IEEE Trans Cybern. 2022 Apr;52(4):2274-2283. doi: 10.1109/TCYB.2020.3003550. Epub 2022 Apr 5.
In this article, the optimal control problem for robotic manipulators (RMs) with prescribed constraints is addressed. Considering the environmental conditions and requirements of practical applications, prescribed constraints are imposed on the system states to guarantee the control performance and normal operation of the robotic system. Accordingly, an error transformation function is adopted to cope with the prescribed constraints and generate an equivalent unconstrained error for the convenience of the intelligent control design. In order to improve the learning ability and optimize the control performance, critic learning (CL) is introduced to the control design of the constrained RM based on the transformed equivalent unconstrained system. In addition, the stability analysis is given to illustrate the feasibility of the proposed CL-based control. Finally, simulations are conducted on a two-degree-of-freedom (DOF)-constrained RM to further validate the effectiveness of the proposed controller.
本文针对带规定约束的机器人机械手（RM）的最优控制问题进行了研究。考虑到环境条件和实际应用的要求，对系统状态施加规定约束，以保证机器人系统的控制性能和正常运行。相应地，采用误差变换函数来处理规定约束，并为智能控制设计的方便生成等效的无约束误差。为了提高学习能力和优化控制性能，在基于转换后的等效无约束系统的约束 RM 控制设计中引入了批评学习（CL）。此外，还给出了稳定性分析，以说明所提出的基于 CL 的控制的可行性。最后，在一个两自由度（DOF）约束 RM 上进行了仿真，进一步验证了所提出控制器的有效性。
