BP neural network-based explicit MPC of nonlinear boiler-turbine systems

IF 3.3 2区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Jing Li , Defeng He , Xiuli Wang , Yu Kang
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引用次数: 0

Abstract

This paper proposes a new explicit model predictive control (EMPC) scheme of constrained nonlinear systems with unknown but bounded input disturbances. Firstly, support vector machine is used to learn internal and external approximations of the feasible state space of the EMPC. Then, the control surface on the feasibility of EMPC is constructed by a backpropagation neural network (BPNN). The finite horizon optimal control solution to the EMPC can be computed from real-time data by training the control surface. The proposed EMPC is also suitable for nonlinear systems with higher dimensions in terms of reducing online computational burdens and enhancing control accuracy. Next, the Hoeffding's Inequality is used to ensure that the EMPC law computed by the BPNN approximation complies with the specified range with a high level of confidence. Moreover, some conditions are obtained to guarantee the stability and recursive feasibility of the EMPC with probabilistic assurances. Finally, a 160 MW boiler-turbine system is employed to verify the effectiveness and applications of the proposed method.
基于BP神经网络的非线性锅炉-汽轮机系统显式MPC
针对输入扰动未知但有界的约束非线性系统,提出了一种新的显式模型预测控制方案。首先,利用支持向量机学习EMPC可行状态空间的内外近似;然后,利用反向传播神经网络(BPNN)构造了EMPC可行性的控制面。通过对控制面进行训练,可以从实时数据中计算出EMPC的有限水平最优控制解。该方法在减少在线计算量和提高控制精度方面也适用于高维非线性系统。其次,利用Hoeffding不等式,保证由BPNN近似计算的EMPC律符合指定的范围,并具有较高的置信度。在此基础上,给出了系统稳定性和递推可行性的概率保证条件。最后,以一台160 MW的锅炉-汽轮机系统为例,验证了所提方法的有效性和应用。
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来源期刊
Journal of Process Control
Journal of Process Control 工程技术-工程:化工
CiteScore
7.00
自引率
11.90%
发文量
159
审稿时长
74 days
期刊介绍: This international journal covers the application of control theory, operations research, computer science and engineering principles to the solution of process control problems. In addition to the traditional chemical processing and manufacturing applications, the scope of process control problems involves a wide range of applications that includes energy processes, nano-technology, systems biology, bio-medical engineering, pharmaceutical processing technology, energy storage and conversion, smart grid, and data analytics among others. Papers on the theory in these areas will also be accepted provided the theoretical contribution is aimed at the application and the development of process control techniques. Topics covered include: • Control applications• Process monitoring• Plant-wide control• Process control systems• Control techniques and algorithms• Process modelling and simulation• Design methods Advanced design methods exclude well established and widely studied traditional design techniques such as PID tuning and its many variants. Applications in fields such as control of automotive engines, machinery and robotics are not deemed suitable unless a clear motivation for the relevance to process control is provided.
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