tracking controller
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2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Zhijun Fu ◽  
Yan Lu ◽  
Fang Zhou ◽  
Yaohua Guo ◽  
Pengyan Guo ◽  
...  

This paper deals with adaptive nonlinear identification and trajectory tracking problem for model free nonlinear systems via parametric neural network (PNN). Firstly, a more effective PNN identifier is developed to obtain the unknown system dynamics, where a parameter error driven updating law is synthesized to ensure good identification performance in terms of accuracy and rapidity. Then, an adaptive tracking controller consisting of a feedback control term to compensate the identified nonlinearity and a sliding model control term to deal with the modeling error is established. The Lyapunov approach is synthesized to ensure the convergence characteristics of the overall closed-loop system composed of the PNN identifier and the adaptive tracking controller. Simulation results for an AFS/DYC system are presented to confirm the validity of the proposed approach.


Symmetry ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 31
Author(s):  
Jichang Ma ◽  
Hui Xie ◽  
Kang Song ◽  
Hao Liu

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).


Actuators ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 308
Author(s):  
Lejun Wang ◽  
Siyu Chen ◽  
Pan Zhang ◽  
Jinhua She ◽  
Xuzhi Lai

This paper presents a simple control method on the basis of the trajectory planning for vertical Acrobot to accomplish the control goal of moving the system from the downward initial position (DIP) and steadying the system at the upward target position (UTP). First, for the active link, we frame a trajectory that contains some adjustable parameters. Along the framed trajectory, we can make the active link stabilize at its end angle from its start angle. Furthermore, we change the trajectory parameters to make the passive link also arrive at the zone near the end angle. Next, we devise a PD-based tracking controller to track this planned trajectory. In this way, the vertical Acrobot is swung up to a small zone near the UTP. Then, from the approximate linear model at the UTP, we devise a stabilization controller to stabilize the vertical Acrobot at the UTP. Finally, we implement the simulation to show the validity of the proposed method.


Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2886
Author(s):  
Changshun Wang ◽  
Dan Wang ◽  
Weigang Pan ◽  
Huang Zhang

This paper presents an output-based tracking controller for a class of car-like mobile robot (CLMR) subject to slipping and skidding. The slipping and skidding are regarded as external disturbances, and an event-triggered extended state observer (ET-ESO) is utilized to recover the velocities as well as to estimate the uncertainties and disturbances. The constrained longitudinal velocity is established, conforming to the traffic flow theory on the kinematic level. The velocity control law and heading angle control law are developed on the dynamic level, respectively. The input to state stability (ISS) of the closed-loop system is analyzed via cascade theory. Simulation results are given to demonstrate the effectiveness of the proposed tracking controller for CLMR subject to slipping and skidding.


2021 ◽  
Vol 11 (22) ◽  
pp. 10634
Author(s):  
Abdullah Aldughaiyem ◽  
Yasser Bin Salamah ◽  
Irfan Ahmad

In recent years, control design for unmanned systems, especially a tractor–trailer system, has gained popularity among researchers. The emergence of such interest is caused by the potential reduction in cost and shortage of number of workers and labors. Two industries will benefit from the advancements of these types of systems: agriculture and cargo. By using the unmanned tractor–trailer system, harvesting and cultivating plants will become a safe and easy task. It will also cause a reduction in cost which in turn reduces the price on the end consumers. On the other hand, by using the unmanned tractor–trailer system in the cargo industry, shipping cost and time for the item delivery will be reduced. The work presented in this paper focuses on the development of a path tracking and a cascaded controller to control a tractor–trailer in reverse motion. The path tracking controller utilizes the Frenet–Serret frame to control the kinematics of the tractor–trailer system on a desired path, while the cascade controller’s main objective is to stabilize the system and to perform commands issued by the path tracker. The controlled parameters in this proposed design are the lateral distance to a path, trailer’s heading angel, articulated angel, and articulated angle’s rate. The main goal of such controller is to follow a path while the tractor–trailer system is moving in reverse and controlling the stability of the articulated vehicle to prevent the occurrence of a jackknife incident (uncontrolled state). The proposed controller has been tested in a different scenario where a successful implementation has been shown.


Axioms ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 298
Author(s):  
Yu-Ting Chen ◽  
Chian-Song Chiu ◽  
Ya-Ting Lee

Mobile robots are widely used in many applications, while various types of mobile robots and their control researches have been proposed in literature. Since swarm robots have higher flexibility and capacity for teamwork, this paper presents a grey estimator-based tracking controller for formation trajectory tracking of swarm robots. First, wheel-type mobile robots are used and modeled for the controller design. Then, a grey dynamic estimator is developed to estimate the environmental disturbance and model uncertainty for linear feedback compensation. As a result, the asymptotic trajectory tracking is assured, so that the application on the swarm robot formation is achieved for a multi-agent system. The computational complexity is slightly reduced by the design. Finally, in order to verify the reliability of swarm robot formation, several types of formation are maintained by the grey estimator-based feedback linearization controller.


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