Chalak Qazani, Mohamad Reza, Jalali, Seyed Mohammad Jafar, Asadi, Houshyar, and Nahavandi, Saeid (2020) Optimising Control and Prediction Horizons of a Model Predictive Control-Based Motion Cueing Algorithm Using Butterfly Optimization Algorithm. In: Proceedings of the IEEE Congress on Evolutionary Computation. From: CEC 2020: IEEE Congress on Evolutionary Computation, 19-24 July 2020, Glasgow, UK.

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Abstract

The Motion Cueing Algorithm (MCA) oversees regenerating the motion feeling of the real vehicle for the simulation-based motion platform (SBMP) within the physical limitations. Model Predictive Control (MPC) is recently employed as an MCA, which is called MPC-based MCA due to the consideration of the plant's boundaries in finding the optimal input signal. The computational load of the MPC directly relates to the control horizon and prediction horizon of the MPC. In this paper, a new optimisation method using butterfly optimisation algorithm is developed to find the optimal control horizon and prediction horizon of MPC-based MCA. The proposed method reduces the time of the tuning process of the MPC-based MCA, which is usually carried out via trial-and-error and genetic algorithm methods. Also, the trial-and-error method increases the motion sensation error and insufficient usage of the SBMP. The model is validated using MATLAB simulation environment, and the outcomes show that the developed butterfly optimisation algorithm will lead better motion sensation with less wrong motion signals and low computational burden compared with the trial-and-error and genetic algorithm method.

Item ID:	87040
Item Type:	Conference Item (Research - E1)
ISBN:	978-1-7281-6929-3
Copyright Information:	© 2020 IEEE.
Date Deposited:	27 Oct 2025 23:42
FoR Codes:	40 ENGINEERING > 4007 Control engineering, mechatronics and robotics > 400705 Control engineering @ 20% 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460203 Evolutionary computation @ 60% 40 ENGINEERING > 4007 Control engineering, mechatronics and robotics > 400706 Field robotics @ 20%
SEO Codes:	28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280110 Expanding knowledge in engineering @ 100%
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