Neural network-based non-linear adaptive controller design for a class of bilinear system
Neural network-based non-linear adaptive controller design for a class of bilinear system
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DOI：
10.1049/ccs.2019.0015
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发表时间：
2020-02
期刊：
影响因子：
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通讯作者：
S. Bamgbose;Xiangfang Li;Lijun Qian
中科院分区：
文献类型：
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作者：
S. Bamgbose;Xiangfang Li;Lijun Qian
针对多输入多输出状态控制齐次双线性系统(BLS)平衡点的全局稳定性问题，提出了一种基于神经网络的非线性自适应控制策略。虽然这类非线性系统既不是分段线性化的，也不是反馈线性化的，但条件可镇定控制系统设计可以用来产生多个状态转移和相应的控制增益。收集的数据被用来训练神经网络以获得最优的增益估计器。然后将最优增益估值器集成到实时控制系统的运行中，自适应地计算控制增益，确保控制器能够连续调整以适应系统行为的变化。通过一个算例说明了所提出的设计克服了传统控制器对所研究的一类BLS的稳定性限制。在此基础上，讨论了传统控制和学习系统集成的实用性，并对所提出的方案进行了稳定性分析。
This study presents a novel neural network (NN)-based non-linear adaptive control strategy for the global stability of multi-input–multi-output state-control homogeneous bilinear system (BLS) at the equilibrium position. Although this class of non-linear system is neither piecewise nor feedback linearisable, conditionally stabilisable control system design can be utilised to generate multiple state transitions and corresponding control gains. The collected data was used to train a NN to obtain an optimal gain estimator. Then the optimal gain estimator was integrated into real-time control system operation to adaptively compute control gains, ensuring that the controller is continuously adjustable to changing behaviour of the system. The proposed design was shown, through an illustrative example, to overcome the stability limitations of traditional controllers for the investigated class of BLS. Furthermore, discussions about the utility of the traditional control and learning system integration, as well as stability analysis of the proposed scheme were presented.