Non-Fragile Observer-Based Dissipative Control of Active Suspensions for In-Wheel Drive EVs with Input Delays and Faults

This paper presents a non-fragile observer-based dissipative control strategy for the suspension systems of electric vehicles equipped with in-wheel motors, accounting for input delays, actuator faults, and observer gain uncertainty. Traditional control approaches—such as H∞, passive control, and robust feedback schemes, often address these challenges in isolation and with increased conservatism. In contrast, this work introduces a unified framework that integrates fault-tolerant control, delay compensation, and robust state estimation within a dissipativity-based setting. A novel dissipativity analysis tailored to Electric Vehicle Active Suspension Systems (EV-ASSs) is developed, with nonzero delay bounds explicitly incorporated into the stability conditions. The observer is designed to ensure accurate state estimation under gain perturbations, enabling robust full-state feedback control. Stability and performance criteria are formulated via Linear Matrix Inequalities (LMIs) using advanced integral inequalities to reduce conservatism. Numerical simulations validate the proposed method, demonstrating effective fault-tolerant performance, disturbance rejection, and precise state reconstruction, thereby extending beyond the capabilities of traditional control frameworks.

Authors

Institutions

Publication Details

Journal
Automation
Published
2025-06-30
DOI
https://doi.org/10.3390/automation6030028
Citations
1
Primary Topic
Adaptive Control of Nonlinear Systems
Type
article
Field-Weighted Citation Impact
1.21

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Non-Fragile Observer-Based Dissipative Control of Active Suspensions for In-Wheel Drive EVs with Input Delays and Faults

A. Srinidhi, Michał Niezabitowski, R. Raja, Jehad Alzabut et al.
1 citations
Automation
Adaptive Control of Nonlinear Systems
1.21
article

Non-Fragile Observer-Based Dissipative Control of Active Suspensions for In-Wheel Drive EVs with Input Delays and Faults

A. Srinidhi, Michał Niezabitowski, R. Raja, Jehad Alzabut, S. Vimal Kumar
article en
1 citations

Abstract

This paper presents a non-fragile observer-based dissipative control strategy for the suspension systems of electric vehicles equipped with in-wheel motors, accounting for input delays, actuator faults, and observer gain uncertainty. Traditional control approaches—such as H∞, passive control, and robust feedback schemes, often address these challenges in isolation and with increased conservatism. In contrast, this work introduces a unified framework that integrates fault-tolerant control, delay compensation, and robust state estimation within a dissipativity-based setting. A novel dissipativity analysis tailored to Electric Vehicle Active Suspension Systems (EV-ASSs) is developed, with nonzero delay bounds explicitly incorporated into the stability conditions. The observer is designed to ensure accurate state estimation under gain perturbations, enabling robust full-state feedback control. Stability and performance criteria are formulated via Linear Matrix Inequalities (LMIs) using advanced integral inequalities to reduce conservatism. Numerical simulations validate the proposed method, demonstrating effective fault-tolerant performance, disturbance rejection, and precise state reconstruction, thereby extending beyond the capabilities of traditional control frameworks.

AutomationVol. 6(3)
Silesian University of Technology (PL), Alagappa University (IN), Prince Sultan University (SA), SRM Institute of Science and Technology (IN), Ostim Technical University (TR), Lebanese American University (LB)
Department of Science and Technology, Ministry of Science and Technology, India
Openalex Percentile: Top 20%
Adaptive Control of Nonlinear Systems
1.21
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.