scholarly journals End-Effector Trajectory Tracking Control of Space Robot withL2Gain Performance

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Haibo Zhang ◽  
Dayi Wang ◽  
Chunling Wei ◽  
Bing Xiao
Keyword(s):  
Trajectory Tracking ◽  
External Disturbance ◽  
Adaptive Controller ◽  
Space Robot ◽  
Disturbance Attenuation ◽  
Trajectory Tracking Control ◽  
End Effector ◽  
Chebyshev Neural Network ◽  
System Output ◽  
System Uncertainties

This paper presents a novel solution to the control problem of end-effector robust trajectory tracking for space robot. External disturbance and system uncertainties are addressed. For the considered robot operating in free-floating mode, a Chebyshev neural network is introduced to estimate system uncertainties and external disturbances. An adaptive controller is then proposed. The closed-loop system is guaranteed to be ultimately uniformly bounded. The key feature of this proposed approach is that, by choosing appropriate control gains, it can achieve any given small level ofL2gain disturbance attenuation from external disturbance to system output. The tracking performance is evaluated through a numerical example.

scholarly journals An Adaptive RBF-NMPC Architecture for Trajectory Tracking Control of Underwater Vehicles

Machines ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 105
Author(s):  
Zhenzhong Chu ◽  
Da Wang ◽  
Fei Meng
Keyword(s):  
Model Predictive Control ◽  
Prediction Model ◽  
Real Time ◽  
Predictive Control ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Working Environment ◽  
Gray Wolf ◽  
Trajectory Tracking Control ◽  
System Output

An adaptive control algorithm based on the RBF neural network (RBFNN) and nonlinear model predictive control (NMPC) is discussed for underwater vehicle trajectory tracking control. Firstly, in the off-line phase, the improved adaptive Levenberg–Marquardt-error surface compensation (IALM-ESC) algorithm is used to establish the RBFNN prediction model. In the real-time control phase, using the characteristic that the system output will change with the external environment interference, the network parameters are adjusted by using the error between the system output and the network prediction output to adapt to the complex and uncertain working environment. This provides an accurate and real-time prediction model for model predictive control (MPC). For optimization, an improved adaptive gray wolf optimization (AGWO) algorithm is proposed to obtain the trajectory tracking control law. Finally, the tracking control performance of the proposed algorithm is verified by simulation. The simulation results show that the proposed RBF-NMPC can not only achieve the same level of real-time performance as the linear model predictive control (LMPC) but also has a superior anti-interference ability. Compared with LMPC, the tracking performance of RBF-NMPC is improved by at least 43% and 25% in the case of no interference and interference, respectively.

Dynamic trajectory-tracking control method of robotic transcranial magnetic stimulation with end-effector gravity compensation based on force sensors

2018 ◽  
Vol 45 (6) ◽  
pp. 722-731
Author(s):  
ZeCai Lin ◽  
Wang Xin ◽  
Jian Yang ◽  
Zhang QingPei ◽  
Lu ZongJie
Keyword(s):  
Neural Networks ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Magnetic Stimulation ◽  
Control Method ◽  
Gravity Compensation ◽  
Trajectory Tracking Control ◽  
End Effector ◽  
Content Type ◽  
Dynamic Trajectory

Purpose This paper aims to propose a dynamic trajectory-tracking control method for robotic transcranial magnetic stimulation (TMS), based on force sensors, which follows the dynamic movement of the patient’s head during treatment. Design/methodology/approach First, end-effector gravity compensation methods based on kinematics and back-propagation (BP) neural networks are presented and compared. Second, a dynamic trajectory-tracking method is tested using force/position hybrid control. Finally, an adaptive proportional-derivative (PD) controller is adopted to make pose corrections. All the methods are designed for robotic TMS systems. Findings The gravity compensation method, based on BP neural networks for end-effectors, is proposed due to the different zero drifts in different sensors’ postures, modeling errors in the kinematics and the effects of other uncertain factors on the accuracy of gravity compensation. Results indicate that accuracy is improved using this method and the computing load is significantly reduced. The pose correction of the robotic manipulator can be achieved using an adaptive PD hybrid force/position controller. Originality/value A BP neural network-based gravity compensation method is developed and compared with traditional kinematic methods. The adaptive PD control strategy is designed to make the necessary pose corrections more effectively. The proposed methods are verified on a robotic TMS system. Experimental results indicate that the system is effective and flexible for the dynamic trajectory-tracking control of manipulator applications.

Adaptive trajectory tracking control of a differential drive wheeled mobile robot

Robotica ◽  
2010 ◽  
Vol 29 (3) ◽  
pp. 391-402 ◽  
Author(s):  
Khoshnam Shojaei ◽  
Alireza Mohammad Shahri ◽  
Ahmadreza Tarakameh ◽  
Behzad Tabibian
Keyword(s):  
Mobile Robot ◽  
Trajectory Tracking ◽  
Feedback Linearization ◽  
Dynamic Models ◽  
Parametric Uncertainty ◽  
Adaptive Controller ◽  
Wheeled Mobile Robot ◽  
Trajectory Tracking Control ◽  
Actuator Dynamics ◽  
Simulation Results

SUMMARYThis paper presents an adaptive trajectory tracking controller for a non-holonomic wheeled mobile robot (WMR) in the presence of parametric uncertainty in the kinematic and dynamic models of the WMR and actuator dynamics. The adaptive non-linear control law is designed based on input–output feedback linearization technique to get asymptotically exact cancellation for the uncertainty in the given system parameters. In order to evaluate the performance of the proposed controller, a non-adaptive controller is compared with the adaptive controller via computer simulation results. The results show satisfactory trajectory tracking performance by virtue of SPR-Lyapunov design approach. In order to verify the simulation results, a set of experiments have been carried out on a commercial mobile robot. The experimental results also show the effectiveness of the proposed controller.

Robust Trajectory Tracking Control of Autonomous Quad-Rotor UAV Based on Double Loop Frame

2011 ◽  
Vol 467-469 ◽  
pp. 1421-1426
Author(s):  
Zhi Cheng Hou ◽  
X. Gong ◽  
Y. Bai ◽  
Y.T. Tian ◽  
Q. Sun
Keyword(s):  
Trajectory Tracking ◽  
Tracking Control ◽  
Control Method ◽  
External Disturbance ◽  
Double Loop ◽  
Trajectory Tracking Control ◽  
Stability Augmentation ◽  
Aerial Vehicle ◽  
Simulation Results ◽  
Control Quantity

This paper deals with the under-actuated characteristic of a quad-rotor unmanned aerial vehicle (UAV). By designing the double loop configuration, the autonomous trajectory tracking is realized. The model uncertainty, external disturbance and the senor noise are also taken into consideration. Then the controller is put forward in the inner loop. An optimal stability augmentation control (SAC) method is used to stabilize the horizon position and keep it away from oscillation. By calculating the nonlinear decouple map, control quantity is converted to the speeds of the four rotors. At last some simulation results and the prototype implementation prove that the control method is effective.

scholarly journals Modeling and Analysis in Trajectory Tracking Control for Wheeled Mobile Robots with Wheel Skidding and Slipping: Disturbance Rejection Perspective

Actuators ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 222
Author(s):  
Xiaoshan Gao ◽  
Liang Yan ◽  
Chris Gerada
Keyword(s):  
Mobile Robot ◽  
Trajectory Tracking ◽  
Disturbance Rejection ◽  
Tracking Control ◽  
Dynamic Models ◽  
Nonholonomic Constraints ◽  
Disturbance Attenuation ◽  
Wheeled Mobile Robots ◽  
Trajectory Tracking Control ◽  
Control Scheme

Wheeled mobile robot (WMR) is usually applicable for executing an operational task around complicated environment; skidding and slipping phenomena unavoidably appear during the motion, which thus can compromise the accomplishment of the task. This paper investigates the trajectory tracking control problem of WMRs via disturbance rejection in the presence of wheel skidding and slipping phenomena. The kinematic and dynamic models with the perturbed nonholonomic constraints are established. The trajectory tracking control scheme at the dynamic level is designed so that the mobile robot system can track the virtual velocity asymptotically, and counteract the perturbation caused by the unknown skidding and slipping of wheels. Both simulation and experimental works are conducted, and the results prove the performance of the proposed control scheme is effective in terms of tracking precision and disturbance attenuation.

scholarly journals Adaptive Sliding Mode Trajectory Tracking Control for WMR Considering Skidding and Slipping via Extended State Observer

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3305 ◽  
Author(s):  
Gang Wang ◽  
Chenghui Zhou ◽  
Yu Yu ◽  
Xiaoping Liu
Keyword(s):  
Trajectory Tracking ◽  
Tracking Control ◽  
Sliding Mode ◽  
External Disturbance ◽  
State Observer ◽  
Extended State Observer ◽  
Extended State ◽  
Parameter Uncertainties ◽  
Trajectory Tracking Control ◽  
Adaptive Sliding Mode

When the wheeled mobile robot (WMR) is required to perform specific tasks in complex environment, i.e., on the forestry, wet, icy ground or on the sharp corner, wheel skidding and slipping inevitably occur during trajectory tracking. To improve the trajectory tracking performance of WMR under unknown skidding and slipping condition, an adaptive sliding mode controller (ASMC) design approach based on the extended state observer (ESO) is presented. The skidding and slipping is regarded as external disturbance. In this paper, the ESO is introduced to estimate the lumped disturbance containing the unknown skidding and slipping, parameter variation, parameter uncertainties, etc. By designing a sliding surface based on the disturbance estimation, an adaptive sliding mode tracking control strategy is developed to attenuate the lumped disturbance. Simulation results show that higher precision tracking and better disturbance rejection of ESO-ASMC is realized for linear and circular trajectory than the ASMC scheme. Besides, experimental results indicate the ESO-ASMC scheme is feasible and effective. Therefore, ESO-ASMC scheme can enhance the energy efficiency for the differentially driven WMR under unknown skidding and slipping condition.

scholarly journals Model Identification and Trajectory Tracking Control for Vector Propulsion Unmanned Surface Vehicles

Electronics ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 22 ◽  
Author(s):  
Xiaojie Sun ◽  
Guofeng Wang ◽  
Yunsheng Fan
Keyword(s):  
Trajectory Tracking ◽  
Tracking Control ◽  
Sliding Mode ◽  
External Disturbance ◽  
Control Point ◽  
Model Identification ◽  
Underactuated System ◽  
Trajectory Tracking Control ◽  
Unmanned Surface Vehicles ◽  
Trajectory Tracking Controller

To promote the development of military and civilian applications for marine technology, more and more scientific research around the world has begun to develop unmanned surface vehicles (USVs) technology with advanced control capabilities. This paper establishes and identifies the model of vector propulsion USV, which is widely used at present. After analyzing its actuator distribution, we consider that the more realistic vessel model should be an incomplete underactuated system. For this system, a virtual control point method is adopted and an adaptive sliding mode trajectory tracking controller with neural network minimum learning parameter (NNMLP) theory is designed. Finally, in the simulation experiment, the thruster speed and propulsion angle are used as the inputs of the controller, and the linear and circular trajectory tracking tests are carried out considering the delay effect of the actuator, system uncertainty, and external disturbance. The results show that the proposed tracking control framework is reasonable.

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