Department of Electrical Engineering, National University of Computer and Emerging Sciences, Lahore, Pakistan.
School of Computer Science, Faculty of Science and Engineering, University of Hull, Hull, United Kingdom.
PLoS One. 2023 Mar 16;18(3):e0283079. doi: 10.1371/journal.pone.0283079. eCollection 2023.
This article presents flexible online adaptation strategies for the performance-index weights to constitute a variable structure Linear-Quadratic-Integral (LQI) controller for an under-actuated rotary pendulum system. The proposed control procedure undertakes to improve the controller's adaptability, allowing it to flexibly manipulate the control stiffness which aids in efficiently rejecting the bounded exogenous disturbances while preserving the system's closed-loop stability and economizing the overall control energy expenditure. The proposed scheme is realized by augmenting the ubiquitous LQI controller with an innovative online weight adaptation law that adaptively modulates the state-weighting factors of the internal performance index. The weight adaptation law is formulated as a pre-calibrated function of dissipative terms, anti-dissipative terms, and model-reference tracking terms to achieve the desired flexibility in the controller design. The adjusted state weighting factors are used by the Riccati equation to yield the time-varying state-compensator gains.
本文提出了一种灵活的在线适应策略，用于对性能指标权重进行调整，以构成一个欠驱动旋转摆系统的可变结构线性二次积分（LQI）控制器。所提出的控制过程旨在提高控制器的适应性，使其能够灵活地操纵控制刚度，从而有效地抑制有界外部干扰，同时保持系统的闭环稳定性并节省整体控制能量消耗。该方案通过在通用 LQI 控制器中增加一个创新的在线权重自适应律来实现，该自适应律自适应地调节内部性能指标的状态加权因子。权重自适应律被制定为耗散项、反耗散项和模型参考跟踪项的预校准函数，以在控制器设计中实现所需的灵活性。调整后的状态加权因子由 Riccati 方程用于生成时变状态补偿器增益。
