Adaptive Tracking Control of Voltage-Driven Robotic Manipulators with Output Constraints

This paper proposes an adaptive tracking control scheme for voltage-driven robotic manipulators with output constraints to achieve satisfactory tracking performance. To overcome the design difficulty from output constraints, a tangent boundary Lyapunov function (tBLF) is first introduced to keep the errors remain within the allowable range of the constraints. Then, RBF neural networks are employed to approximate the uncertainties in the system, and a tracking differentiator (TD) is applied to obtain the differentiation of the virtual control laws in the back stepping design. Finally, the comparative simulations are given to illustrate the effectiveness of the proposed scheme.