Neural Learning Control of Flexible Joint Manipulator with Predefined Tracking Performance and Application to Baxter Robot

Abstract

This paper focuses on neural learning from adaptive neural control (ANC) for a class of flexible joint manipulator under the output tracking constraint. To facilitate the design, a new transformed function is introduced to convert the constrained tracking error into unconstrained error variable. Then, a novel adaptive neural dynamic surface control scheme is proposed by combining the neural universal approximation. The proposed control scheme not only decreases the dimension of neural inputs but also reduces the number of neural approximators. Moreover, it can be verified that all the closed-loop signals are uniformly ultimately bounded and the constrained tracking error converges to a small neighborhood around zero in a finite time. Particularly, the reduction of the number of neural input variables simplifies the verification of persistent excitation (PE) condition for neural networks (NNs). Subsequently, the proposed ANC scheme is verified recursively to be capable of acquiring and storing knowledge of unknown system dynamics in constant neural weights. By reusing the stored knowledge, a neural learning controller is developed for better control performance. Simulation results on a single-link flexible joint manipulator and experiment results on Baxter robot are given to illustrate the effectiveness of the proposed scheme.

Industrial Robot Trajectory Tracking Control Using Multi-Layer Neural Networks Trained by Iterative Learning Control

“…In a general framework of neural-learning control, NNs are used to either approximate the robot forward dynamics or inverse dynamics for controller design [13], and the control problem falls within the context of either reinforcement learning [14,15], adaptive control [16], or optimal control [17]. Radial basis function (RBF) NNs, multi-layer feedforward NNs, and recurrent neural networks (RNNs) have been explored for trajectory tracking control [18][19][20][21][22]. The concept of incorporating fuzzy logic into NN control has also grown into a popular research topic [23][24][25][26].…”

Biologically Inspired Motion Modeling and Neural Control for Robot Learning From Demonstrations

“…where the first two terms guarantee the constraints satisfaction, the third term improves the robustness to disturbance, and the last term is used to compensate for the dynamics uncertainties. Substituting (33), (34), (36) and (38) into (32), we have:…”

“…Therefore, considering the closed-loop system including the robot dynamics (22), the control torque (38) and the NN update law (44), the tracking error e 1 will converge asymptotically to the compact set:…”

Dynamic learning from adaptive neural control for flexible joint robot with tracking error constraints using high-gain observer

“…To address the learning and control problem of unknown cascaded nonlinear systems, a few elegant dynamic learning methods were proposed by combining a recursive design with a system decomposition strategy (Wang, Wang, Liu, & Hill, 2012;Wang & Wang, 2015a, 2015b. Furthermore, the learning mechanism was also applied into some physical systems such as marine surface vessels (Dai, Wang, & Luo, 2012;Dai, Zeng, & Wang, 2016), robot manipulators (Wang, Ye, and Chen, 2017b), and gait recognition (Zeng & Wang, 2016). For FJR with unknown system dynamics, two or three NN approximators will be used in a recursive design process.…”