Dynamic Learning from Adaptive Neural Control of Uncertain Robots with Guaranteed Full-State Tracking Precision

Abstract

A dynamic learning method is developed for an uncertain -link robot with unknown system dynamics, achieving predefined performance attributes on the link angular position and velocity tracking errors. For a known nonsingular initial robotic condition, performance functions and unconstrained transformation errors are employed to prevent the violation of the full-state tracking error constraints. By combining two independent Lyapunov functions and radial basis function (RBF) neural network (NN) approximator, a novel and simple adaptive neural control scheme is proposed for the dynamics of the unconstrained transformation errors, which guarantees uniformly ultimate boundedness of all the signals in the closed-loop system. In the steadystate control process, RBF NNs are verified to satisfy the partial persistent excitation (PE) condition. Subsequently, an appropriate state transformation is adopted to achieve the accurate convergence of neural weight estimates. The corresponding experienced knowledge on unknown robotic dynamics is stored in NNs with constant neural weight values. Using the stored knowledge, a static neural learning controller is developed to improve the full-state tracking performance. A comparative simulation study on a 2-link robot illustrates the effectiveness of the proposed scheme.

Minimal-Learning-Parameter Technique Based Adaptive Neural Sliding Mode Control of MEMS Gyroscope

“…For future work, more efficient learning methods [21][22][23][24][25] will be tested on the dynamics while the implementation for real systems will be analyzed.…”

Adaptive Backstepping Sliding Mode Control of Trajectory Tracking for Robotic Manipulators

“…e dynamic parameters which describe the dynamic model are important for the control algorithms, effective simulation results, and accurate trajectory tracking algorithms. Dynamic equation of the robotic manipulator withn-DOF has been characterized in many literature studies [1][2][3][4][5][6][7][8][9][10][11] as follows:…”

“…e first stage of trajectory tracking is to establish the precise mathematical model of the robotic manipulator. However, the nonlinear part of the dynamic model of the robotic manipulator is ignored in many literatures [1][2][3][4][5] or parameter identification by many approaches [6][7][8]; even the torque in the joint space and the moment of inertia were ignored in [9]. By calculating kinetic energy, potential energy, and generalized force, the Lagrange equation was utilized to build the dynamic equation for robotic manipulator [10,11].…”

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

“…To satisfy this condition, a performance function transformation was used to convert the "constrained" system into the "unconstrained" one [30]. Based on the idea in [30], further researches on prescribed performance for a variety of systems are proposed [31][32][33][34][35][36]. Authors in [31,32] presented novel controllers for FJRs to achieve tracking control of link angles with any prescribed performance requirements.…”

“…Authors in [31,32] presented novel controllers for FJRs to achieve tracking control of link angles with any prescribed performance requirements. By combining neural learning control scheme, further results are given in [33][34][35]. In [36], an adaptive prescribed performance tracking control scheme is investigated for a class of output feedback nonlinear systems with input unmodeled dynamics based on dynamic surface control method.…”