多個評價指標是，採用目標規劃設計多目標優化算法。論文第一部分提出的設計方法可以將非綫性和魯邦模型預測控制設計為凸優化問題，進而採用神經動力學優化的方法進行實時求解。論文的第二部分設計了針對非凸優化的多神經網絡算法，並在此基礎上提出了模型預測控制算法。多神經網絡算法模型人類頭腦風暴的過程，同時應用多個神經網絡相互協作地進行全局搜索。神經網絡的動態方程指導其進行局部精確搜索，神經網絡之間的信息交換指導全局搜索。實驗結果表明該算法可以高效地獲得非凸優化的全局最優解。基於多神經網絡優化的的模型預測控制算法是一種創新性的高性能控制方法。論文的最後討論了應用模型預測控制解決海洋航行器的運動控制問題。 / Model predictive control (MPC) is an advanced model-based control strategy that generates control signals in real time by optimizing an objective function iteratively over a finite moving prediction horizon, subject to system constraints. As a very effective multivariable control technology, MPC has achieved enormous success in process industries, robotics, and economics. A major challenge of the MPC research and development lies in the realization of high-performance nonlinear and robust MPC algorithms. MPC requires to perform real time dynamic optimization, which is extremely demanding in terms of solution optimality and computational efficiency. The difficulty is significantly amplified when the optimization problem is nonconvex. / In parallel to the development of MPC, research on neural networks has made significant progress, aiming at building brain-like models for modeling complex systems and computing optimal solutions. It is envisioned that the advances in neural network research will play a more important role in the MPC synthesis. This thesis is concentrated on analysis and design of neurodynamic approaches to nonlinear and robust MPC. The primary objective is to improve solution optimality by developing highly efficient neurodynamic optimization methods. / The thesis is comprised of two coherent parts under a unified framework. The first part consists of several neurodynamics-based MPC approaches, aiming at solving nonlinear and robust MPC problems without confronting non-convexity. The nonlinear models are decomposed to input affine models with unknown terms, or transformed to linear parameter varying systems. The unknown terms are learned by using extreme learning machines via supervised learning. Minimax method and disturbance invariant tube method are used to achieve robustness against uncertainties. When multiobjective MPC is considered, goal programming technique is used to deal with multiple objectives. The presented techniques enable MPC to be reformulated as convex programs. Neurodynamic models with global convergence, guaranteed optimality, and low complexity are customized and applied for solving the convex programs in real time. Simulation results are presented to substantiate the effectiveness and to demonstrate the characteristics of proposed approaches. The second part consists of collective neurodynamic optimization approaches, aiming at directly solving the constrained nonconvex optimization problems in MPC. Multiple recurrent neural networks are exploited in framework of particle swarm optimization by emulating the paradigm of brainstorming. Each individual neural network carries out precise constrained local search, and the information exchange among neural networks guides the improvement of the solution quality. Implementation results on benchmark problems are included to show the superiority of the collective neurodynamic optimization approaches. The essence of the collective neurodynamic optimization lies in its global search capability and real time computational efficiency. By using collective neurodynamic optimization, high-performance nonlinear MPC methods can be realized. Finally, the thesis discusses applications of MPC on the motion control of marine vehicles. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Yan, Zheng. / Thesis (Ph.D.) Chinese University of Hong Kong, 2014. / Includes bibliographical references (leaves 186-203). / Abstracts also in Chinese.
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_1077638 |
| Date | January 2014 |
| Contributors | Yan, Zheng , active 2014 (author.), Wang, Jun (Jun Li Jim) (thesis advisor.), Chinese University of Hong Kong Graduate School. Division of Mechanical and Automation Engineering, (degree granting institution.) |
| Source Sets | The Chinese University of Hong Kong |
| Language | English, Chinese |
| Detected Language | English |
| Type | Text, bibliography, text |
| Format | electronic resource, electronic resource, remote, 1 online resource (xi, 207 leaves) : illustrations (some color), computer, online resource |
| Rights | Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
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