This paper presents a neuroadaptive tracking control approach for uncertain robotic manipulators subject to asymmetric yet time-varying full-state constraints without involving feasibility conditions. Existing control algorithms either ignore motion constraints or impose additional feasibility conditions. In this paper, by integrating a nonlinear state-dependent transformation into each step of backstepping design, we develop a control scheme that not only directly accommodates asymmetric yet time-varying motion (position and velocity) constraints but also removes the feasibility conditions on virtual controllers, simplifying design process, and making implementation less demanding. Neural network (NN) unit accounting for system uncertainties is included in the loop during the entire system operational envelope in which the precondition on the NN training inputs is always ensured. The effectiveness and benefits of the proposed control method for robotic manipulator are validated via computer simulation.

中文翻译：

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时变不对称运动约束下的神经自适应机器人控制：一种无可行性条件的方法


本文提出了一种神经自适应跟踪控制方法，适用于受不对称但时变全状态约束的不确定机器人操纵器，且不涉及可行性条件。现有的控制算法要么忽略运动约束，要么施加额外的可行性条件。在本文中，通过将非线性状态相关变换集成到反步设计的每个步骤中，我们开发了一种控制方案，该方案不仅直接适应不对称但时变的运动（位置和速度）约束，而且消除了虚拟控制器上的可行性条件，简化设计过程，降低实施要求。考虑系统不确定性的神经网络（NN）单元包含在整个系统操作范围内的循环中，其中始终确保神经网络训练输入的前提条件。通过计算机仿真验证了所提出的机器人操纵器控制方法的有效性和优点。