Abstract
This paper proposes an adaptive control scheme based on terminal sliding mode (TSM) for robotic manipulators with output constraints and unknown disturbances. The fuzzy logic system (FLS) is developed to approximate unknown dynamics of robotic manipulators. An error transformation technique is used in the process of controller design to ensure that the output constraints are not violated. The advantage of the error transformation compared to traditional barrier Lyapunov functions (BLFs) is that there is no need to design a virtual controller. Thus, the design complexity of the controller is reduced. Through Lyapunov stability analysis, the system state can be proved to converge to the neighborhood near the balanced point in finite time. Extensive simulation results illustrated the validity of the proposed controller.
Highlights
In recent decades, robotic manipulators have been widely used in industrial and aerospace fields due to the rapid development of artificial intelligence [1,2,3,4,5,6]
Input, and measurement disturbance always exist, some linear control schemes cannot obtain satisfactory performance. erefore, many researchers utilize adaptive control [7,8,9,10,11], robust control [12, 13], output-feedback control [14, 15], and learning control strategies [16, 17] to overcome above difficulties. e security issues caused by output constraints cannot be ignored because humans interact with robotic manipulators
In [25], an adaptive neural network tracking control is proposed for robotic manipulators subjected to output constraints. e output constraints of some systems are not immutable; to handle this problem, an asymmetric barrier Lyapunov function is used in the design process of the controller in [26]
Summary
Robotic manipulators have been widely used in industrial and aerospace fields due to the rapid development of artificial intelligence [1,2,3,4,5,6]. In [25], an adaptive neural network tracking control is proposed for robotic manipulators subjected to output constraints. Fast terminal sliding mode control schemes are used to control the single input single output system (SISO) and the robotic manipulators, respectively, in [42, 43]. Both have achieved fast and high-precision tracking performance. To better solve the trajectory tracking problem of a class of manipulators with output constrains and model uncertainty, a novel adaptive fuzzy control scheme that combines error transformation with finite time sliding surface is designed. First, the dynamic model, fuzzy logic system, and error transformation are presented, followed by the derivation of the controller. en, the stability analysis and mathematical proof are given. e paper ends with some comparative simulations and conclusions
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