双执行器推力矢量平台的鲁棒PIDNet控制
Robust PIDNet Control of a Dual-Actuator Thrust Vectoring Platform
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中文总结 AI 辅助
本文针对双执行器推力矢量平台姿态跟踪问题，提出结合RBF网络在线积分补偿的鲁棒PIDNet控制器，经实验验证其性能优于传统PID与超螺旋控制器，ITNE降低10%且无额外控制消耗。
中文摘要 AI 辅助
本文针对双执行器推力矢量平台的姿态跟踪问题提出了一种鲁棒控制器，控制目标是利用通过万向节连接的两个线性执行器跟踪单位球面上的期望推力方向。所提框架结合了有界非线性PD作用与径向基函数（RBF）网络，该网络仅依靠执行器位移测量得到的跟踪误差及其时间导数，通过在线权重调整生成依赖于状态的积分补偿项。控制律被构造成耦合多输入多输出结构，使RBF网络能够补偿跨通道效应，包括方向相关的摩擦和几何耦合，且实现无需依赖模型。通过凸李雅普诺夫函数证明了稳定性，该函数的梯度沿误差流形自然有界；针对扩展误差推导了H∞增益界，其具有关于跟踪精度的局部线性化解释。在两自由度万向节装置上的实验验证了所提控制器的性能，其相比传统PID控制器和超螺旋控制器均有提升，在线自适应使ITNE较非自适应基线降低10%，且未额外消耗控制量。
英文摘要
This paper presents a robust controller for attitude tracking on a dual-actuator thrust vectoring platform. The control objective is to track a desired thrust direction on the unit sphere using two linear actuators connected through a universal joint. The proposed framework combines a bounded nonlinear PD action with a radial basis function (RBF) network that generates state-dependent integral compensation through online weight adaptation, relying solely on tracking error and its time derivative from actuator-displacement measurements. The control law is formulated as a coupled multiple-input--multiple-output structure, enabling the RBF network to compensate cross-channel effects, including direction-dependent friction and geometric coupling, with model-free implementation. Stability is established through a convex Lyapunov function whose gradient is naturally bounded along the error manifold. An H-infinity gain bound is derived for the extended error, with a local linearized interpretation in terms of tracking accuracy. The proposed controller is validated experimentally on a two-degree-of-freedom gimbal rig, achieving improved performance relative to a conventional PID and to a super-twisting controller, with online adaptation reducing the ITNE by 10% over the non-adaptive baseline at no additional control effort.
