Introductory Chapter: Introduction to Disturbance Rejection Control

1. Introduction

Disturbing dynamic systems on a regular basis can have a significant impact on their performance and stability. Disturbance rejection control techniques seek to mitigate the impact of disturbances while preserving desirable system behavior. This chapter delves further into the definition, goals, control mechanisms, and applications of disturbance rejection control. The theoretical foundations and practical applications of various disturbance rejection control systems are also discussed, with an emphasis on the importance of robustness and adaptability. Any dynamic system will experience disturbances, which can be caused by both internal and external uncertainties. These disruptions have a substantial impact on the system’s performance and can cause it to deviate from target setpoints or trajectories [1]. Disturbance rejection control strategies are used to lessen the effects of disturbances and preserve desirable system behavior. An overview of disturbance rejection control and its significance in many applications will be given in this introduction. This chapter concludes by summarizing the prospects for the future of this field and prospective future research endeavors.

2. Objectives and definition

Disturbance rejection control is a subfield of control engineering that aims to reduce the impact of disturbances on the output or performance of the system [2]. In the face of disturbances, the major goal of disturbance rejection control is to keep the system stable, accurate, and resilient. The control system strives to make sure that the output closely resembles the desired reference signal or trajectory, even in the presence of disturbances, by actively compensating for them [3].

3. Types of disturbances

Depending on the characteristics of the system and its surroundings, disturbances can take many distinct forms. Internal and external disturbances are two major groups into which they might be divided. External disturbances come from the environment and can be caused by things like temperature fluctuations, wind loads, or adjustments to the input signals [4]. On the other side, internal disturbances result from uncertainties within the system itself, such as parameter changes, sensor noise, or model errors [5, 6].

4. Techniques for disturbance rejection control

To reject disturbances and preserve desirable system performance, a variety of control strategies are used. Among the methods that are frequently utilized are:

4.1 Feedforward control

This technique uses an estimated disturbance model and a compensating control action to mitigate the effects of the disturbance before they have an impact on the system output. This method works best when the disturbance can be precisely quantified or predicted in advance [7, 8, 9, 10, 11].

4.2 Feedback control

The classic strategy of feedback control compares the system’s output continually to the intended reference signal and modifies the control action to minimize error. Feedback control aids in making up for disturbances that cannot be precisely predicted or measured in advance [12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28].

4.3 Adaptive control

Adaptive control approaches modify the control action in response to shifting system dynamics and disturbance characteristics by using online parameter estimation and adaptation algorithms. In situations when the system parameters or disturbance characteristics change over time, adaptive control is especially helpful [29, 30, 31, 32, 33, 34].

5. Robustness and adaptability

Techniques for disturbance rejection control must be resilient and adaptable to provide efficient disturbance compensation. The term “robustness” describes a system’s capacity to continue operating consistently and accurately in the face of unknowns and interruptions. Robust design methodologies and uncertainty modeling are used into robust control algorithms to ensure stability and performance guarantees even when disturbances are greater than expected [12].

A control system’s adaptability is its capacity to change its structure or parameters in response to varying environmental factors. Using adaptive control techniques, the system may continually estimate and update the properties of the disturbance and modify the control action as necessary. In the face of time-varying disturbances or shifting system dynamics, this adaptability aids in maintaining system performance [7].

6. Applications

Disturbance rejection control is used in a variety of industries, including process, manufacturing, robotics, and aerospace.

6.1 Aerospace

Disturbance rejection management is essential in aerospace applications for stabilizing aircraft during turbulence and fending off outside disturbances like wind gusts. By successfully adjusting for disturbances that impact the aircraft’s trajectory and performance, it provides safe and stable flight [35].

6.2 Robotics

In order to maintain exact positioning and tracking of robotic arms in the face of external forces, uncertainties, or disturbances, disturbance rejection control is crucial. It makes it possible for robots to complete jobs reliably and precisely even in changing environments [35].

6.3 Manufacturing

To obtain accurate control of diverse operations, disturbance rejection control is used in manufacturing processes. For instance, disturbance rejection control in CNC machining helps maintain precise tool positioning and tracking by adjusting for outside influences or uncertainties that can impair the quality of the cutting [36].

6.4 Process industries

Maintaining product quality and stability is crucial in process industries, such as chemical plants, therefore, disturbance rejection control is essential. In order to maintain constant process performance and product quality, it enables the control system to account for disturbances, variations in input parameters, or uncertainties [17].

7. Motivation for this book

Disturbance rejection control is a fundamental aspect of control engineering that addresses the challenges posed by disturbances in dynamic systems. By employing various control techniques such as feedforward, feedback, and adaptive control, disturbance rejection control mitigates the effects of disturbances and ensures stable system behavior. The robustness and adaptability of control algorithms are essential to handle uncertainties and changing conditions effectively. Disturbance rejection control has broad applications in aerospace, robotics, manufacturing, and process industries, enabling systems to operate reliably and achieve desired performance even in the presence of disturbances. Future research in this field should focus on developing advanced disturbance modeling techniques, robust control algorithms, and adaptive strategies to enhance disturbance rejection capabilities and address emerging challenges.