scholarly journals Intelligent Vehicle Lateral Control Method Based on Feedforward + Predictive LQR Algorithm

Actuators ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 228
Author(s):  
Tao Yang ◽  
Ziwen Bai ◽  
Zhiqiang Li ◽  
Nenglian Feng ◽  
Liqing Chen
Keyword(s):  
Control System ◽  
Vehicle Dynamics ◽  
Error Control ◽  
Control Method ◽  
Tracking Error ◽  
Error Model ◽  
Intelligent Vehicle ◽  
Lateral Control ◽  
Perception System ◽  
Lqr Controller

Aiming at the problems of control stability of the intelligent vehicle lateral control method, single test conditions, etc., a lateral control method with feedforward + predictive LQR is proposed, which can better adapt to the problem of intelligent vehicle lateral tracking control under complex working conditions. Firstly, the vehicle dynamics tracking error model is built by using the two degree of freedom vehicle dynamics model, then the feedforward controller, predictive controller and LQR controller are designed separately based on the path tracking error model, and the lateral control system is built. Secondly, based on the YOLO-v3 algorithm, the environment perception system under the urban roads is established, and the road information is collected, the path equation is fitted and sent to the control system. Finally, the joint simulation is carried out based on CarSim software and a Matlab/Simulink control model, and tested combined with hardware in the loop test platform. The results of simulation and hardware-in-loop test show that the transverse controller with feedforward + predictive LQR can effectively improve the accuracy of distance error control and course error control compared with the transverse controller with feedforward + LQR control, LQR controller and MPC controller on the premise that the vehicle can track the path in real time.

Control Algorithm Study on Cantilever Machine Teaching-Playback

2014 ◽  
Vol 635-637 ◽  
pp. 1212-1215
Author(s):  
Ruo Han Liu ◽  
Chun Hua Li
Keyword(s):  
Control System ◽  
Intelligent Control ◽  
Control Algorithm ◽  
Control Method ◽  
Tracking Error ◽  
Cutting Parameters ◽  
Configuration Software ◽  
Monitoring Site ◽  
Software Monitoring ◽  
Operation Panel

In order to realize intelligent control, the cutting trajectory of TBM research machine adopts the principle of teaching and reappearing cutting trajectory control method, combining with the control system of teaching and reappearing and SIMATIC C7, operation interface is realized by using configuration software monitoring, monitoring site visually through the operation panel parameters change, timely adjust the cutting parameters. The experimental results show that the tracking error within the scope of the permit. The method to improve the intelligence of machine cutting control provides a reference basis.

Active Vibration Control Method for Space Mechanical Structure with Piezostacks

2013 ◽  
Vol 198 ◽  
pp. 433-438
Author(s):  
Andrzej Piotr Koszewnik
Keyword(s):  
Control System ◽  
Vibration Control ◽  
Control Method ◽  
Active Vibration Control ◽  
Three Dimensional ◽  
Control Laws ◽  
Distributed Parameters ◽  
Short Period ◽  
Lqr Controller ◽  
Active Vibration

Mechanical structures are spatial, three-dimensional (3D) systems of distributed parameters. They present quite complicated plants, if methods of control systems theory are applied. The design process of the vibration control system for such plants is extremely difficult and requires an extensive heuristic knowledge. The subject of the control system is to eliminate the vibrations of the free end at the plane parallel to the foundation Similar problems are met, when the stabilization of robot arms, antennas, satellite solar batteries or slender skyscrapers is considered. In the paper we have presented the 3D bar structure with sticked parallel two piezo-stacks into bars. Recall piezo-elements are actuators, but sensors are two eddy-current sensors located in near free end the structure in perpendicular directions X and Y. Thus the whole structure is TITO (Two Input Two Output) system. For such system the control law was designed with used LQR controller. Above controller was designed for coupled and decoupled system also. In both case a correct damp and very short period of the vibration were criteria to choose the controller parameters. All investigations were carried out in two steps. In the first step control laws were designed in computer simulation. In the second step these control laws were verified experimentally on the laboratory stand by using DSP. Finally, desired control laws were compared.

Research on Lateral Control System for Intelligent Vehicle Based on Magnetic Markers Guidance

2014 ◽  
Vol 543-547 ◽  
pp. 1340-1343
Author(s):  
Fei Shen ◽  
Feng Luo
Keyword(s):  
Control System ◽  
Intelligent Vehicles ◽  
Intelligent Vehicle ◽  
Vehicle Speed ◽  
Lateral Control ◽  
Good Reliability ◽  
Magnetic Markers ◽  
High Control ◽  
Control Accuracy ◽  
Real Vehicle

This paper presents the development of lateral control system for intelligent vehicle based on magnetic markers guidance. A lateral controller based on fuzzy logic is designed for intelligent vehicle that is non-linear controlled object. Simulation results show that the proposed control algorithm can ensure tracking reference path of intelligent vehicles accurately. The function of the system is finally verified by real vehicle experiment and the results show that the control system has high control accuracy, real-time performance and good reliability at an acceptable vehicle speed.

scholarly journals Robust Prescribed Performance Control of Uncertain Vehicle Lateral System under State Constraints

2021 ◽  
Vol 2083 (3) ◽  
pp. 032029
Author(s):  
Jing Yu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Lyapunov Function ◽  
Finite Time ◽  
Tracking Control ◽  
State Constraints ◽  
Tracking Error ◽  
Lateral Control ◽  
Barrier Lyapunov Function ◽  
The Neural Network

Abstract In the study of the zero-error tracking control problem for vehicle lateral control systems under full-state constraints and nonparametric uncertainties, the zero-error tracking control problem is presented in this paper. A neural adaptive tracking control scheme is proposed by combining the error transformation of the vehicle lateral control system with the barrier Lyapunov function, which realizes that the tracking error of the vehicle lateral control converges to a prescribed compact set at a controllable or specified convergence rate in a specified finite time. The scheme has the following significant characteristics: 1) Based on the Nussbaum gain, the preset new energy finite-time control algorithm, the tracking error of the vehicle lateral control system with non-parametric uncertainty and external disturbance decreases to zero with t → ∞. In addition, it also has the control ability to cope with the presence or even unknown moment of inertia of the system. 2) Barrier Lyapunov function (BLF) ensures the bounded input of the neural network during the whole system envelope, and ensures the stable learning and approximation of the neural network. Furthermore, the bounded stability of the closed-loop system is proved by Lyapunov analysis. Finally, the effectiveness and superiority of the proposed control method are verified by simulation.

scholarly journals A Lateral Control Method of Intelligent Vehicle Based on Fuzzy Neural Network

2014 ◽  
Vol 7 (1) ◽  
pp. 296209 ◽  
Author(s):  
Linhui Li ◽  
Hongxu Wang ◽  
Jing Lian ◽  
Xinli Ding ◽  
Wenping Cao
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Intelligent Vehicle ◽  
Lateral Control ◽  
Fuzzy Neural

scholarly journals Application of Immune Feedback Control Algorithm Based on BP Network Approximation in Intelligent Vehicle Steering System

2019 ◽  
Vol 15 (09) ◽  
pp. 71
Author(s):  
Yanting Lan ◽  
Xiaodong Chen
Keyword(s):  
Feedback Control ◽  
High Speed ◽  
Control Method ◽  
Tracking Error ◽  
Steering System ◽  
Correction Function ◽  
Horizontal Wind ◽  
Intelligent Vehicle ◽  
External Interference ◽  
Bp Network

<p align="justify"><strong>Abstract—</strong>Steering system of intelligent vehicle is very difficult to execute precise control in driving due to many known and unknown disturbances. Therefore, design of steering algorithm has to be feasible for uncertain external interference, such as uneven pavement and horizontal wind, with automatic correction function for changes in the location of intelligent cars caused by road tilt and horizontal wind. As traditional PID control is impossible to meet the control requirements, according to mechanism of biological immunity, an immune feedback control method was proposed to approximate nonlinearity of T and B cells by BP network. Joint simulations of serpentine condition were carried out by Carsim and Simulink. The results show that trajectory tracking error, operating load, risk of rollover and slip and control stability of control system are synthetically evaluated. The comprehensive evaluation index of automatic steering algorithm under high speed operation is fairly advantageous. Experimental results also show that the algorithm effectively realizes tracking of intelligent vehicle for marking lines and avoidance of obstacles.</p>

scholarly journals Method of Cooperative Formation Control for Underactuated USVS Based on Nonlinear Backstepping and Cascade System Theory

2021 ◽  
Vol 28 (1) ◽  
pp. 149-162
Author(s):  
Zaopeng Dong ◽  
Yang Liu ◽  
Hao Wang ◽  
Tao Qin
Keyword(s):  
System Theory ◽  
Control System ◽  
Formation Control ◽  
Control Method ◽  
Tracking Error ◽  
Lyapunov Stability Theory ◽  
State Variables ◽  
State Transformation ◽  
Cascade System ◽  
Control Laws

Abstract This paper presents a method for the cooperative formation control of a group of underactuated USVs. The problem of formation control is first converted to one of stabilisation control of the tracking errors of the follower USVs using system state transformation design. The followers must keep a fixed distance from the leader USV and a specific heading angle in order to maintain a certain type of formation. A global differential homeomorphism transformation is then designed to create a tracking error system for the follower USVs, in order to simplify the description of the control system. This makes the complex formation control system easy to analyse, and allows it to be decomposed into a cascaded system. In addition, several intermediate state variables and virtual control laws are designed based on nonlinear backstepping, and actual control algorithms for the follower USVs to control the surge force and yaw moment are presented. A global system that can ensure uniform asymptotic stability of the USVs’ cooperative formation control is achieved by combining Lyapunov stability theory and cascade system theory. Finally, several simulation experiments are carried out to verify the validity, stability and reliability of our cooperative formation control method.

scholarly journals Accuracy Improvement of Robot Arm Trajectory based on Adaptive Neural Network Algorithm

CONVERTER ◽  
2021 ◽  
pp. 709-715
Author(s):  
Peibo Li, Peixing Li, Chen Yanpeng
Keyword(s):  
Neural Network ◽  
Control System ◽  
Control Method ◽  
Tracking Error ◽  
Network Control ◽  
Neural Network Control ◽  
Robot Arm ◽  
Adaptive Neural Network ◽  
Adaptive Neural Network Control ◽  
Simulation Results

An adaptive neural network control method was proposed to solve the problems such as unstable motion and large trajectory tracking error when the robot arm was disturbed by the external environment.The dynamic equations of the manipulator were given and the dynamic characteristics of the manipulator were studied by using the positive feedback neural network. Then the adaptive neural network control system was designed, and the stability and convergence of the closed-loop system were proved by the Lyapunov function. Later, the model diagram of the robot arm was established, and the dynamics parameters of the manipulator were simulated by MATLAB /Simulink software.At the same time, they were compared with the simulation results of the PID control system for analysis.The simulation results showed that the trajectory tracking error and input torque fluctuation were smaller when the trajectory of the robot arm was disturbed by the external world. When adopting the control method of the adaptive neural network, the robot arm could improve the control precision of the trajectory, thus reducing the jitter of the robot arm motion.

scholarly journals Neural Network Control for Trajectory Tracking and Balancing of a Ball-Balancing Robot with Uncertainty

2021 ◽  
Vol 11 (11) ◽  
pp. 4739
Author(s):  
Hyo-Geon Jang ◽  
Chang-Ho Hyun ◽  
Bong-Seok Park
Keyword(s):  
Neural Network ◽  
Control System ◽  
Trajectory Tracking ◽  
Control Method ◽  
Tracking Error ◽  
System Structure ◽  
Underactuated System ◽  
Dynamic Surface ◽  
Comparison Results ◽  
Balancing Robot

In this paper, a neural-network-based control method to achieve trajectory tracking and balancing of a ball-balancing robot with uncertainty is presented. Because the ball-balancing robot is an underactuated system and has nonlinear couplings in the dynamic model, it is challenging to design a controller for trajectory tracking and balancing. Thus, various approaches have been proposed to solve these problems. However, there are still problems such as the complex control system and instability. Therefore, the objective of this paper was to propose a solution to these problems. To this end, we developed a virtual angle-based control scheme. Because the virtual angle was used as the reference angle to achieve trajectory tracking while keeping the balance of the ball-balancing robot, we could solve the underactuation problem using a single-loop controller. The radial basis function networks (RBFNs) were employed to compensate uncertainties, and the controller was designed using the dynamic surface control (DSC) method. From the Lyapunov stability theory, it was proven that all errors of the closed-loop control system were uniformly ultimately bounded. Therefore, the control system structure was simple and ensured stability in achieving simultaneous trajectory tracking and balancing of the ball-balancing robot with uncertainty. Finally, the simulation results are given to verify the performance of the proposed controller through comparison results. As a result, the proposed method showed a 19.2% improved tracking error rate compared to the existing method.

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