Decentralized adaptive controller design of large-scale uncertain robotic systems

Decentralized Adaptive Robust Control of Robot Manipulators Using Disturbance Observers

“…In contrast to the DOB-based linear local controllers for robot manipulators, in this study, by some special nonlinear damping terms, the boundedness of the signals of the overall nonlinear system is first ensured. Differing from some existing works [5]- [11] where only the control performance of the overall system is analyzed, we also analyze how the DOB and adaptive sliding mode control play in a cooperative way to achieve an excellent control performance in each local subsystem, provided the boundedness of the overall system signals. Simulation results are provided to support the theoretical results.…”

“…In fact, the majority of contemporary robots are still controlled by the decentralized (independent joint) proportional-integral-derivative (PID) law in favor of its simple computation and low-cost setup [3], [4]. Due to strong nonlinearities in dynamics, advanced decentralized control techniques are still attracting much attention to achieve a satisfactory trajectory performance of robot manipulators [5]- [11].…”

Adaptive Stability Control of the Robot Based on Extended State Observer

“…Assuming that some of the tracking error coordinates are not measurable, an observer based controller resulting in K-exponential convergence of the tracking error was introduced in [3]. A decentralized neural network control design was developed for robotic systems in [4], and the unknown nonlinear influencing factors can be estimated by the neural network in real time. Cortesao [5] introduced the Active Observer algorithm in the framework of Kalman filters, and designed observer based on the Kalman filtering principle that improved the parameter error robustness of the robot control system.…”

Lyapunov Function Based Neural Networks for Adaptive Tracking of Robotic Arm

“…A tracking controller is designed in [12] by combining fuzzy logic with sliding mode control. In [13], a decentralized neural network control is developed for uncertain robot manipulators. However, in these techniques, mathematical formulation of error convergence and closed loop stability conditions is not provided.…”