Adaptive Control Techniques for Robotic Manipulators

Adaptive control techniques have become indispensable for the precise and reliable operation of robotic manipulators in dynamic and uncertain environments. By adjusting control laws in real time to accommodate variations in payload, joint friction, unmodelled dynamics and external disturbances, adaptive schemes ensure stability and high performance across a broad spectrum of tasks. Traditional model-based controllers rely on accurate dynamic models, which are often impractical to derive for complex manipulators or changing tools. Adaptive strategies address this by estimating unknown parameters or unmodelled forces on the fly, harnessing methods such as model reference adaptive control, sliding mode control, neural-network compensation and fuzzy-logic adaptation. Recent advances combine adaptive mechanisms with optimisation algorithms and machine-learning architectures to enhance robustness, reduce chattering and improve convergence rates. These developments underpin applications ranging from high-precision assembly and aerospace inspection to tele-operated surgery and modular reconfigurable robotics. The ongoing trend is towards hybrid adaptive frameworks that integrate data-driven identification, disturbance observers and reinforcement-learning elements, thereby expanding the manipulators’ capability to self-tune in the face of novel tasks and environments.

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Research from all publishers

A sliding mode-based online fault compensation scheme has been demonstrated for modular reconfigurable robots with actuator failures. By solving the Hamilton–Jacobi–Bellman equation via adaptive dynamic programming, the controller iteratively refines its policy while a robust term ensures the sliding surface conditions. When faults occur, the scheme compensates in real time without discrete fault isolation, maintaining asymptotic stability and trajectory accuracy under diverse operating conditions.

An adaptive fuzzy neural network sliding mode controller has been applied to parallel robots to reconcile the trade-off between robustness and tracking precision. The fuzzy neural network estimates unknown nonlinear dynamics, while the sliding mode element enforces convergence to the desired trajectory. Simulation results confirm superior disturbance rejection and reduced chattering compared with classical sliding mode approaches, highlighting the promise of neural-fuzzy adaptation for multi-degree-of-freedom manipulators.

Interval type-2 fuzzy logic controllers for a planar parallel manipulator have been enhanced using a social spider optimisation algorithm to tune input and output scaling factors. This meta-heuristic optimisation significantly improves the controller’s ability to handle uncertainty and external perturbations. Comparative studies show that the interval type-2 design, optimised via social spider principles, outperforms conventional type-1 fuzzy controllers in trajectory tracking and robustness metrics under both nominal and disturbed scenarios.

Adaptive Control Techniques for Robotic Manipulators publication trend

The graph below shows the total number of articles in adaptive control techniques for robotic manipulators across all publications each year (not limited to Nature Index journals).

Technical terms

Adaptive control: A class of control methods that adjust controller parameters in real time to cope with system uncertainties and changing conditions.

Sliding mode control: A robust control strategy that forces system trajectories onto a predefined sliding surface, ensuring high disturbance rejection and finite-time convergence.

Fuzzy neural network: A hybrid computational structure combining fuzzy logic’s qualitative reasoning with neural networks’ learning capabilities to approximate complex nonlinear functions.

Adaptive dynamic programming: A data-driven optimisation approach that iteratively solves the Hamilton–Jacobi–Bellman equation to derive optimal control policies without full knowledge of system dynamics.

References

1. Sliding mode-based online fault compensation control for modular reconfigurable robots through adaptive dynamic programming. Complex & Intelligent Systems (2021).
2. Parallel robot with fuzzy neural network sliding mode control. Advances in Mechanical Engineering (2018).
3. Social spider optimization algorithm for tuning parameters in PD-like Interval Type-2 Fuzzy Logic Controller applied to a parallel robot. Measurement and Control (2021).