Related Experiment Video
Updated: Nov 1, 2025

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
Published on: August 15, 2020
Full-state constrained neural control and learning for the nonholonomic wheeled mobile robot with unknown dynamics
Yuxiang Wu1, Yu Wang1, Haoran Fang1
1School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China.
Abstract:
The adaptive learning and control are proposed for the full-state(FS) constrained NWMR system with external destabilization. First, the constrained state is reformulated as the unconstrained state. Then, approximating the unknown dynamics in the closed-loop (CL) system is conducted via radial basis function (RBF) NN. Also, a sliding term is designed to deal with the external destabilization and the neural network training error. The derived adaptive neural controller can realize the asymptotic stability of a robot system without violating FS constraints. Moreover, the neural weights are converged so that the unknown dynamics are expressed by the constant weights in the CL system. It is also applicable to other similar control tasks. Lastly, the proposed algorithm is simulated and validated.
Related Concept Videos
Linear Approximation in Time Domain
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
Controller Configurations
Control-system compensation involves various configurations, most commonly series or cascade compensation, in which the controller...
Rolling Resistance: Problem Solving
Kinematic Equations: Problem Solving
One-Degree-of-Freedom System
A one-degree-of-freedom system is defined by an independent variable that determines its state and behavior. One example of a one-degree-of-freedom system is a simple harmonic oscillator, such as a...
Feedback control systems
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...

