Article: Adaptive control of electromagnetic suspension based on reinforcement learning and fuzzy rules Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.34 pp.1 - 16 Abstract: To address the challenges of strong nonlinearity and uncertain disturbances in electromagnetic suspension systems, this paper proposes an RL-Fuzzy adaptive control architecture that integrates reinforcement learning with fuzzy logic. The core innovation involves utilising fuzzy rules to dynamically adjust the exploration rate of the deep deterministic policy gradient algorithm and incorporating Lyapunov stability constraints to suppress current overshoots. Validated using the Springer Nature real vehicle bench dataset, the proposed method reduces the root mean square of body acceleration to 1.15 m/s2 (surpassing the ISO 2631 high-comfort threshold) and full optimisation of key indicators: safety (tyre displacement variance 1.89 mm2), energy consumption (current rms 1.28 A2), training efficiency (42.3% reduction in steps). This approach provides a computationally efficient robust control framework for intelligent suspension systems. Inderscience Publishers - linking academia, business and industry through research

Title: Adaptive control of electromagnetic suspension based on reinforcement learning and fuzzy rules

Authors: Zefeng Ding; Haili Tang; Xiaojuan Cao

Addresses: School of Automotive Engineering, Hunan Mechanical and Electrical Polytechnic, Changsha, 410151, China ' School of Automotive Engineering, Hunan Mechanical and Electrical Polytechnic, Changsha, 410151, China ' School of Automotive Engineering, Hunan Mechanical and Electrical Polytechnic, Changsha, 410151, China

Abstract: To address the challenges of strong nonlinearity and uncertain disturbances in electromagnetic suspension systems, this paper proposes an RL-Fuzzy adaptive control architecture that integrates reinforcement learning with fuzzy logic. The core innovation involves utilising fuzzy rules to dynamically adjust the exploration rate of the deep deterministic policy gradient algorithm and incorporating Lyapunov stability constraints to suppress current overshoots. Validated using the Springer Nature real vehicle bench dataset, the proposed method reduces the root mean square of body acceleration to 1.15 m/s² (surpassing the ISO 2631 high-comfort threshold) and full optimisation of key indicators: safety (tyre displacement variance 1.89 mm²), energy consumption (current rms 1.28 A²), training efficiency (42.3% reduction in steps). This approach provides a computationally efficient robust control framework for intelligent suspension systems.

Keywords: electromagnetic suspension system; RL-Fuzzy adaptive control; deep deterministic policy gradient; Lyapunov stability constraint.

DOI: 10.1504/IJICT.2026.153002

International Journal of Information and Communication Technology, 2026 Vol.27 No.34, pp.1 - 16

Received: 09 Dec 2025
Accepted: 10 Jan 2026
Published online: 17 Apr 2026 *

Title: Adaptive control of electromagnetic suspension based on reinforcement learning and fuzzy rules

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