基于事件触发与分阶段误差约束的时变干扰观测与饱和补偿自适应控制

Adaptive control with time-varying disturbance observation and saturation compensation based on event-triggered mechanism and phased error constraints

摘要

针对控制系统在未知非线性动态、时变干扰、输入饱和及高斯噪声下的鲁棒控制问题，提出一种深度融合事件触发机制与动态耦合策略的自适应滑模控制方法。首先，采用动态面控制（DSC）技术，结合径向基神经网络（RBFNN）逼近系统未知动态，设计分阶段误差约束策略以实现收敛速度与稳态精度的动态协同优化；其次，引入时变干扰观测器在线估计波浪干扰等扰动，通过构建辅助动态系统对非对称输入饱和进行补偿；再次，结合事件触发机制，通过自适应动态阈值调控降低控制器更新频次，减少计算资源消耗。基于Lyapunov稳定性理论，证明闭环系统所有信号半全局一致最终有界，且跟踪误差可收敛至预设邻域内。仿真结果表明，该方法显著提升了系统抗干扰能力与控制效率，为相关控制理论在工程实践中的应用提供了理论支撑。

Abstract

Aiming at the robust control problem of control systems under unknown nonlinear dynamics, time-varying disturbances, input saturation and Gaussian noise, an adaptive sliding mode control method deeply integrating event-triggered mechanism and dynamic coupling strategy was proposed. Firstly, the dynamic surface control (DSC) technique combined with radial basis function neural network (RBFNN) was adopted to approximate the unknown dynamics of the system, and a staged error constraint strategy was designed to achieve dynamic collaborative optimization of convergence speed and steady-state accuracy. Secondly, a time-varying disturbance observer was introduced to estimate disturbances such as wave disturbances online, and an intelligent smoothing processing mechanism driven by Gaussian error function was used to handle the problem of asymmetric input saturation. Furthermore, by combining the event-triggered mechanism, the adaptive dynamic threshold control was used to reduce the control update frequency and decrease the consumption of computing resources. Based on the Lyapunov stability theory, it is proved that all signals of the closed-loop system are semi-globally uniformly ultimately bounded, and the tracking error can converge to a preset neighborhood. Simulation results show that the proposed method significantly improves the system’s anti-interference ability and control efficiency, providing theoretical support for the application of related control theories in engineering practice.

关键词

自适应控制 / 事件触发机制 / 分阶段误差约束 / 时变干扰 / 动态面控制（DSC） / 径向基神经网络（RBFNN） / 非对称饱和补偿

Key words

adaptive control / event-triggered mechanism / phased error constraints / time-varying disturbance / dynamic surface control (DSC) / radial basis function neural network (RBFNN) / asymmetric saturation compensation

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基金资助

山东省高等学校“青创团队计划”(2022KJ210)

山东省重点研发计划（重大科技创新项目）(2024CXGC010804)