Control Strategies for Robotic Manipulation Systems
Summary
Robotic manipulation systems span a wide spectrum of applications, from precision assembly and surgical robots to underwater and soft robotic platforms. Central to their performance is the selection of control strategies that can manage nonlinear dynamics, flexible and compliant structures, underactuation, environmental disturbances and sensor noise, all while ensuring stability and high tracking accuracy. Classical methods such as proportional–integral–derivative (PID) control remain popular for their simplicity, but often struggle with model uncertainties and vibration suppression. Model-based approaches—including input-output feedback linearisation, optimal control and robust sliding mode techniques—offer analytical guarantees at the cost of accurate system identification. Recent advances have introduced adaptive and learning-based controllers, such as neural network and fuzzy logic augmentations, which can compensate for unmodelled dynamics and time-varying parameters. Concurrently, the rise of compliant and soft robotics has driven research into controllers that exploit elastic coupling for safety and adaptability. Overall, modern manipulation control strategies are increasingly hybrid, combining classical theory with data-driven adaptation to deliver reliable performance across diverse robotic platforms and task requirements.
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Control Strategies for Robotic Manipulation Systems publication trend
The graph below shows the total number of articles in control strategies for robotic manipulation systems across all publications each year (not limited to Nature Index journals).
Technical terms
Underactuated system: A system with fewer control inputs than degrees of freedom, requiring indirect or constrained control approaches.
Compliant mechanism: A design incorporating elastic deformation to achieve motion or force transmission, enhancing adaptability and safety.
Input-output feedback controller: A control law based on the relationship between the system input and measured output to achieve desired dynamic performance.
Sliding mode control: A robust control technique that forces system trajectories onto a designed manifold by discontinuous control action, handling uncertainties effectively.
Fractional-order controller: A generalisation of standard controllers using non-integer calculus orders to improve robustness to disturbances and noise.
References
- Dynamic Coupling for Underactuated Compliant Arms With Not Well-Defined Relative Degree. IEEE Transactions on Systems Man and Cybernetics Systems (2024).
- Dynamic Modeling and Vibration Suppression for Two-Link Underwater Flexible Manipulators. IEEE Access (2022).
- Control of Very Lightweight 2-DOF Single-Link Flexible Robots Robust to Strain Gauge Sensor Disturbances: A Fractional-Order Approach. IEEE Transactions on Control Systems Technology (2021).
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