Control Systems and Motion Control in Robotics

Control systems and motion control lie at the heart of modern robotics, enabling machines to perceive their environment, plan trajectories and execute precise movements. At its core, a control system regulates actuators based on sensor feedback to achieve desired positions, speeds or forces. Traditional schemes such as proportional–integral–derivative control have been augmented by adaptive, robust and model-based strategies to handle nonlinearities, uncertainties and time-varying dynamics. Impedance and admittance control allow robots to interact safely with humans and unstructured environments by modulating mechanical compliance. More recently, data-driven approaches, including reinforcement learning and disturbance observers, have supported real-time adaptation in the face of complex disturbances. Advances in hardware, such as field-programmable gate arrays and high-density sensors, have dramatically reduced latency and enabled parallel computation of kinematic and dynamic equations. Together, these developments have underpinned leaps in industrial automation, surgical robotics, autonomous vehicles and micromanipulation, with growing emphasis on energy efficiency, reliability and safe human–robot collaboration.

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Recent work has reinforced the role of flexible and open architectures in industrial settings. A comprehensive review of modern industrial robot control systems highlights the integration of sensor networks, axis controllers and industrial Ethernet to reduce wiring complexity and enable agile bin-picking, assembly and machining tasks. The authors emphasise advances in offline programming tools and impedance control for more natural interactions between robots and workpieces.

In high-precision mechatronic systems, a disturbance observer coupled with deep reinforcement learning was shown to deliver sub-micrometre accuracy in micropositioning. By embedding a sliding-mode observer to reject lumped disturbances and employing a deep deterministic policy gradient agent, the control scheme achieved robust tracking under external perturbations, suggesting a promising route for semiconductor inspection and biomedical applications.

Another study in precision manufacturing introduced a sparse fuzzy PID controller for CNC machine tools, using a reduced rule base to adjust proportional, integral and derivative gains in real time. This sparse implementation improved computational efficiency and cut overshoot by over 70%, while demonstrating enhanced anti-interference performance compared with both classical PID and full-order fuzzy PID schemes.

Control Systems and Motion Control in Robotics publication trend

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

Technical terms

PID controller: A feedback mechanism combining proportional, integral and derivative actions to minimise error between a desired set-point and actual system output.

Fuzzy Logic Controller: A rule-based control scheme that handles uncertainty and nonlinearities by mapping input variables to control actions through linguistic rules.

Disturbance observer: A tool that estimates and compensates for external or internal disturbances affecting system performance, enhancing robustness.

Deep reinforcement learning: A data-driven control approach where an agent learns optimal policies through trial-and-error interactions with its environment, using deep neural networks.

Impedance control: A strategy that regulates the dynamic relationship between force and motion to ensure compliant interaction between robot and environment.

Field-programmable gate array (FPGA): Reconfigurable hardware allowing parallel execution of control algorithms and fast computation of kinematic and dynamic equations.

References

1. Exploring Industrial Robot Control Systems: Components, Software and Applications. Journal of Robotics Spectrum (2024).
2. Adaptive Sliding Mode Disturbance Observer and Deep Reinforcement Learning Based Motion Control for Micropositioners. Micromachines (2022).
3. Design of Fuzzy PID Controller Based on Sparse Fuzzy Rule Base for CNC Machine Tools. Machines (2023).
4. The Parallel Solving Method of Robot Kinematic Equations Based on FPGA. Journal of Robotics (2023).