A practical trajectory tracking control of autonomous vehicles using linear time-varying MPC method

2021 ◽  
pp. 095440702110229
Author(s):  
Hui Pang ◽  
Nan Liu ◽  
Chuan Hu ◽  
Zijun Xu
Keyword(s):  
Trajectory Tracking ◽  
Autonomous Vehicles ◽  
Linear Time ◽  
Tracking System ◽  
Rapid Development ◽  
Tracking Error ◽  
Kinematic Model ◽  
Computational Cost ◽  
Error Model ◽  
Time Varying

With the rapid development and implementation of autonomous vehicles (AVs), the simultaneous and accurate trajectory tracking problem for such AVs has become a popular research topic. This paper proposes a comprehensive linear time-varying model predictive controller (LTV-MPC) design for a type of AV, aiming to achieve good trajectory tracking in a practical driving scenario. First, a two-degree-of-freedom kinematic model of an AV is established. Next, an error model of the AV’s trajectory tracking system is constructed using linear time-varying theory. A successive linearization is introduced to linearize the nonlinear tracking error model, and a quadratic programming optimization problem is then formulated. Thus, the control sequence for this AV is incorporated into the predictive control framework, and the desired controller can be solved with a relatively higher computational efficiency and lower computational cost. Finally, the effectiveness and performance of the proposed controller are validated via a comparison of simulations conducted using MATLAB software and experiments conducted using a self-established test platform. The results demonstrate that the proposed LTV-MPC method can track the prescribed reference road trajectories with high precision and stability for an AV under various driving conditions.

Iterative learning algorithm with a quadratic criterion for linear time-varying systems

2002 ◽  
Vol 216 (3) ◽  
pp. 309-316 ◽  
Author(s):  
S N Huang ◽  
K K Tan ◽  
T H Lee
Keyword(s):  
Learning Algorithm ◽  
Linear Time ◽  
Tracking Error ◽  
Iterative Learning ◽  
Tracking Performance ◽  
Time Varying ◽  
Control Scheme ◽  
Time Varying Systems ◽  
Simulation Results ◽  
Learning Law

A novel iterative learning controller for linear time-varying systems is developed. The learning law is derived on the basis of a quadratic criterion. This control scheme does not include package information. The advantage of the proposed learning law is that the convergence is guaranteed without the need for empirical choice of parameters. Furthermore, the tracking error on the final iteration will be a class K function of the bounds on the uncertainties. Finally, simulation results reveal that the proposed control has a good setpoint tracking performance.

scholarly journals Trajectory Tracking Control for WMRs with the Time-Varying Longitudinal Slippage Based on a New Adaptive SMC Method

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Zhi Li ◽  
Bo You ◽  
Liang Ding ◽  
Haibo Gao ◽  
Fengxiang Huang
Keyword(s):  
Trajectory Tracking ◽  
Control Method ◽  
Sliding Mode ◽  
Kinematic Model ◽  
Time Varying ◽  
Wheeled Mobile Robots ◽  
Trajectory Tracking Control ◽  
Complete Control ◽  
Loop Control ◽  
Adaptive Smc

Wheeled mobile robots (WMRs) in real complex environments such as on extraterrestrial planets are confronted with uncertain external disturbances and strong coupling of wheel-ground interactions while tracking commanded trajectories. Methods based on sliding mode control (SMC) are popular approaches for these situations. Traditional SMC has some potential problems, such as slow convergence, poor robustness, and excessive output chattering. In this paper, a kinematic-based feed-forward control model is designed for WMRs with longitudinal slippage and applied to the closed-loop control system for active compensation of time-varying slip rates. And a new adaptive SMC method is proposed to guide a WMR in trajectory tracking missions based on the kinematic model of a general WMR. This method combines the adaptive control method and a fast double-power reaching law with the SMC method. A complete control loop with active slip compensation and adaptive SMC is thus established. Simulation results show that the proposed method can greatly suppress chattering and improve the robustness of trajectory tracking. The feasibility of the proposed method in the real world is demonstrated by experiments with a skid-steered WMR on the loose-soil terrain.

scholarly journals An Autonomous Path Controller in a System on Chip for Shrimp Robot

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 441 ◽  
Author(s):  
Sergio Barrios-dV ◽  
Michel Lopez-Franco ◽  
Jorge D. Rios ◽  
Nancy Arana-Daniel ◽  
Carlos Lopez-Franco ◽  
...  
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Trajectory Tracking ◽  
Neural Control ◽  
Tracking System ◽  
Kinematic Model ◽  
System On Chip ◽  
Optimal Controller ◽  
Real Time System ◽  
On Chip

This paper presents a path planning and trajectory tracking system for a BlueBotics Shrimp III®, which is an articulate mobile robot for rough terrain navigation. The system includes a decentralized neural inverse optimal controller, an inverse kinematic model, and a path-planning algorithm. The motor control is obtained based on a discrete-time recurrent high order neural network trained with an extended Kalman filter, and an inverse optimal controller designed without solving the Hamilton Jacobi Bellman equation. To operate the whole system in a real-time application, a Xilinx Zynq® System on Chip (SoC) is used. This implementation allows for a good performance and fast calculations in real-time, in a way that the robot can explore and navigate autonomously in unstructured environments. Therefore, this paper presents the design and implementation of a real-time system for robot navigation that integrates, in a Xilinx Zynq® System on Chip, algorithms of neural control, image processing, path planning, and inverse kinematics and trajectory tracking.

Iterative learning identification and control for point-to-point tracking of linear time-varying systems with unknown parameters and stochastic noise

2017 ◽  
Vol 40 (13) ◽  
pp. 3834-3845 ◽  
Author(s):  
Yan Geng ◽  
Xiaoe Ruan
Keyword(s):  
Linear Time ◽  
Tracking Error ◽  
Iterative Learning ◽  
Time Varying ◽  
Unknown Parameters ◽  
Point Tracking ◽  
Time Varying Systems ◽  
System Output ◽  
Point To Point ◽  
And Control

In this paper, an interactive iterative learning identification and control (ILIC) scheme is developed for a class of discrete-time linear time-varying systems with unknown parameters and stochastic noise to implement point-to-point tracking. The identification is to iteratively estimate the unknown system parameter matrix by adopting the gradient-type technique for minimizing the distance of the system output from the estimated system output, whilst the control law is to iteratively upgrade the current control input with the current point-to-point tracking error scaled by the estimated system parameter matrix. Thus, the iterative learning identification and the iterative learning control are scheduled in an interactive mode. By means of norm theory, the boundedness of the discrepancy between the system matrix estimation and the real one is derived, whilst, by the manner of the statistical technique, it is conducted that the mathematical expectation of the tracking error monotonically converges to nullity and the variance of the tracking error is bounded. Numerical simulations exhibit the validity and effectiveness of the proposed ILIC scheme.

A Time-Varying Generalized Minimum Variance Controller for Servo Application

2013 ◽  
Vol 278-280 ◽  
pp. 1403-1408 ◽  
Author(s):  
Zheng Li
Keyword(s):  
Performance Index ◽  
Linear Time ◽  
Tracking Error ◽  
Error Variance ◽  
Minimum Variance ◽  
Time Varying ◽  
Output Tracking ◽  
Time Varying Systems ◽  
Control Objective ◽  
Process Disturbances

A generalized minimum variance controller is developed for linear time-varying systems for servo applications. The plants to be controlled is described using a SISO CARMA model and the control objective is to minimize a generalized minimum variance performance index, where the output tracking error variance is penalized by squared incremental of plant input in order to reduce fluctuation in plant input and attenuate process disturbances.

Iterative Learning Identification/Iterative Learning Control for Linear Time-Varying Systems

2016 ◽  
Vol 138 (10) ◽  
Author(s):  
Nanjun Liu ◽  
Andrew Alleyne
Keyword(s):  
Iterative Learning Control ◽  
Linear Time ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Parameter Estimates ◽  
Time Varying ◽  
Fixed Parameter ◽  
Time Varying Systems ◽  
And Control

This paper integrates a previously developed iterative learning identification (ILI) (Liu, N., and Alleyne, A. G., 2016, “Iterative Learning Identification for Linear Time-Varying Systems,” IEEE Trans. Control Syst. Technol., 24(1), pp. 310–317) and iterative learning control (ILC) algorithms (Bristow, D. A., Tharayil, M., and Alleyne, A. G., 2006, “A Survey of Iterative Learning Control,” IEEE Control Syst. Mag., 26(3), pp. 96–114), into a single norm-optimal framework. Similar to the classical separation principle in linear systems, this work provides conditions under which the identification and control can be combined and guaranteed to converge. The algorithm is applicable to a class of linear time-varying (LTV) systems with parameters that vary rapidly and analysis provides a sufficient condition for algorithm convergence. The benefit of the integrated ILI/ILC algorithm is a faster tracking error convergence in the iteration domain when compared with an ILC using fixed parameter estimates. A simple example is introduced to illustrate the primary benefits. Simulations and experiments are consistent and demonstrate the convergence speed benefit.

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