Trajectory Tracking Control of Autonomous Ground Vehicles Using Adaptive Learning MPC

Zhang, Kunwu; Sun, Qi; Shi, Yang

doi:10.1109/tnnls.2020.3048305

Cited by 67 publications

(29 citation statements)

References 31 publications

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“…Current results for adaptive MPC problems with parametric uncertainties are relatively limited [18][19][20]. Conventional adaptive MPC is based on certainty-equivalence (CE) theory [21,22]. The idea of CE theory is that the most recent parameter estimation is used as if the prediction model is exact, neglecting the uncertainty and the prediction error of the model parameters.…”

Section: Introductionmentioning

confidence: 99%

Adaptive dual model predictive control for linear systems with parametric uncertainties

Mengting

Ning

et al. 2022

IET Control Theory & Appl

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Adaptive dual model predictive control (DMPC) for linear systems with constant parametric uncertainties is investigated in this paper. In particular, chance constraints on output and input are considered. The online set‐membership identification and recursive least squares are utilized for shrinking the uncertain parameter set and getting the estimation point of the parameters. The dual effect represented by the parameters' prediction error is considered in the receding horizon optimization problem for easing the impact of the uncertainties and improving the performance simultaneously. Chance constraints on output are tackled by converting into convex alternatives. The output tracking problem is transformed into a quadratically constrained quadratic‐programming (QCQP) problem, which is computationally tractable. A numerical example is provided to illustrate the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Adaptive dual model predictive control for linear systems with parametric uncertainties

Mengting

Ning

et al. 2022

IET Control Theory & Appl

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“…In practical applications, as for considering the ergonomic characteristics, there is a deviation between the centroid of the connecting rod and the geometric center as dynamical modeling is constructed. Therefore, a unified analytic model describing the dynamic feature of robot-assisted bathing precisely is extremely complicated to construct [6]. Based on the above issues, the trajectory tracking and compliant control of robot-assisted bathing must be focused on this paper under conditions where the model parameters are unknown.…”

Section: Introductionmentioning

confidence: 99%

Impedance Iterative Learning Sliding Mode Control for Robot-Assisted Bathing with Non-repetitive Trajectory

Guo

zhang

et al. 2022

Preprint

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In this paper, an impedance iterative learning sliding mode control (IILSMC) scheme for robot-assisted bathing with unknown model parameters is investigated. Firstly, impedance control is not only applied to adjust the desired trajectory, but also implemented to ensure the active compliance control of the robot-assisted bathing. Furthermore, iterative learning control is designed to dynamically estimate the unknown model parameters. To release the identical initial condition of iterative learning control (ILC), the trajectory reconstruction method is proposed, such that the convergence of tracking errors can be guaranteed with random initial status. Moreover, adaptive sliding mode control (SMC) is proposed where non-parametric, external disturbances and the human-machine interaction (HMI) torque generated during the bathing process is suppressed by adaptive method. Based on the composite energy function (CEF) method, the convergence of the double closed-loop system in the time domain and iterative domain is proved. Finally, through co-simulation of MATLAB/Simulink and ADAMS, the effectiveness and superiority of the control strategy proposed in this paper is jointly verified.

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“…The MPC controller with linear time variation can improve the flexibility and stability of vehicle steering through multi-module allocation processing [29,30]. Adding adaptive to MPC can control the stability of vehicle tracking trajectory under complex operating conditions [31,32]. MPC combined with Gaussian function [33] and predictive following theory (PFT), respectively [34], such can improve vehicle driving comfort.…”

Section: Introductionmentioning

confidence: 99%

Battery Energy Consumption Analysis of Automated Vehicles Based on MPC Trajectory Tracking Control

Pei

Zhang

2022

Electrochem

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In the field of automated technology research and development, trajectory tracking plays a crucial role in the energy consumption of the vehicle’s power battery. Reducing the deviation between the actual trajectory and the reference trajectory is the focus of trajectory tracking research. This paper proposes the use of the model predictive control (MPC) method to reduce the deviation of lateral and longitudinal position between the actual driving trajectory and the reference trajectory. First, the driving conditions of the vehicle are reflected by establishing the vehicle dynamics model. Then, the MPC trajectory tracking controller is built by designing the objective function with constraints; Finally, the feasibility of this approach was verified by a joint Carsim-Simulink simulation. The simulation results show that the MPC controller designed in this paper can track the trajectory better, and reduce the lateral and longitudinal position deviation. To a certain extent, the battery energy consumption is reduced and the accuracy of the tracking trajectory and the safety of vehicle driving are improved.

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Trajectory Tracking Control of Autonomous Ground Vehicles Using Adaptive Learning MPC

Cited by 67 publications

References 31 publications

Adaptive dual model predictive control for linear systems with parametric uncertainties

Adaptive dual model predictive control for linear systems with parametric uncertainties

Impedance Iterative Learning Sliding Mode Control for Robot-Assisted Bathing with Non-repetitive Trajectory

Battery Energy Consumption Analysis of Automated Vehicles Based on MPC Trajectory Tracking Control

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