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Article

Autonomous Collision Avoidance Using MPC with LQR-Based Weight Transformation

Department of Mechanical Engineering Sciences, Connected Autonomous Vehicle Lab (CAV-Lab), University of Surrey, Guildford GU2 7XH, UK
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Author to whom correspondence should be addressed.
Academic Editor: Enrico Meli
Sensors 2021, 21(13), 4296; https://doi.org/10.3390/s21134296
Received: 29 March 2021 / Revised: 1 June 2021 / Accepted: 7 June 2021 / Published: 23 June 2021
(This article belongs to the Special Issue Artificial Intelligence and Internet of Things in Autonomous Vehicles)
Model predictive control (MPC) is a multi-objective control technique that can handle system constraints. However, the performance of an MPC controller highly relies on a proper prioritization weight for each objective, which highlights the need for a precise weight tuning technique. In this paper, we propose an analytical tuning technique by matching the MPC controller performance with the performance of a linear quadratic regulator (LQR) controller. The proposed methodology derives the transformation of a LQR weighting matrix with a fixed weighting factor using a discrete algebraic Riccati equation (DARE) and designs an MPC controller using the idea of a discrete time linear quadratic tracking problem (LQT) in the presence of constraints. The proposed methodology ensures optimal performance between unconstrained MPC and LQR controllers and provides a sub-optimal solution while the constraints are active during transient operations. The resulting MPC behaves as the discrete time LQR by selecting an appropriate weighting matrix in the MPC control problem and ensures the asymptotic stability of the system. In this paper, the effectiveness of the proposed technique is investigated in the application of a novel vehicle collision avoidance system that is designed in the form of linear inequality constraints within MPC. The simulation results confirm the potency of the proposed MPC control technique in performing a safe, feasible and collision-free path while respecting the inputs, states and collision avoidance constraints. View Full-Text
Keywords: trajectory planning; MPC; LQR; LQT; inverse optimal control; collision avoidance trajectory planning; MPC; LQR; LQT; inverse optimal control; collision avoidance
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MDPI and ACS Style

Taherian, S.; Halder, K.; Dixit, S.; Fallah, S. Autonomous Collision Avoidance Using MPC with LQR-Based Weight Transformation. Sensors 2021, 21, 4296. https://doi.org/10.3390/s21134296

AMA Style

Taherian S, Halder K, Dixit S, Fallah S. Autonomous Collision Avoidance Using MPC with LQR-Based Weight Transformation. Sensors. 2021; 21(13):4296. https://doi.org/10.3390/s21134296

Chicago/Turabian Style

Taherian, Shayan, Kaushik Halder, Shilp Dixit, and Saber Fallah. 2021. "Autonomous Collision Avoidance Using MPC with LQR-Based Weight Transformation" Sensors 21, no. 13: 4296. https://doi.org/10.3390/s21134296

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