控制理论(社会学)
稳健性(进化)
自抗扰控制
计算机科学
滑模控制
非线性系统
国家观察员
微分器
鲁棒控制
跟踪误差
沉降时间
分段
超调(微波通信)
内部模型
控制工程
工程类
噪音(视频)
控制系统
MATLAB语言
倒立摆
粒子群优化
输入整形
控制器(灌溉)
理论(学习稳定性)
作者
Shengze Yang,Junfeng Ma,Dayi Zhao,Chenxiao Li,Liyong Fang
出处
期刊:Sensors
[Multidisciplinary Digital Publishing Institute]
日期:2025-10-03
卷期号:25 (19): 6109-6109
被引量:1
摘要
To address the challenges of insufficient response speed and robustness in optical attitude control systems under highly dynamic disturbances and internal uncertainties, a composite control strategy is proposed in this study. By integrating the proposed piecewise sliding control (P-SMC) with the improved active disturbance rejection control (ADRC), this strategy achieves complementary performance, which can not only suppress the disturbance but also converge to a bounded region fast. Under highly dynamic disturbances, the improved extended state observer (ESO) based on the EKF achieves rapid response with amplified state observations, and the Nonlinear State Error Feedback (NLSEF) generates a compensation signal to actively reject disturbances. Simultaneously, the robust sliding mode control (SMC) suppresses the effects of system nonlinearity and uncertainty. To address chattering and overshoot of the conventional SMC, this study proposes a novel P-SMC law which applies distinct reaching functions across different error bands. Furthermore, the key parameters of the composite scheme are globally optimized using the particle swarm optimization (PSO) algorithm to achieve Pareto-optimal trade-offs between tracking accuracy and disturbance rejection robustness. Finally, MATLAB simulation experiments validate the effectiveness of the proposed strategy under diverse representative disturbances. The results demonstrate improved performance in terms of response speed, overshoot, settling time and control input signals smoothness compared to conventional control algorithms (ADRC, C-ADRC, T-SMC-ADRC). The proposed strategy enhances the stability and robustness of optical attitude control system against internal uncertainties of system and sensor measurement noise. It achieves bounded-error steady-state tracking against random multi-source disturbances while preserving high real-time responsiveness and efficiency.
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