Cooperative Deterministic Learning-Based Trajectory Tracking for a Group of Unicycle-Type Vehicles

2018 ◽  
Author(s):  
Xiaonan Dong ◽  
Chengzhi Yuan ◽  
Fen Wu
Keyword(s):  
Trajectory Tracking ◽  
Tracking Control ◽  
Rbf Neural Network ◽  
Lyapunov Stability Theory ◽  
State Feedback Control ◽  
Trajectory Tracking Control ◽  
Deterministic Learning ◽  
Nonholonomic Vehicles ◽  
Joint Tracking ◽  
Reference Trajectories

A cooperative deterministic learning based state feedback control algorithm is proposed in this paper for joint tracking control and learning/identification for a group of identical nonholonomic vehicles. Specifically, this algorithm is able to model the unknown nonlinear dynamics of the nonholonomic vehicle, and use it for trajectory tracking control with cooperative deterministic learning (DL) theory. In addition, cooperative DL grants every vehicle in the system the ability of knowledge learning not only along the trajectory of its own, but also along the trajectories of all other vehicles as well. It is shown using Lyapunov stability theory that with cooperative DL, the closed-loop system is guaranteed to be stable, with all vehicles tracking its own reference trajectories, and the radial basis function (RBF) neural network (NN) weights of all agents converge to the same constants.

Trajectory tracking control of robot manipulators using a neural-network-based torque compensator

1998 ◽  
Vol 212 (5) ◽  
pp. 361-372 ◽  
Author(s):  
Q Li ◽  
S K Tso ◽  
W J Zhang
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
High Performance ◽  
Robot Manipulator ◽  
Robot Manipulators ◽  
Lyapunov Stability Theory ◽  
System Stability ◽  
Trajectory Tracking Control ◽  
Modelling Uncertainties

In this paper, an adaptive neural-network-based torque compensator is developed for the trajectory-tracking control of robot manipulators. The overall control structure employs a classical non-linear decoupling controller for actuating torque computation based on an approximated robot dynamic model. To suppress the effects of uncertainties associated with the estimated model, a supplementary neural network algorithm is developed to generate compensation torques. The weight adaptation rule for this neuro-compensator is derived on the basis of the Lyapunov stability theory. Both global system stability and the error convergence can then be guaranteed. Simulation studies on a two-link robot manipulator demonstrate that high performance of the proposed control algorithm could be achieved under severe modelling uncertainties.

Neural Network Trajectory Tracking Control of Compliant Parallel Robot

2015 ◽  
Vol 799-800 ◽  
pp. 1069-1073
Author(s):  
Hao Tian ◽  
Yue Qing Yu
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Control Method ◽  
Rbf Neural Network ◽  
Control Strategies ◽  
Parallel Robot ◽  
Network Control ◽  
Trajectory Tracking Control ◽  
Compliant Joint

Trajectory tracking control of compliant parallel robot is presented. According to the characteristics of compliant joint, the system model is derived and the dynamic equation is obtained based on the Lagrange method. Radial Basis Function (RBF) neural network control is designed to globally approximate the model uncertainties. Further, an itemized approximate RBF control method is proposed for higher identify precision. The trajectory tracking abilities of two control strategies are compared through simulation.

A New Neural Network Based Sliding Mode Adaptive Controller for Tracking Control of Robot Manipulator

2021 ◽  
Vol 11 (2) ◽  
pp. 12-16
Author(s):  
Monisha Pathak ◽  
◽  
Mrinal Buragohain ◽  
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Rbf Neural Network ◽  
Adaptive Controller ◽  
Robotic Manipulator ◽  
Trajectory Tracking Control ◽  
Chattering Effect

In this paper a New RBF Neural Network based Sliding Mode Adaptive Controller (NNNSMAC) for Robot Manipulator trajectory tracking in the presence of uncertainties and disturbances is introduced. The research offers a learning with minimal parameter (LMP) technique for robotic manipulator trajectory tracking. The technique decreases the online adaptive parameters number in the RBF Neural Network to only one, lowering computational costs and boosting real-time performance. The RBFNN analyses the system's hidden non-linearities, and its weight value parameters are updated online using adaptive laws to control the nonlinear system's output to track a specific trajectory. The RBF model is used to create a Lyapunov function-based adaptive control law. The effectiveness of the designed NNNSMAC is demonstrated by simulation results of trajectory tracking control of a 2 dof Robotic Manipulator. The chattering effect has been significantly reduced.

Event-triggered reset trajectory tracking control for unmanned surface vessel system

2020 ◽  
pp. 095965182095327
Author(s):  
Haoping Wang ◽  
Shuyu Zhang
Keyword(s):  
Trajectory Tracking ◽  
Tracking Control ◽  
Input Saturation ◽  
Lyapunov Stability Theory ◽  
Trajectory Tracking Control ◽  
Nonlinear Disturbance Observer ◽  
Surface Vessels ◽  
Unmanned Surface Vessel ◽  
Reset Control ◽  
Event Triggered

This article considers the trajectory tracking control for unmanned surface vessels with unknown time-variant external disturbances and input saturation. The strategy mainly consists of event-triggered reset sub-controller and nonlinear disturbance observer–based compensation sub-controller. To reduce network transmissions, and in the meanwhile, guarantee the desirable closed-loop behavior, the event-triggered reset control is proposed where the reset law and the event-triggered mechanism are designed separately. Both of static and dynamic event-triggered reset controllers are designed. Their corresponding stability is demonstrated using Lyapunov stability theory. Finally, numerical simulation results are presented to demonstrate the effectiveness and robustness of the proposed trajectory tracking control strategy.

Research of Trajectory Tracking Control of Cruise Missile Based on Observer

2012 ◽  
Vol 184-185 ◽  
pp. 1599-1602
Author(s):  
Bo Hao ◽  
Fan Li ◽  
Jian Hui Zhao
Keyword(s):  
Trajectory Tracking ◽  
Tracking Control ◽  
Control Method ◽  
State Feedback Control ◽  
Cruise Missile ◽  
Linear Quadratic ◽  
Trajectory Tracking Control ◽  
Linear Quadratic Optimal Control ◽  
Full State Feedback ◽  
And Control

To achieve cruise missile accurate trajectory tracking control, an observer-based tracking control method is designed. An observer is developed to estimate the states and control signals of desired trajectory as the inputs of the tracking controller. The linear quadratic optimal control is used to realize full-state feedback control for trajectory tracking. A certain cruise missile is used for the tracking simulation and the result shows satisfactory performance, the control method is simple and suitable for engineering applications.

scholarly journals Sliding Mode Controller for Trajectory Tracking Control of Autonomous Mobile Robot

2018 ◽  
Vol 17 (1) ◽  
pp. 28-33
Author(s):  
Ang Oon Thay ◽  
Mohd Ariffanan Mohd Basri ◽  
Nurul Adilla Mohd Subha ◽  
Mohamad Amir Shamsudin ◽  
Shafishuhaza Sahlan
Keyword(s):  
Mobile Robot ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
High Speed ◽  
Sliding Mode ◽  
Lyapunov Stability Theory ◽  
Autonomous Mobile Robot ◽  
Trajectory Tracking Control ◽  
Heavy Load ◽  
Dynamic Controller

Trajectory tracking control is an important issue in the field of autonomous mobile robot. In high speed and heavy load applications, the dynamic of autonomous mobile robot plays an important factor in allowing the robot to follow the desired trajectory path. However, the parameters attribute to robot dynamic are difficult to model and highly uncertain. One of the uncertainty factors is the load variation which changes the dynamic parameters of autonomous mobile robot. Meanwhile, Sliding Mode Control (SMC) is well known for its robustness against model uncertainties and disturbances. This paper is about design of dynamic controller based on SMC technique for trajectory tracking control of autonomous mobile robot system. The model of mobile robot is developed based on Pioneer 3-DX mobile robot. The trajectory tracking controller is divided into two parts, kinematic controller and dynamic controller. Stability of both dynamic and kinematic controller is verified using Lyapunov stability theory. The performance of trajectory tracking control for proposed dynamic controller based on SMC technique is compared against dynamic controller based on Proportional-Integral-Derivative (PID) technique with and without the presence of dynamic uncertainties. Simulation results show proposed dynamic controller based on SMC technique give better performance in trajectory tracking control in comparison to PID.

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