Trajectory Tracking Control of Unconstrained Object Using the SIRMs Dynamically Connected Fuzzy Inference Model

2000 ◽  
Vol 4 (4) ◽  
pp. 302-312 ◽  
Author(s):  
Jianqiang Yi ◽  
◽  
Naoyoshi Yubazaki ◽  
Kaoru Hirota ◽  
Keyword(s):  
Trajectory Tracking ◽  
Tracking Control ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Tracking Error ◽  
Control Object ◽  
Tennis Ball ◽  
Table Tennis ◽  
Inference Model ◽  
Trajectory Tracking Control

A trajectory tracking experiment system taking an unconstrained table-tennis ball as the control object is constructed, and a fuzzy controller based on the SIRMs dynamically connected fuzzy inference model is proposed. For each of the three input items of the fuzzy controller, a SIRM (Single Input Rule Module) is established and an importance degree is defined. Especially for the input item corresponding to ball velocity, its importance degree is tuned dynamically according to moving conditions. The summation of the products of the importance degree and the fuzzy inference result of the SIRMs is calculated to control the angles of a table, making the ball on the table move along a desired trajectory. A virtual spiral asymptotic trajectory is also introduced to give the object an adequate desired position at each sampling time. Tracking experiment results for three kinds of circles and one kind of ellipses show that in more than 80% of the experiments performed under the SIRMs dynamically connected fuzzy inference model, the maximum tracking error is smaller than 0.05m and the unevenness of the sampling steps necessary for each round is very small. Compared with conventional fuzzy controller, the SIRMs dynamically connected fuzzy inference model is proved to be effective in tracking control of unconstrained objects.

scholarly journals A Novel Trajectory Tracking Control Strategy for Underactuated Quadrotor UAV with Uncertainties and Disturbances

2020 ◽  
Author(s):  
Jiang Han ◽  
Siyang Yang ◽  
Lian Xia ◽  
Ye-Hwa Chen
Keyword(s):  
Trajectory Tracking ◽  
Control Strategy ◽  
Tracking Control ◽  
Tracking Error ◽  
Uniform Boundedness ◽  
Trajectory Tracking Control ◽  
Quadrotor Uav ◽  
Uniform Ultimate Boundedness ◽  
Position Controller ◽  
Aerial Vehicle

Abstract In this research, a novel position trajectory tracking control architecture has been constructed for an underactuated quadrotor unmanned aerial vehicle (UAV) with uncertainties and disturbances. Primarily, we divide the whole dynamic system into an underactuated position subsystem and a fully-actuated attitude subsystem. For the position subsystem, we have transformed it into a fully-actuated system by constructing a virtual PD controller, and this controller can render the position tracking error asymptotically stable. Besides, based on the position controller designed for quadrotor UAV, the desired attitudes, i.e. roll, pitch and yaw angles, will be derived. Next, as for the attitude subsystem which is sensitive to uncertainties and external disturbances, a novel robust attitude constraint-following controller is proposed for this aircraft, this attitude controller can not only guarantee the uniform boundedness and uniform ultimate boundedness of constraint deviation, but also does not requiring more information of uncertainties and disturbances except their bounds. Eventually, the simulations have demonstrated a sound tracking performance of our proposed control strategy for quadrotor UAV even in the presence of uncertainties and disturbances.

Iterative Learning Control With Switching Gain Feedback for Nonlinear Systems

2010 ◽  
Vol 6 (1) ◽  
Author(s):  
P. R. Ouyang ◽  
B. A. Petz ◽  
F. F. Xi
Keyword(s):  
Feedback Control ◽  
Nonlinear Systems ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Iterative Learning Control ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Trajectory Tracking Control ◽  
Control Scheme

Iterative learning control (ILC) is a simple and effective technique of tracking control aiming at improving system tracking performance from trial to trial in a repetitive mode. In this paper, we propose a new ILC called switching gain PD-PD (SPD-PD)-type ILC for trajectory tracking control of time-varying nonlinear systems with uncertainty and disturbance. In the developed control scheme, a PD feedback control with switching gains in the iteration domain and a PD-type ILC based on the previous iteration combine together into one updating law. The proposed SPD-PD ILC takes the advantages of feedback control and classical ILC and can also be viewed as online-offline ILC. It is theoretically proven that the boundednesses of the state error and the final tracking error are guaranteed in the presence of uncertainty, disturbance, and initialization error of the nonlinear systems. The convergence rate is adjustable by the adoption of the switching gains in the iteration domain. Simulation experiments are conducted for trajectory tracking control of a nonlinear system and a robotic system. The results show that fast convergence and small tracking error bounds can be observed by using the SPD-PD-type ILC.

scholarly journals Deep Learning-Based Trajectory Tracking Control forUnmanned Surface Vehicle

2021 ◽  
Vol 2021 ◽  
pp. 1-22
Author(s):  
Wenli Sun ◽  
Xu Gao
Keyword(s):  
Deep Learning ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Autonomous Navigation ◽  
Tracking Error ◽  
Interval Programming ◽  
Trajectory Tracking Control ◽  
Guidance Law ◽  
Effect Level ◽  
Operating Suite

Trajectory tracking control based on waypoint behavior is a promising way for unmanned surface vehicle (USV) to achieve autonomous navigation. This study is aimed at the guidance progress in the kinematics; the artificial intelligence method of deep learning is adopted to improve the trajectory tracking level of USV. First, two deep neural network (DNN) models are constructed to evaluate navigation effects and to estimate guidance law parameters in real time, respectively. We then pretrain the DNN using a Gaussian–Bernoulli restricted Boltzmann machine to further improve the accuracy of predicting navigation effect. Finally, two DNNs are connected in parallel with the control loop of USV to provide predictive supervision and auxiliary decision making for traditional control methods. This kind of parallel way conforms to the ship manipulation of habit. Furthermore, we develop a new application on the basis of Mission Oriented Operating Suite Interval Programming named “pDeepLearning.” It can predict the navigation effect online by DNN and adjust the guidance law parameters according to the effect level. The experimental results show that, compared with the original waypoint behavior of USV, the prediction model proposed in this study reduces the trajectory tracking error by 19.0% and increases the waypoint behavior effect level.

Designing and implementing a plug-in type repetitive controller for a redundantly actuated ankle rehabilitation robot

2018 ◽  
Vol 232 (5) ◽  
pp. 592-607 ◽  
Author(s):  
Mustafa Sinasi Ayas ◽  
Ismail Hakki Altas
Keyword(s):  
Trajectory Tracking ◽  
Tracking Control ◽  
Search Algorithm ◽  
External Disturbance ◽  
Tracking Error ◽  
Rehabilitation Robot ◽  
Trajectory Tracking Control ◽  
Intensive Training ◽  
Ankle Rehabilitation ◽  
Repetitive Controller

This article is focused on increasing the tracking performance of a developed ankle rehabilitation robot subject to external disturbance. A plug-in-type repetitive controller cascaded to a proportional–integral–derivative controller is designed and implemented in order to make improvement in the tracking ability of the parallel mechanism while performing the common range of motion exercises which are intensive training exercises in ankle rehabilitation. First, the trajectory tracking control is simply implemented by the PID controllers, the parameters of which are optimally tuned using Cuckoo search algorithm. Then, the designed RC is plugged into the system and trajectory tracking control is carried out. Performance measurements of the PID controller and plug-in RC controller are estimated using error-based performance methods and considerable improvements are observed in attenuating the external disturbance and decreasing the tracking error when the plug-in RC is implemented.

scholarly journals Prescribed performance adaptive fault-tolerant trajectory tracking control for an ocean bottom flying node

2019 ◽  
Vol 16 (3) ◽  
pp. 172988141984194 ◽  
Author(s):  
Hongde Qin ◽  
Zheyuan Wu ◽  
Yanchao Sun ◽  
Yushan Sun
Keyword(s):  
Trajectory Tracking ◽  
Tracking Control ◽  
Control Method ◽  
Tracking Error ◽  
Approximation Error ◽  
Ocean Current ◽  
Ocean Bottom ◽  
Model Uncertainties ◽  
Trajectory Tracking Control ◽  
Prescribed Performance

The ocean bottom flying node is a novel autonomous underwater vehicle that explores the oil and gas resources in deep water. Thousands of the ocean bottom flying nodes track different predefined trajectories arriving at target points in a small ocean area, respectively. A class of prescribed performance adaptive trajectory tracking control method is investigated for the ocean bottom flying node trajectory tracking problem with ocean current disturbances, model uncertainties as well as thruster faults. Based on a predefined performance function and an error transformation, the ocean bottom flying node trajectory tracking error is restricted to prespecified bounds to ensure a desired transient and steady response. Radial basis function neural network is used to approximate the general uncertainty caused by ocean current disturbances, model uncertainties, and thruster faults. Further, the upper bound of approximation error is estimated by an adaptive law. Using the adaptive laws, we propose a prescribed performance adaptive trajectory tracking controller. The simulation examples on an ocean bottom flying node system show that the proposed control scheme can compensate for the effect of the general uncertainty while obtaining the fast transient process and expected trajectory tracking accuracy.

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