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引用次数: 0
摘要
本研究探讨了在未知测量灵敏度和非恒定控制增益条件下,非线性互联大规模系统(LSS)的分散自适应事件触发神经网络(NN)控制问题。由于未知测量灵敏度的影响,无法直接利用 LSS 的真实状态。为了克服这一困难,我们开发了一种有效的自适应反馈控制方案。随后,利用 NN 解决了非线性项和未知非定常控制增益问题。开发了一种改进的一阶补偿系统,以提高饱和非线性情况下的控制性能。此外,基于饱和控制器和测量误差,还开发了一种重要的动态事件触发控制(DETC)协议,减少了控制器的更新次数。根据 Lyapunov 稳定性理论,所提出的基于 DETC 的分散自适应协议证明了所有信号都是半全局均匀最终有界的。仿真实例说明了所提出的控制协议的有效性。
Event-Based Adaptive Neural Network Control for Large-Scale Systems With Nonconstant Control Gains and Unknown Measurement Sensitivity
This study explored the issue of decentralized adaptive event-triggered neural network (NN) control for nonlinear interconnected large-scale systems (LSSs) subjected to unknown measurement sensitivity and nonconstant control gains. Due to the impact of unknown measurement sensitivity, the real states of LSSs cannot be directly utilized. To overcome this difficulty, an effective adaptive feedback control scheme was developed. Subsequently, NNs were exploited to address the nonlinear terms and unknown nonconstant control gains. A modified first-order compensation system was developed to enhance the control performance in the presence of saturation nonlinearity. Furthermore, a significant dynamic event-triggered control (DETC) protocol was developed based on the saturation controller and measurement error, which reduced the number of controller updates. According to the Lyapunov stability theory, the proposed DETC-based decentralized adaptive protocol demonstrated that all signals were semiglobally uniformly ultimately bounded. The simulation examples illustrate the validity of the presented control protocol.
期刊介绍:
The IEEE Transactions on Systems, Man, and Cybernetics: Systems encompasses the fields of systems engineering, covering issue formulation, analysis, and modeling throughout the systems engineering lifecycle phases. It addresses decision-making, issue interpretation, systems management, processes, and various methods such as optimization, modeling, and simulation in the development and deployment of large systems.