Path-tracking control of underactuated ships under tracking error constraints

2015 ◽  
Vol 14 (4) ◽  
pp. 343-354 ◽  
Author(s):  
Khac Duc Do
Keyword(s):  
Tracking Control ◽  
Tracking Error ◽  
Path Tracking

scholarly journals Self-Optimizing Path Tracking Controller for Intelligent Vehicles Based on Reinforcement Learning

Symmetry ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 31
Author(s):  
Jichang Ma ◽  
Hui Xie ◽  
Kang Song ◽  
Hao Liu
Keyword(s):  
Reinforcement Learning ◽  
Pid Control ◽  
Tracking Control ◽  
Tracking Error ◽  
Path Tracking ◽  
Pid Controllers ◽  
Model Parameters ◽  
Control Parameters ◽  
Tracking Controller ◽  
Complex Trajectories

The path tracking control system is a crucial component for autonomous vehicles; it is challenging to realize accurate tracking control when approaching a wide range of uncertain situations and dynamic environments, particularly when such control must perform as well as, or better than, human drivers. While many methods provide state-of-the-art tracking performance, they tend to emphasize constant PID control parameters, calibrated by human experience, to improve tracking accuracy. A detailed analysis shows that PID controllers inefficiently reduce the lateral error under various conditions, such as complex trajectories and variable speed. In addition, intelligent driving vehicles are highly non-linear objects, and high-fidelity models are unavailable in most autonomous systems. As for the model-based controller (MPC or LQR), the complex modeling process may increase the computational burden. With that in mind, a self-optimizing, path tracking controller structure, based on reinforcement learning, is proposed. For the lateral control of the vehicle, a steering method based on the fusion of the reinforcement learning and traditional PID controllers is designed to adapt to various tracking scenarios. According to the pre-defined path geometry and the real-time status of the vehicle, the interactive learning mechanism, based on an RL framework (actor–critic—a symmetric network structure), can realize the online optimization of PID control parameters in order to better deal with the tracking error under complex trajectories and dynamic changes of vehicle model parameters. The adaptive performance of velocity changes was also considered in the tracking process. The proposed controlling approach was tested in different path tracking scenarios, both the driving simulator platforms and on-site vehicle experiments have verified the effects of our proposed self-optimizing controller. The results show that the approach can adaptively change the weights of PID to maintain a tracking error (simulation: within ±0.071 m; realistic vehicle: within ±0.272 m) and steering wheel vibration standard deviations (simulation: within ±0.04°; realistic vehicle: within ±80.69°); additionally, it can adapt to high-speed simulation scenarios (the maximum speed is above 100 km/h and the average speed through curves is 63–76 km/h).

Path tracking control of electromechanical micro-positioner by considering control effort of the system

2020 ◽  
pp. 095965182095327
Author(s):  
Mohammad Reza Gharib ◽  
Ali Koochi ◽  
Mojtaba Ghorbani
Keyword(s):  
Tracking Control ◽  
Tracking Error ◽  
Path Tracking ◽  
Lumped Parameter Model ◽  
Proportional Integral Derivative ◽  
Control Effort ◽  
Proportional Integral Derivative Controller ◽  
Proportional Integral ◽  
Micro Actuators ◽  
And Control

Position controlling with less overshoot and control effort is a fundamental issue in the design and application of micro-actuators such as micro-positioner. Also, tracking a considered path is very crucial for some particular applications of micro-actuators such as surgeon robots. Herein, a proportional–integral–derivative controller is designed using a feedback linearization technique for path tracking control of a cantilever electromechanical micro-positioner. The micro-positioner is simulated based on a 1-degree-of-freedom lumped-parameter model. Three different paths are considered, and the capability of the designed controller on the path tracking with lower error and control effort is investigated. The obtained results demonstrate the efficiency of the designed proportional–integral–derivative controller not only for reducing the tracking error but also for decreasing the control effort.

scholarly journals Self-Adaptive Path Tracking Control for Mobile Robots under Slippage Conditions Based on an RBF Neural Network

Algorithms ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 196
Author(s):  
Yiting Kang ◽  
Biao Xue ◽  
Riya Zeng
Keyword(s):  
Neural Network ◽  
Mobile Robots ◽  
Tracking Control ◽  
Dynamic Models ◽  
Rbf Neural Network ◽  
Tracking Error ◽  
Path Tracking ◽  
Neuron Network ◽  
Control Framework ◽  
Self Adaptive

Wheeled mobile robots are widely implemented in the field environment where slipping and skidding may often occur. This paper presents a self-adaptive path tracking control framework based on a radial basis function (RBF) neural network to overcome slippage disturbances. Both kinematic and dynamic models of a wheeled robot with skid-steer characteristics are established with position, orientation, and equivalent tracking error definitions. A dual-loop control framework is proposed, and kinematic and dynamic models are integrated in the inner and outer loops, respectively. An RBF neutral network is employed for yaw rate control to realize adaptability to longitudinal slippage. Simulations employing the proposed control framework are performed to track snaking and a DLC reference path with slip ratio variations. The results suggest that the proposed control framework yields much lower position and orientation errors compared with those of a PID and a single neuron network (SNN) controller. It also exhibits prior anti-disturbance performance and adaptability to longitudinal slippage. The proposed control framework could thus be employed for autonomous mobile robots working on complex terrain.

Path Tracking Control of Micro-Tracked Mobile Robot

2014 ◽  
Vol 644-650 ◽  
pp. 265-271 ◽  
Author(s):  
Jian Gao ◽  
Shi Long Zhang
Keyword(s):  
Mobile Robot ◽  
Tracking Control ◽  
Tracking Error ◽  
Kinematic Model ◽  
Path Tracking ◽  
Positioning Accuracy ◽  
And Control ◽  
Tracked Mobile Robot ◽  
The Given ◽  
Motor Model

The positioning accuracy of tracked mobile robot is low because of sliding in steering process. Taking the micro-tracked mobile robot as the platform, the interface force between tracks and ground was analyzed, and the motor model, kinematic model and dynamic model were established further. A tracking error controller was built based on the tracking error equations, and the co-simulation of mechanical and control system was applied to predict the robot’s trajectory. That controller was applied on a small tracked mobile robot designed by the authors’ laboratory, and the path tracking experiments with and without obstacles had been done. The results show that the robot can accurately track the given path, whether there are obstacles or not.

Adaptive Preview based Control System for Unmanned Vehicle Path Tracking

2021 ◽  
Vol 13 (4) ◽  
Author(s):  
Wei Zhou
Keyword(s):  
Control System ◽  
Root Mean Square ◽  
Tracking Control ◽  
Tracking Error ◽  
Path Tracking ◽  
Mean Square ◽  
Steering Control ◽  
Deviation Angle ◽  
Control Module ◽  
Unmanned Vehicle

The unmanned vehicle control technology is constantly updated. How to accurately track the path has become a key issue. For this reason, a path tracking control system for an unmanned vehicle is designed. The system control module solves the lateral and longitudinal control problems of the unmanned vehicle. The preview compensation controller corrects the deviation of the vehicle approaching the normal track. The steering control module changes the direction of the vehicle based on the motor command signal. In the software part, the kinematics model of the unmanned vehicle in the plane rectangular coordinate system is built. In this model, the steering geometric track is constructed based on the Stanley algorithm. Track tracking preview model can adjust the preview adaptively according to the lateral deviation and heading angle deviation of the vehicle and gets the adaptive preview point. The simulation results show that the maximum absolute value of preview deviation angle, the root mean square of preview deviation angle and the root mean square of tracking error are lower. The effect of path tracking control is better. The effect of path tracking control is less affected by vehicle speed and road environment.

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