Disturbance Rejection in Control Systems

Disturbance rejection in control systems refers to the capacity of a controller to maintain desired performance in the presence of external perturbations or unmodelled dynamics. Classical approaches draw upon the internal model principle, embedding a representation of known disturbance frequencies within the controller, or on robust control theory, which designs feedback laws to tolerate bounded uncertainties. Observers and disturbance estimators reconstruct exogenous inputs in real time, enabling active compensation. Recent advances have integrated adaptive schemes and data-driven techniques to cope with time-varying uncertainties and to reduce reliance on precise plant models. Event-triggered communication and machine-learning-inspired internal models have extended disturbance rejection capabilities in networked and resource-constrained environments. Applications span power grid converters, precision robotics, vehicle dynamics and renewable energy conversion, where even modest disturbances can degrade stability or energy capture. The development of multi-input multi-output observers, two-degree-of-freedom architectures and higher-order repetitive controllers exemplifies the breadth of strategies now available. Globally, improved disturbance rejection enhances the resilience of critical infrastructure and supports the transition to more autonomous and energy-efficient systems.

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Disturbance Rejection in Control Systems publication trend

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Technical terms

Repetitive control: A feedback technique that embeds an internal model of periodic signals to achieve exact tracking and disturbance rejection at specific frequencies.

Equivalent-input-disturbance estimator: An observer that reconstructs the net effect of external disturbances and model uncertainties, enabling active compensation in the control input.

Takagi–Sugeno fuzzy model: A framework for approximating nonlinear systems by blending linear local models weighted by membership functions.

Gaussian process internal model: A nonparametric, kernel-based statistical model used as an internal representation of disturbances within a controller.

Two-degree-of-freedom control: A control architecture that separates reference tracking and disturbance rejection into distinct feedforward and feedback loops for improved performance tuning.

References

1. Periodic event-triggered modified repetitive control with equivalent-input-disturbance estimator based on T-S fuzzy model for nonlinear systems. Soft Computing (2022).
2. Gaussian process repetitive control: Beyond periodic internal models through kernels. Automatica (2022).
3. Robust control of pantograph‐catenary system: Comparison of 1‐DOF‐based and 2‐DOF‐based control systems. IET Control Theory and Applications (2021).