In this paper, a RISE (Robust Integral of the Sign Error) controller with adaptive feedforward compensation terms based on Associative Memory Neural Network (AMNN) type B-Spline is proposed to regulate the positioning of a Delta Parallel Robot (DPR) with three degrees of freedom. Parallel Kinematic Manipulators (PKMs) are highly nonlinear systems, so the design of a suitable control scheme represents a significant challenge given that these kinds of systems are continually dealing with parametric and non-parametric uncertainties and external disturbances. The main contribution of this work is the design of an adaptive feedforward compensation term using B-Spline Neural Networks (BSNNs). They make an on-line approximation of the DPR dynamics and integrates it into the control loop. The BSNNs’ functions are bounded according to the extreme values of the desired joint space trajectories that are the BSNNs’ inputs, and their weights are on-line adjusted by gradient descend rules. In order to evaluate the effectiveness of the proposed control scheme with respect to the standard RISE controller, numerical simulations for different case studies under different scenarios were performed.

本文提出了一种基于自适应记忆神经网络（AMNN）B样条的带自适应前馈补偿项的RISE（符号误差鲁棒积分）控制器，用于调节具有3度角的Delta并联机器人（DPR）的位置自由。并联运动机械手（PKM）是高度非线性的系统，因此，鉴于这类系统一直在处理参数和非参数不确定性以及外部干扰，因此合适的控制方案的设计面临着重大挑战。这项工作的主要贡献是使用B样条神经网络（BSNN）设计了自适应前馈补偿项。他们对DPR动态进行在线近似，并将其集成到控制回路中。BSNN的功能根据作为BSNN输入的所需关节空间轨迹的极值来界定，其权重由梯度下降规则在线调整。为了评估建议的控制方案相对于标准RISE控制器的有效性，针对不同情况下的不同案例研究进行了数值模拟。