Automotive Mechatronics and Autonomous Systems

Automotive mechatronics integrates mechanical structures, electronic sensors and actuators, and embedded software to deliver ever more sophisticated vehicle functions. From active suspension and electronic steering to automated gear shifts and friction management, modern vehicles harness real-time control algorithms, signal processing and networked communication to enhance safety, comfort and efficiency. Concurrently, autonomous systems build on this mechatronic foundation by combining advanced perception—using cameras, lidar, radar and ultra-wideband radios—with decision-making frameworks such as model predictive control and machine learning. Together, these developments are reshaping mobility: vehicles can now adjust damping to road conditions, maintain lane and distance at highway speeds, optimise energy usage in hybrid powertrains and, in specialist applications, plan collision-free trajectories without human input. Globally, these technologies promise reduced traffic fatalities, lower emissions and new models of shared mobility, while presenting challenges in regulatory frameworks, cybersecurity and system integration.

Research from Nature Portfolio

Recent work has introduced an advanced sliding-mode control algorithm for active hydraulic suspension systems. By combining an observer-based sliding-mode controller with an in-loop parameter optimisation, vehicles maintained stable ride dynamics under severe oscillations, ensuring sustained road–wheel contact and markedly reduced body acceleration. A further study has developed a variable-universe fuzzy-PID control for semi-active dampers, dynamically adapting fuzzy inference parameters to real-time error signals and their rates of change. This approach achieved superior comfort and handling stability over a wide range of speeds and road roughness grades. Most recently, researchers have devised an interactive multiple-model adaptive Kalman filter to estimate suspension states under varying vehicle speeds, sprung mass conditions and road profiles. The method simultaneously identifies roughness levels and adapts controller gains, leading to improved ride comfort and more accurate state estimation compared with conventional fixed-gain observers.

Automotive Mechatronics and Autonomous Systems publication trend

The graph below shows the total number of articles in automotive mechatronics and autonomous systems across all publications each year (not limited to Nature Index journals).

Technical terms

Sliding-mode control: A robust control technique that forces system trajectories to a predefined manifold, providing insensitivity to matched uncertainties.

Fuzzy PID control: A hybrid approach combining fuzzy-logic inference with classical proportional–integral–derivative algorithms to adapt gain parameters in real time.

Model predictive control (MPC): An optimisation-based strategy that predicts future system behaviour over a finite horizon to compute control inputs that minimise a cost function.

Active disturbance rejection control (ADRC): A method that estimates and compensates total system disturbances via an extended state observer to maintain performance under uncertainty.

Deep reinforcement learning (DRL): A machine-learning framework in which an agent learns optimal control policies by interacting with its environment to maximise cumulative rewards.

References

1. Evaluate the stability of the vehicle when using the active suspension system with a hydraulic actuator controlled by the OSMC algorithm. Scientific Reports (2022).
2. Research on variable universe fuzzy PID control for semi-active suspension with CDC dampers based on dynamic adjustment functions. Scientific Reports (2024).
3. Adaptive suspension state estimation based on IMMAKF on variable vehicle speed, road roughness grade and sprung mass condition. Scientific Reports (2024).
4. Regionless Explicit Model Predictive Control of Active Suspension Systems With Preview. IEEE Transactions on Industrial Electronics (2019).
5. Semi-Active Suspension Control Based on Deep Reinforcement Learning. IEEE Access (2020).
6. Research on active disturbance rejection control strategy of electric power steering system under extreme working conditions. Measurement and Control (2023).