Abstract
This paper investigates the issue of finite-time tracking control for flexible-joint robots. In the design scheme, the unknown continuous function is identified by a fuzzy system. By introducing the command filter technique, “explosion of complexity” problem which arises from repeated differentiation of virtual controllers is avoided. Meanwhile, errors resulting from the first-order filters can be reduced with the introduced compensation signal. Besides, the proposed method ensures that the tracking performance could be achieved within a limited time. Eventually, the simulation is given to demonstrate the effectiveness of the proposed scheme.
Highlights
Compared to the rigid-joint robots, flexible-joint (FJ) robots have many advantages of high performance, such as light mass, small size, and low energy consumption
For nonlinear systems with saturation input, the finite-time tracking control problem with command filter is investigated in Yu et al
Inspired by the above works, this paper studies the problem of finite-time tracking control for FJ robots and develops an adaptive fuzzy control algorithm with the help of the command filter technique
Summary
Compared to the rigid-joint robots, flexible-joint (FJ) robots have many advantages of high performance, such as light mass, small size, and low energy consumption. Considering the uncertain nonlinear systems with actuator faults, Li23 developed a fault-tolerant control scheme by the aid of command filter design. For nonlinear systems with saturation input, the finite-time tracking control problem with command filter is investigated in Yu et al.. Inspired by the above works, this paper studies the problem of finite-time tracking control for FJ robots and develops an adaptive fuzzy control algorithm with the help of the command filter technique. Consider the nonlinear system (equation [3]), under the virtual controller (equations [17], [25], and [31]), the actual controller (equation [39]), and the adaptive law (equation [40]), the tracking error is practical finite stable and all signals in the resulting system are bounded. We obtain the following conclusion by choosing a suitable matrix Li
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