Active Disturbance Rejection Control—New Trends in Agricultural Cybernetics in the Future: A Comprehensive Review
<p>Number of papers accessed on Web of Science using the search terms “ADRC” and “Active Disturbance Rejection Control” every 5 years.</p> "> Figure 2
<p>Main categories of applications of ADRC technology in the agricultural field.</p> "> Figure 3
<p>The structure of the ADRC controller.</p> "> Figure 4
<p>The structure of the PID controller.</p> "> Figure 5
<p>System control block diagram based on ADRC [<a href="#B58-machines-13-00111" class="html-bibr">58</a>].</p> "> Figure 6
<p>Trajectory tracking experiment results performed in the snowy and inclined region. (<b>a</b>) Actual trajectories, (<b>b</b>) Inclination angles of the experimental region, (<b>c</b>,<b>d</b>) Actual trajectories in longitudinal (x) and lateral (y) directions. The notations “wo/S” and “w/S” specify without and with slip estimation [<a href="#B68-machines-13-00111" class="html-bibr">68</a>].</p> "> Figure 7
<p>Photos of steering controller performance test [<a href="#B74-machines-13-00111" class="html-bibr">74</a>].</p> "> Figure 8
<p>Underwater manipulator trajectory tracking experimental platform [<a href="#B85-machines-13-00111" class="html-bibr">85</a>].</p> "> Figure 9
<p>(<b>a</b>) the basic structure diagram of LADRC; (<b>b</b>) Structure of the experimental hydraulic system [<a href="#B99-machines-13-00111" class="html-bibr">99</a>].</p> "> Figure 10
<p>(<b>a</b>) Block diagram of the system controller, (I) Decoupling of the system, (II) Block diagram of the improved LADRC [<a href="#B102-machines-13-00111" class="html-bibr">102</a>]; (<b>b</b>) Field spray quantity data curve (uniform motion) [<a href="#B111-machines-13-00111" class="html-bibr">111</a>].</p> ">
Abstract
:1. Introduction
2. Overview of ADRC
2.1. Components and Fundamentals of ADRC
2.2. Challenges and Needs in Agriculture
2.2.1. Main Challenges in the Agricultural Production Process
- Nonlinearity of Agricultural Systems: Due to the inherent complexity of agricultural operations, agricultural systems are often nonlinear. Determining the observability and controllability of these systems is an exceedingly difficult task, requiring extensive research. The nonlinear characteristics of agricultural systems imply that their responses do not follow simple linear patterns. While linear control methods like extended Kalman filters and basic PID controllers can effectively handle basic control tasks such as autonomous navigation, automatic speed regulation, and straight-line tracking, their difficulty in parameter adjustment results in poor performance with traditional linear control methods [43].
- Time variability and Uncertainty in Agricultural Environments: Agricultural environments are often affected by seasons, weather, and other external factors, leading to time-varying or uncertain system parameters [44]. The time-varying and uncertain nature of agricultural environments make it difficult for control systems to accurately predict and adjust to these changing conditions.
- Variability of Agricultural Parameters: Agricultural systems typically involve complex interactions between multiple variables, such as soil moisture, temperature, and light intensity. This significantly increases the complexity of control systems, affecting system stability. Additionally, due to the variability of agricultural parameters and the complexity of their interactions, it is challenging to establish accurate and reasonable models [45].
- Economic Considerations in Agricultural Production: Beyond the above requirements, agricultural production must also consider energy consumption and cost-effectiveness. Therefore, control system design must involve the creation of efficient control strategies to achieve desired goals while minimizing costs.
2.2.2. Limitations of Traditional Control Methods
3. Application of ADRC Technology in Agriculture
3.1. Agricultural Machinery and Equipment Control
3.2. Farm Navigation and Trajectory Tracking
3.3. Agricultural Production Process Control
3.4. Other Agricultural Sectors
4. Application of ADRC in Combination with Other Technologies in Agriculture
4.1. Application of LADRC in Agriculture
4.2. Combination of SMC and ADRC Applications in Agriculture
4.3. Combination of Other Technologies and ADRC Applications in Agriculture
5. Discussions
5.1. Advantages of ADRC Technology in Agricultural Cybernetics
- Handling Nonlinearity of Controlled Objects: Han’s groundbreaking work on feedback system structures in 1980 [149] pointed out that, under certain conditions, dynamic systems, whether linear or nonlinear, can be transformed into a canonical form of cascade integrators through feedback. Based on this, ADRC has demonstrated its efficient control capabilities in nonlinear environments, particularly in the complex and variable field of agricultural control.
- Handling Wide-Ranging Uncertainty and Disturbances: Xue and Huang [150] compared ADRC with Disturbance Observer-Based Control (DOBC) and found that for systems with both model uncertainties and disturbances, ADRC and DOBC yield similar control results. However, as disturbances increase, ADRC begins to exhibit advantages by emphasizing the “total disturbance” affecting the output process rather than the disturbances entering at their original positions. This allows ADRC to stabilize the system and shape the transient response, making it highly promising for the uncertain, disturbed, and time-variant agricultural working environments.
- Low Dependency on Models: One of ADRC’s advantages in agriculture is low dependency on models. Agricultural environments are complex and variable, encompassing factors such as soil types, climate conditions, and crop needs. Traditional control methods often require accurate models for precise control. In contrast, ADRC estimates disturbances and uncertainties in the system in real time without relying on precise mathematical models [151]. This approach allows ADRC to remain effective even when the system model is not entirely accurate or when parameters change, enhancing the efficiency and reliability of agricultural production through its flexibility and robustness.
- Self-Optimization and Integration with Other Control Technologies: ADRC features superior self-optimization capabilities, enabling it to dynamically estimate external disturbances and internal states, thereby adjusting control strategies to optimize system performance. This characteristic allows it to maintain high accuracy and system stability even in uncertain environments. Additionally, ADRC’s flexibility extends to its ability to effectively integrate with other control technologies. The review highlights that ADRC has been successfully combined with advanced control technologies, such as fuzzy control and neural networks, further enhancing the adaptability of control systems in uncertain and dynamic environments, and gaining widespread use in the agricultural field.
5.2. Challenges of ADRC Technology in Agricultural Cybernetics
- Limited Real-World Applications: Most research on ADRC technology for agricultural machinery remains confined to simulation experiments or laboratory prototype experiments. There is a lack of field experiments in real agricultural settings, which means that some unknown factors may be overlooked in practical scenarios, leading to insufficient practical applicability of the research.
- Limited Research on UAVs: The application of ADRC in agricultural UAVs is often restricted to specific aspects of motion control. There is a lack of research on motion control performance under multi-factor disturbances. Therefore, the overall control performance of ADRC for UAVs remains to be thoroughly investigated.
- Limited Exploration in Smart Greenhouse Control: Current studies on ADRC for smart greenhouse control are mostly limited to laboratory model experiments or simulations based on software such as MATLAB and LabView. Additionally, existing research often focuses on single-factor control, such as temperature, light, or humidity. In actual greenhouse production, factors such as temperature, light, and humidity all directly affect yield and economic benefits.
- Gaps in Aquaculture Control: There remains a significant gap in the application of ADRC technology for aquaculture control.
- Focus on Navigation and Trajectory Tracking: ADRC applications in unmanned agricultural equipment are mainly focused on navigation and trajectory tracking, and not enough attention has been paid to actuator control, which needs to be further researched.
5.3. Future Directions of ADRC Technology in Agricultural Cybernetics
- Field Applications in Agricultural Production: Building on the results from simulation and laboratory experiments, future studies should focus on conducting experiments in real-world agricultural environments such as rice paddies and cotton fields. Environmental factors like soil moisture and crop growth stages should be considered, and ADRC parameters should be optimized to adapt to these conditions. Long-term and large-scale field tests are necessary to evaluate the effectiveness of the technology, identify new challenges, and enhance its practical applicability.
- UAV Applications: In the field of UAVs, future research should explore the simultaneous application of ADRC to UAV attitude control and payload suspension control. Improvements in control stability and precision can be achieved through algorithm enhancements, and combining ADRC with path planning and autonomous flight technologies could further extend its functionality.
- Smart Greenhouse Control: Yield in greenhouses is directly influenced by factors such as gas concentration, lighting, and temperature. Therefore, future research should adopt ADRC strategies to simultaneously control multiple factors in smart greenhouses rather than limiting control to single variables like temperature. Integrating ADRC with sensor data and hyperspectral technology [152,153,154] can enable more precise control of greenhouse conditions.
- Aquaculture Applications: ADRC has potential applications in aquaculture, including water quality control (e.g., waste removal), water environment management, and the control of feed and medication dispensing. By optimizing water quality monitoring and adjustment, and integrating with water quality sensors and automation equipment, ADRC can facilitate intelligent water quality management.
- ADRC Optimization: Future research should explore combining ADRC techniques with intelligent algorithms and deep learning techniques. Previous research on traditional control strategies has used intelligent algorithms to optimize controller parameters [155,156,157]. Similarly, ADRC can benefit from combining with intelligent algorithms to improve its performance. In addition, combining ADRC with deep learning can lead to further improvements. Known for its efficiency, accuracy and robustness, deep learning has been widely used in agriculture [158,159,160,161,162]. By utilizing deep learning, ADRC control strategies can optimize themselves to further improve the overall performance of the ADRC system.
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
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Task | Models | Behavior | Result |
---|---|---|---|
Wheeled chassis motion control [55] | PID | ADRC significantly outperforms the PID and Ackermann models in terms of various performances such as pendulum angular velocity, turning radius and disturbance recovery time. | |
Ackerman Controls | |||
ADRC | √ | ||
Agricultural vehicle heading control [15,56] | FOPID | The results of simulation experiments comparing two controllers, FOPID and ADRC, assuming constant-value perturbation and time-varying perturbation signals, show that the control performance of ADRC is more superior. | |
ADRC | √ | ||
UAV Attitude Control [59] | PID | Compared with the existing PID and Fuzzy PID methods for attitude control of plant protection drones, experimental results show that ADRC exhibits excellent adjustment capability and robustness, effectively achieving attitude control for tandem plant protection drones. | |
Fuzzy PID | |||
ADRC | √ |
Task | Models | Behavior | Result |
---|---|---|---|
Straw compact mode machine water content control [80] | Smith-PID | Compared with smith-PID and smith-LADRC, SSA-Smith-LADRC has the advantages of accurate regulation, strong anti-interference ability and elimination of time lag. | |
Smith-LADRC | |||
SSA-Smith-LADRC | √ | ||
Weed component to row control [99] | PID | The results show that the LADRC technique has superior anti-jamming performance in terms of row-to-row control of the weeding component, reflecting its strong robustness. | |
LADRC | √ | ||
Precision spraying of pesticides [111] | PID | Compared to traditional PID and modified PID control, LADRC controllers have greater immunity and robustness, while the difficulty of setting control parameters is greatly reduced | |
Optimized PID | |||
LADRC | √ |
Task | Models | Behavior | Result |
---|---|---|---|
Underwater robot control [117] | PID | MIMO-ESO ISMC tracks the desired trajectory more accurately with less tracking bias and outperforms PD controllers in terms of peak overshoot and convergence speeds. | |
MIMO-ESO ISMC | √ | ||
Trajectory tracking for paddy sprayers [120] | PID | SADRC outperforms PD attitude control in terms of overshoot and stabilization time duration, and is able to improve sprayer trajectory tracking accuracy in complex environments | |
SADRC | √ | ||
UGV path tracing [123] | PID | The ASMC demonstrated the best tracking performance compared to the PID control and the SMC, which shows the great potential of the ADRC for UGV trajectory tracking tasks | |
SMC | |||
ASMC | √ |
Task | Models | Behavior | Result |
---|---|---|---|
Fertilizer flow control [128] | PID | The superiority of the ADRC controller and the feasibility of UMDA for ADRC optimization are verified based on the actual effect of the action. | |
ADRC | |||
UMDA + ADRC | √ | ||
UAV flight interference suppression [129] | ADRC | The superiority and effectiveness of the ACDRC technique in UAV anti-disturbance performance is demonstrated by indoor experiments. | |
ACDRC | √ | ||
Tractor tracking [130] | LESO | The practical importance of ADRC + DS-DO in outdoor practical applications is demonstrated, while ADRC + DS-DO provides a solution to the problem of error accumulation along the vehicle chain. | |
ADRC + DS-DO | √ |
Aspect | ADRC | PID | MPC | Fuzzy Control | Neural Network Control |
---|---|---|---|---|---|
Nonlinear Handling | ⭐⭐⭐⭐⭐ | ⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ |
Disturbance Rejection | ⭐⭐⭐⭐⭐ | ⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐ |
Model Dependency | ⭐⭐⭐⭐⭐ | ⭐ | ⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐ |
Adaptability | ⭐⭐⭐⭐⭐ | ⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐⭐ |
Computational Load | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐ | ⭐⭐⭐ | ⭐⭐ |
Integration Potential | ⭐⭐⭐⭐⭐ | ⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ |
Agricultural Use | ⭐⭐⭐⭐⭐ | ⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ |
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Tu, Y.-H.; Wang, R.-F.; Su, W.-H. Active Disturbance Rejection Control—New Trends in Agricultural Cybernetics in the Future: A Comprehensive Review. Machines 2025, 13, 111. https://doi.org/10.3390/machines13020111
Tu Y-H, Wang R-F, Su W-H. Active Disturbance Rejection Control—New Trends in Agricultural Cybernetics in the Future: A Comprehensive Review. Machines. 2025; 13(2):111. https://doi.org/10.3390/machines13020111
Chicago/Turabian StyleTu, Yu-Hao, Rui-Feng Wang, and Wen-Hao Su. 2025. "Active Disturbance Rejection Control—New Trends in Agricultural Cybernetics in the Future: A Comprehensive Review" Machines 13, no. 2: 111. https://doi.org/10.3390/machines13020111
APA StyleTu, Y.-H., Wang, R.-F., & Su, W.-H. (2025). Active Disturbance Rejection Control—New Trends in Agricultural Cybernetics in the Future: A Comprehensive Review. Machines, 13(2), 111. https://doi.org/10.3390/machines13020111