An adaptive type-2 fuzzy sliding mode tracking controller for a robotic manipulator

Abstract

<abstract> <p>With the wide application of intelligent manufacturing and the development of diversified functions of industrial manipulator, the requirements for the control accuracy and stability of the manipulator servo system are also increasing. The control of industrial manipulator is a time-varying system with nonlinear and strong coupling, which is often affected by uncertain factors, including parameter changing, environmental interference, joint friction and so on. Aiming at the problem of the poor control accuracy of the manipulator. Under the complex disturbance environment, control accuracy of the manipulator will be greatly affected, so this paper proposes an adaptive type-2 fuzzy sliding mode control (AT2FSMC) method applied to the servo control of the industrial manipulator, which realizes the adaptive adjustment of the boundary layer thickness to suppress the trajectory error caused by the external disturbance and weakens the chattering problem of the sliding mode control. The simulation results on a two-axis manipulator indicate that, with the presence of external disturbances, the proposed control method can help the manipulator maintain control signal stability and improve tracking accuracy. It also suppressed chattering produced by sliding mode control (SMC) and strengthening the robustness of the system. Compared with other conventional trajectory tracking control methods, the effectiveness of the proposed method can be reflected. Finally, the proposed method is tested in an actual manipulator to complete a practical trajectory to prove its feasibility.</p> </abstract>

Event-triggered Stochastic Finite-time Tracking Control of Robot Manipulator with Uncertain Disturbance Neural Network Estimation

“…Here, a SCN neural network estimation method is presented for disturbance d(t). By assigning the input layer weights and hidden layer biases under the supervisory mechanism (10), The SCN can approximate the uncertain disturbance and make the estimation error converge to zero gradually. Compared with other neural network algorithms, the SCN method has obvious advantages in terms of learning and generalization ability [33] .…”

“…In reality, there are uncertain disturbances in various fields of manipulator applications, and we need to compensate these disturbances in order to obtain satisfactory tracking performance. In [10], an adaptive sliding mode tracking controller of an industrial manipulator was proposed to suppress the complex uncertain factors including parameter changing, environmental disturbance and joint friction. In [11], a robust observer-based trajectory tracking control method was presented to solve the stable motion problem for an unmanned aerial manipulator considering internal interactions and external disturbances.…”