Synchronous Control Strategies for Multi-Motor Systems

Multi-motor systems underpin a wide range of industrial and robotic applications, from precision manufacturing lines and gantry cranes to distributed-drive electric vehicles and coordinated robotic arms. The core challenge is to achieve tight synchrony of speed, position or torque across multiple drive units despite differences in load, parameter uncertainty and external disturbance. Traditional proportional–integral–derivative (PID) schemes can struggle to reconcile unbalanced loads or dynamic coupling, leading to steady-state errors or oscillatory behaviour. Modern strategies address these limitations by introducing cross-coupling architectures that feed the error of one motor into the control loop of its neighbours, by deploying robust nonlinear methods such as sliding-mode or active-disturbance-rejection control to attenuate perturbations, and by leveraging intelligent approaches—fuzzy logic or neural networks—to tune controller parameters on-line. Networked topologies, including ring and mean-coupling schemes or electronic line-shafting, ensure scalable coordination across more than two axes. These advances yield faster response, reduced synchronisation error and enhanced robustness, with global significance for energy efficiency, production quality and operational safety in sectors as diverse as automotive manufacturing, aerospace component machining and autonomous electric vehicles.

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Synchronous Control Strategies for Multi-Motor Systems publication trend

The graph below shows the total number of articles in synchronous control strategies for multi-motor systems across all publications each year (not limited to Nature Index journals).

Technical terms

Cross-coupling control: A strategy that injects synchronisation errors from each motor into the controllers of adjacent motors to reduce inter-axis deviation.

Sliding-mode control: A robust nonlinear method that forces system trajectories onto a predefined manifold, providing insensitivity to matched disturbances.

Fuzzy logic controller: An intelligent control scheme using linguistic rules and membership functions to approximate nonlinear system behaviour and adjust gains in real time.

Extended state observer (ESO): A component of active-disturbance-rejection control that estimates unmodelled dynamics and external disturbances for compensation.

Radial basis function neural network: A feed-forward network employing radially symmetric activation functions for fast interpolation and system identification.

Electronic line-shafting: A virtual mechanical coupling technique that enforces synchrony across distributed drives by linking their reference trajectories.

References

1. A Survey of Fuzzy Algorithms Used in Multi-Motor Systems Control. Electronics (2020).
2. A Cross Coupling Control Strategy for Dual-Motor Speed Synchronous System Based on Second Order Global Fast Terminal Sliding Mode Control. IEEE Access (2020).
3. An Electronic Line-Shafting Control Strategy Based on Sliding Mode Observer for Distributed Driving Electric Vehicles. IEEE Access (2021).
4. Multi-Motor Cooperative Control Strategy for Speed Synchronous Control of Construction Platform. Electronics (2022).