Adaptive impedance control of uncertain robot manipulators with saturation effect based on dynamic surface technique and self-recurrent wavelet neural networks

Abstract

SUMMARYSaturation nonlinearities, among the known challenges in control engineering, are ubiquitous in robotic systems and can lead to stability and performance degradation. In this paper, an adaptive dynamic surface impedance (ADSI) control approach is developed for an n-link robotic manipulator by employing self-recurrent wavelet neural networks (SRWNNs) in order to overcome the saturation effect. The proposed control approach is inspired by the theory of dynamic surface control (DSC) and SRWNNs. As a novel application of the dynamic surface method to obtain a simple structure, the target impedance is formulated in the state–space, and effective dynamic surfaces are defined to track the desired impedance behavior. In fact, DSC is used to force the robot manipulator to track the desired impedance, while the robot interacts with an environment. In addition, SRWNNs are applied to approximate the parametric uncertainties and external disturbances in the robot dynamical model. Self-feedback neurons are embedded as memory units in SRWNNs to model the sudden dynamic jumps of the environment. Using Lyapunov's method, an ADSI controller is designed, and adaptation laws are induced to guarantee the stability of the closed-loop system. Finally, simulations are conducted to verify the proper performance of the proposed approach for removing the saturation effect and tracking the target impedance. It is worth noting that the simulation results indicate the robustness of the controller against uncertainties and external disturbances.

Adaptive Fuzzy Backstepping Control Based on Dynamic Surface Control for Uncertain Robotic Manipulator

“…Based on the tracking error, considering the boundary layer error (22), the surface error (23) and the weight matrix errors (15), another Lyapunov function is chosen as follows:…”

“…Dandan Lei and his co-authors successfully developed composite controllers based on DSC technique for micro-gyroscope [20][21] to improve the timeliness and effectiveness of tracking and other performances in the presence of model uncertainties and external disturbances. In [22], the effectiveness of the new application of DSC on uncertain robot manipulators is verified effectiveness by using Lyapunov theory and simulating results. Both [23] and [24] adopt DSC technique to propose advanced tracking controllers for induction motor servo drive and DC/DC boost converter respectively.…”

Motion control of linear induction motor using self-recurrent wavelet neural network trained by model predictive controller

“…The wavelet neural network (WNN) is one of the most important feedforward neural networks, and it has been the subject of much debate in recent years. The recurrent wavelet neural networks is a more advanced wavelet neural networks model [12], [13].…”

Adaptive Robust Tracker Design for Nonlinear Sandwich Systems Subject to Saturation Nonlinearities

“…1,2 The most important static nonlinearities are saturation and dead-zone, and the dynamic nonlinearities are hysteresis and backlash. These types of hard nonlinearities exist in a wide range of practical systems such as mechanical systems, 3 hydraulic valves, 4 mechatronic systems, 5 DC servo motors, 6 mechanical transmission systems, 7 etc. In industrial fields, many systems can be described as sandwich systems such as hydraulic actuator of aircraft elevator, 8 X-Y moving positioning stage system 9 and hydraulic actuator with a pilot valve.…”