Trajectory Tracking and Obstacle Avoidance for Wheeled Mobile Robots Based on EMPC With an Adaptive Prediction Horizon

Wang, Shizhan; Yang, Hongjiu; Hai, Zhao

doi:10.1109/tcyb.2021.3125333

Cited by 28 publications

(10 citation statements)

References 30 publications

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“…Consider a two‐wheeled differential drive mobile vehicle in Figure 1. The numerical example scene has been set to track a given circular reference trajectory with multiple obstacles 39 . Note that the nonlinear switched MPC strategy is adopted to achieve smooth and stable tracking and obstacle avoidance.…”

Section: Numerical Examplementioning

confidence: 99%

“…Obviously, the moving vehicle does not safely avoid the obstacle which means that Algorithm 1 has good obstacle avoidance performance in this paper. Note that both this paper and the literature 39 adopt obstacle avoidance MPC algorithms based on artificial potential field functions, and thus have similar cost function description methods. Moreover, “safe area” and “obstacle avoidance area” are not distinguished separately in the Reference 39, the cost function with the artificial potential field function is used in the whole control process to achieve the goal of obstacle avoidance.…”

Section: Numerical Examplementioning

confidence: 99%

See 1 more Smart Citation

Nonlinear switched model predictive control with multiple Lyapunov functions for trajectory tracking and obstacle avoidance of nonholonomic systems

Hai

Yang

Wang

et al. 2023

Intl J Robust & Nonlinear

Self Cite

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In this paper, we investigate trajectory tracking and obstacle avoidance for a nonholonomic system subject to external disturbances by a nonlinear switched model predictive control (MPC) strategy. In the nonlinear switched MPC strategy, a potential field is introduced in a cost function to guarantee a smooth path for the nonholonomic system. A switched mechanism is designed to ensure switching stability by setting multiple Lyapunov functions in different areas. Different from traditional switched mechanisms, an average dwell time closely related to stability conditions is proposed to balance safety and stability in the whole process. Recursive feasibility is presented for the nonlinear switched MPC strategy in trajectory tracking and obstacle avoidance. Simulation results are provided to show effectiveness and superiority of the nonlinear switched MPC strategy by a two‐wheeled mobile vehicle.

show abstract

Section: Numerical Examplementioning

confidence: 99%

Section: Numerical Examplementioning

confidence: 99%

Nonlinear switched model predictive control with multiple Lyapunov functions for trajectory tracking and obstacle avoidance of nonholonomic systems

Hai

Yang

Wang

et al. 2023

Intl J Robust & Nonlinear

Self Cite

View full text Add to dashboard Cite

show abstract

“…Based on the measured voltage input and velocity output, the system parameters are identified using the least-square method and the model validation is conducted by comparing the actual velocity measurement with the estimated velocity based on the proposed model. for q = 3 and the system model matrices in (7) can be desribed as follows:…”

Section: A Experiments Setupmentioning

confidence: 99%

“…As a system with nonholonomic constraints and a clear model of the mechatronics system, the tracking control of wheelchairs has been widely studied by many researchers [5]- [7]. Depending on the type of model used and the level of control implemented, the speed control of wheelchairs can be divided into two categories.…”

Section: Introductionmentioning

confidence: 99%

Safety-based Speed Control of a Wheelchair using Robust Adaptive Model Predictive Control

Ye¹,

Wang²,

Li³

et al. 2022

Preprint

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Electric-powered wheelchair plays an important role in providing accessibility for people with mobility impairment. Ensuring the safety of wheelchair operation in different application scenarios and for diverse users is crucial when the designing controller for tracking tasks. In this work, we propose a safety-based speed tracking control algorithm for wheelchair systems with external disturbances and uncertain parameters at the dynamic level. The set-membership approach is applied to estimate the sets of uncertain parameters online and a designed model predictive control scheme with online model and control parameter adaptation is presented to guarantee safety-related constraints during the tracking process. The proposed controller can drive the wheelchair speed to a desired reference within safety constraints. For the inadmissible reference that violates the constraints, the proposed controller can steer the system to the neighbourhood of the closest admissible reference. The effectiveness of the proposed control scheme is validated based on the high-fidelity speed tracking results of two tasks that involve feasible and infeasible references.

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“…Many adaptive approaches have been designed offline and lack the ability to capture high-order modelfollowing dynamics [3][4][5][6]. Hence, many adaptive learning approaches employ either complex or computationally expensive algorithms [5][6][7][8]. This gets more challenging when adaptive mechanisms are adopted for coupled regulation and optimization missions, where the dimensions of the state and action spaces grow significantly [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Real-time measurement-driven reinforcement learning control approach for uncertain nonlinear systems

Abouheaf

Boase

Gueaieb

et al. 2023

Engineering Applications of Artificial Intelligence

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Trajectory Tracking and Obstacle Avoidance for Wheeled Mobile Robots Based on EMPC With an Adaptive Prediction Horizon

Cited by 28 publications

References 30 publications

Nonlinear switched model predictive control with multiple Lyapunov functions for trajectory tracking and obstacle avoidance of nonholonomic systems

Nonlinear switched model predictive control with multiple Lyapunov functions for trajectory tracking and obstacle avoidance of nonholonomic systems

Safety-based Speed Control of a Wheelchair using Robust Adaptive Model Predictive Control

Real-time measurement-driven reinforcement learning control approach for uncertain nonlinear systems

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