Disturbance Estimation Research Articles

Robust Control of Irrigation Systems Using Predictive Methods and Disturbance Rejection

Ensuring that the world’s population meets its food needs, despite water restrictions, can be significantly improved by increasing irrigation efficiency and productivity. Achieving this goal necessitates technological advancements in control systems. Therefore, the implementation of effective control systems across the entire irrigation water distribution chain is crucial and requires technological modernization. This paper presents a control scheme that combines the benefits of model predictive control (MPC) and active disturbance rejection by using generalized proportional integral (GPI) observers. The proposed control scheme was applied to a three-canal irrigation system. The simulation results confirm that the proposed controller is robust to disturbances and ensures accurate tracking for all reference levels. The controller’s performance is highlighted by the improvement in response time and considerable reduction in overshoot compared with the optimized proportional integral (PI) controllers. Additionally, the use of GPI observers allows for the precise estimation of nonlinear disturbances and phase variables, enhancing the robustness of the system. The efficiency of the observer is due to its ability to adequately estimate global additive disturbances, including unknown parameters and external disturbances in the input–output dynamics.

Fuzzy Extended State Observer-Based Sliding Mode Control for an Agricultural Unmanned Helicopter

In the context of agricultural unmanned helicopters, the complex wind disturbances over crop fields and structural perturbations due to variations in pesticide container weights present substantial challenges to flight safety. To address these issues, this paper proposes an innovative fuzzy extended state observer-based sliding mode control (FESO-SMC) methodology aimed at enhancing the aircraft’s resilience against such disturbances. Initially, this study adopts a state expansion strategy to integrate both wind and structural disturbances into a comprehensive disturbance model applicable to the agricultural unmanned helicopter. Following this, a sliding mode control law is formulated with consideration for unknown total disturbances, employing specific sliding mode functions alongside exponential reaching laws. An extended state observer is simultaneously implemented within the sliding mode control framework to estimate and mitigate these disturbances, thereby augmenting the disturbance rejection capabilities of the flight control system. Additionally, the integration of fuzzy logic facilitates adaptive parameter adjustment for the extended state observer, leading to more accurate disturbance estimation. Consequently, a trajectory tracking control system tailored specifically for the agricultural unmanned helicopter has been developed, and its performance was evaluated through simulation experiments. The results indicate that, under certain disturbances, the attitude control error of the FESO-SMC controller is reduced to one-fifth that of traditional sliding mode controllers, while position control accuracy is enhanced more than twofold, thus demonstrating that the proposed FESO-SMC controller not only exhibits superior anti-disturbance capability and robustness but also achieves higher tracking accuracy compared to conventional sliding mode controller.

An extended state repetitive observer formulation for periodic/polynomial signal rejection in feedback control schemes

Repetitive control is a renowned method for managing periodic disturbances and references in control systems. Despite its effectiveness, performance issues arise when confronting disturbances like polynomial or sinusoidal signals. To enhance its capability, we introduce an extended state repetitive observer by incorporating internal models of periodic, sinusoidal, and polynomial signals into a novel observer framework. This design empowers the observer to adeptly reject and track both periodic and combined sinusoidal+polynomial signals. Beyond addressing the limitations of traditional repetitive control, our observer offers precise disturbance estimation for further analysis and feedback implementation. The observer’s performance is demonstrated through experimental validation within a control setup across various scenarios, focusing on its ability to reject periodic disturbances and track periodic signals effectively.

Predefined-Time Enhanced Antidisturbance Attitude Control for Rigid–Liquid Coupled Launch Vehicles

High-precision and fast-response attitude control of launch vehicles plays an increasingly important role in numerous space launch missions, ensuring the efficiency of space transportation. However, disturbances caused by liquid sloshing and external environments degrade the control performances seriously. Because of the complicated coupling with control inputs and angular acceleration, the dynamics of liquid sloshing disturbance are highly uncertain. Therefore, it is urgent to develop novel disturbance estimation methods for launch vehicles by thoroughly analyzing disturbance dynamics. In this paper, an observer-based predefined-time attitude control method aimed at achieving precise and rapid estimation of liquid sloshing disturbance and other external disturbances is proposed. More specifically, a prescribed-time reduced-order state observer is proposed for estimating the liquid sloshing disturbance and the unmeasurable system state, leveraging the specific characteristics and inherent coupling relations of liquid sloshing disturbance. Additionally, a prescribed-time extended state observer is introduced for estimating external disturbances and nonlinear terms. Subsequently, by employing a predefined-time output feedback controller, a composite control law is proposed to enhance the antidisturbance capabilities of rigid–liquid coupled launch vehicles. Finally, simulation examples are presented to illustrate the effectiveness of the proposed method.

Intelligent Saturation Power Limit Load Distribution Algorithm (ISPLLDA) for Cooperative Manipulators Applications

Cooperative manipulators face challenges related to an inadequate distribution of external loads and a decrease in Dynamic Load Carrying Capacity (DLCC). Understanding the impact of optimal load distribution on power consumption, load carrying capacity, and gripper error is crucial. This paper presents the Intelligent Saturation Power Limit Load Distribution Algorithm (ISPLLDA), a novel method that achieves optimal external load distribution. ISPLLDA dynamically distributes the external load among manipulators based on torque-bearing capacity and actuator position. Additionally, nonlinearity in system dynamics introduces uncertainties, leading to incorrect DLCC evaluation, increased error, and higher actuator power consumption. To address this, a Radial Basis Function Neural Network (RBFNN) accurately determines actuator saturation limits in the presence of uncertainty, enabling correct estimation of system dynamics and external disturbances. ISPLLDA ensures near-simultaneous saturation of all manipulators' actuators, maximizing their capacity utilization. The proposed method is validated through simulations and experimental tests on cooperative manipulators. Results demonstrate a 17% increase in load-carrying capacity, as well as more than 35% improvement in error and torque indexes compared to the Lagrange multipliers method.

Adaptive Backstepping Control for Underwater Robots in Complex Environments

This paper explores the tracking control of an Autonomous Underwater Vehicle (AUV) in challenging ocean conditions. To address disturbances from waves and uncertainties in AUV parameters, a robust adaptive controller is developed. The controller utilizes an adaptive backstepping algorithm to achieve control objectives. The stability of the control law is analyzed using the Lyapunov method, and performance is validated through case studies. The study focuses on tracking control of the AUV in the presence of disturbances, which are modelled as unknown parameters. An adaptive algorithm is used to estimate these parameters, and the backstepping method is employed for system control. The backstepping controller has been implemented to effectively follow reference trajectories and maintain stability of the AUV within their vicinity. The results of the proposed method were compared with those of the sliding mode controller in the scenario of unmodeled dynamics, with the proposed method demonstrating superior performance. Results demonstrate that the adaptive backstepping controller effectively maintains tracking despite disturbances. The study highlights the benefits of incorporating disturbance estimation in underwater vehicle control.

Coupling Disturbance Modeling and Compensation for Aerial Manipulator in Highly Dynamic Motion.

When a manipulator moves in a highly dynamic scenario with a large range of rapid motion, the coupling disturbances between the manipulator and the UAV in the aerial manipulator system (AMS) become very strong, which directly affects the ability of the AMS to perform aerial manipulation and even poses a threat to the safety of the system. The aim of this article is to address the strong coupling disturbance problem in the AMS through precise coupling disturbance modeling and compensation. First, considering the rapid changes in the center of mass (CoM) and the moment of inertia (MoI) of the system under a highly dynamic scenario, this article delves into the generation mechanism of the coupling disturbances and models them based on the variable inertia parameters. The proposed precise coupling disturbance model (CDM) makes good use of the state information of the system, which enables one to achieve accurate estimation of the coupling disturbances without the aid of external force and torque sensors. With the proposed model, the strong coupling disturbances in the AMS are compensated in a feedforward way during the controller design process. An indoor AMS experimental platform is developed for validation purposes. The experiments and simulation are conducted in a highly dynamic scenario, involving rapid movements of the manipulator across a large range. The experimental and simulation results demonstrate the effectiveness and advantages of the proposed method for suppressing the strong coupling disturbances.

Online algebraic disturbance estimation method for linear systems with noisy state measurements

Online algebraic disturbance estimation method for linear systems with noisy state measurements

Development of an Extended State Observer for Monitoring of a PWR Based on a Two-Point Kinetic Model

Accurate estimation of unmeasured system states and disturbances in a pressurized water reactor (PWR) is essential for effective control, operation optimization, and safety monitoring. To this end, this paper investigates the estimation of unmeasured system states and disturbances of the PWR system during load-following operation. First, a mathematical model for the PWR system is established based on the two-point kinetics equations with one equivalent delayed neutron precursor group. Subsequently, an extended state observer (ESO) integration scheme, incorporating two coupled ESOs, is constructed to estimate unmeasured system states, including relative density of delayed neutron precursor, average fuel temperature, total reactivity, xenon concentration, and iodine concentration, along with time-varying disturbances, with the use of measurements of the PWR system only. According to the Lyapunov stability theorem, it is proved that the estimation error dynamic of the proposed ESO integration scheme is uniformly ultimately bounded stable. Finally, simulation results confirm that the proposed ESO integration scheme provides higher estimation accuracy and stronger robustness against measurement noises, model uncertainties, and external disturbances compared to both a high-gain observer and a high-order sliding mode observer.

Unknown input observer-Model predictive control scheme for state and disturbance estimation of shunt active filter

The distribution system's nonlinear loads cause low total harmonic distortion (THD), low distortion power factor, and localized communication interference, among other poor power quality metrics. Shunt active power filter (SAPF) capacity to function depends on the controller's ability to follow the reference signal. To manage larger systems with several inputs and outputs, it would be challenging task to design PID controllers, because excessive controller gains would need to be tuned. Also, every control loop would operate independently of one another, as if there were no interactions between the two loops. This paper proffers Model prediction control for Shunt active power filter (SAPF), which can manage systems with several inputs and outputs that may interact with one another. Luenberger observer (LO) and Proportional Integral observer (PIO) fail to estimates the actual states of SAPF to SAPF, as shown even in the presence of three unknown disturbances, i.e step, triangular and noise type. The proposed unknown input observer (UIO) in the presence of three unknown disturbances perfectly tracks the reference signal. Apart from state estimation, the proposed observer also estimates all the unknown disturbances, when compared to PIO. The results have been simulated in MATLAB environment.

Adaptive robust non-singular fast terminal sliding mode control for a two-link robotic manipulator with NN-based ESO

A novel adaptive non-singular fast terminal sliding mode control (NFTSMC) scheme is developed for a highly nonlinear two-link robotic manipulator (TLRM) system subject to system uncertainties and unknown disturbances. First, to compensate for the system uncertainties, neural network (NN) is employed to approximate the unknown nonlinear dynamic function. Subsequently, to address the challenges of disturbance compensation and unmeasured joint angular velocity, a nonlinear NN-based extended state observer (NN-based ESO) is introduced. The NN-based ESO facilitates accurate disturbance estimation and compensation, while also enabling simultaneous estimation of the joint angular velocity. Through the integration of these techniques, the closed-loop system’s stability is proven using the direct Lyapunov method, demonstrating remarkable tracking performance within finite time. Simulation results validate the effectiveness of the NFTSMC with the NN-based ESO, showcasing superior performance compared to alternative control strategies.

Event-Triggered Adaptive Antidisturbance Switching Control for Switched Systems With Dynamic Neural Network Disturbance Modeling.

In this article, a dynamic event-triggered adaptive antidisturbance (ETAAD) switching control strategy is proposed for switched systems subject to multisource disturbances. The disturbances are divided into two categories: the available unmodeled disturbance and the unavailable dynamic neural network modeled disturbance. First, a dynamic ET criterion is set based on the system state. Then, a novel dynamic ETA disturbance estimator is introduced to observe the modeled disturbance. Furthermore, according to the ET rule and adaptive disturbance observer, a switched controller is designed. Next, under the controller and switching criterion with the average dwell time limitation, sufficient conditions are given to force the switched systems to realize multisource disturbance suppression (DS), trajectory tracking, and communication resource (CR) saving simultaneously. Meanwhile, the Zeno phenomenon may be caused by the ET rule being excluded. In addition, the presented ETAAD approach is also applicable to the nonswitched systems case. Finally, a simulation case is given to validate the effectiveness of the dynamic ETAAD switching control method.

Finite-horizon approximate optimal attitude control based on adaptive dynamic programming for ultra-low-orbit satellite

This study investigates the finite-horizon approximate optimal attitude control problem for ultra-low-orbit satellites, addressing the complexities introduced by substantial disturbance, actuator faults, actuator saturation, and time constraint. Initially, a fixed-time concurrent learning fault and disturbance estimation approach is proposed that relieves persistent excitation constraints and isolates different influences individually. Subsequently, the cost function is designed with actuator fault estimation, ensuring that the control strategy consistently adheres to actuator saturation constraints and can compensate for current faults. Furthermore, based on the adaptive dynamic programming, an approximate optimal attitude control approach is proposed, which employs time-varying activation functions to approximate the optimal cost function. A fixed-time neural network weight adaptation strategy is designed to ensure the precision and reliability of the approximation. Finally, the numerical simulation confirms the validity and practical applicability of the proposed approach in satellite attitude control systems.

Disturbance rejection-based adaptive non-singular fast terminal sliding mode control for a quadrotor under severe turbulent wind.

This paper presents the design of a disturbance rejection-based control strategy for a quadrotor unmanned aerial vehicle subject to model uncertainties and external disturbances described by turbulent wind gusts of severe intensity. First, an extended state observer is introduced to supply full-state and total disturbance estimations within a fixed time regardless of initial estimation errors. Then, an adaptive non-singular fast terminal sliding mode controller with a single-gain structure is proposed to reduce the tuning complexity and drive the pose of the rotorcraft while providing practical finite-time convergence, robustness to bounded external disturbances, non-overestimation of its control gain, and chattering attenuation. Furthermore, the stability of the closed-loop system is guaranteed through homogeneity and Lyapunov theory. Simulation results obtained through the ROS/Gazebo framework demonstrate graphically and quantitatively that the proposed observer-based controller reduces the influence of perturbations and requires less torque effort than existing methods in the presence of sensor noise.

An Extend Sliding Mode Disturbance Observer for Optical Inertial Platform Line-of–Sight Stabilized Control

As the imaging distance and focal length of photoelectric systems increase, the requirements for line-of–sight stabilization of optical inertial stabilized platforms (ISPs) become higher. Disturbance rejection directly determines the stability accuracy of optical inertial stabilized platforms. However, the accurate observation and suppression of wide-band and rapidly changing disturbances remains a challenge in current engineering applications. This paper proposes a robust extended sliding mode observer (ESMO) method to improve disturbance estimation performance. First, the linear extended state observer (LESO) is designed by taking the total disturbances as extended states. Then, a sliding mode observer (SMO) is incorporated in the extended states of the extended observer, forming a robust ESMO. Subsequently, the robustness and convergence characteristics of the proposed method are mathematically proved, revealing that it operates robustly without knowing the disturbance’s upper bound and offers faster dynamics and higher accuracy than the LESO. Finally, a series of simulation experimental tests are performed to demonstrate the effectiveness of the proposed method. The proposed method observes wide-band and rapidly changing disturbances utilizing the rapidly switching characteristic of the SMO and smooths the jitter of the SMO by cascading sliding mode estimation to the differentiation term of extended observation, achieving the integral effect of the reaching law. Meanwhile, this method only requires adjusting two parameters, making it suitable for engineering applications. It can be effectively used in optical inertial stabilized platform control systems for disturbance estimation and compensation.

Neural network prescribed-time observer-based output-feedback control for uncertain pure-feedback nonlinear systems

To prevent the complicated backstepping design, as well as the coupled design of state observer/disturbance approximator and baseline controller that makes the parameter synthesis difficult, this article proposes a neural network (NN) prescribed-time observer-based output-feedback control approach for uncertain pure-feedback nonlinear systems. After reformulating the original system into a canonical form with matched lumped disturbance, an adaptive NN prescribed-time observer (NNPTO) is developed to quickly provide accurate estimates of unknown states and disturbance. Then the observer is integrated with designed non-backstepping state-feedback controller to construct an output-feedback control scheme. Three features distinguish our approach from existing non-backstepping methods: (1) the accurate estimation is realized in a finite time prescribed through a single parameter; (2) the design of the state observer/disturbance approximator and baseline controller is decoupled, which eases the synthesis process and makes lots of existing non-backstepping state-feedback controllers compatible with our output-feedback control approach; (3) The NNPTO has trivial oscillation and small observation peaking errors under large disturbance, which effectively improves the output tracking performance. Numerical examples and an application example of a rigid single-link manipulator system are presented to demonstrate the effectiveness and practicability of our approach.

Finite-time distributed state and disturbance estimation for LTI systems with disturbance: a PEBO-based approach

For a class of linear time-invariant (LTI) systems with disturbance, the finite-time distributed estimation problem of both system state and disturbance was investigated. First, observability decomposition is performed to transform the original system into a cascaded one that is composed of multiple subsystems, and the system disturbance is modelled as the output of an external system. Second, the first substate and external system state are augmented into a new state, and the explicit relationships between state and unknown parameters are established by applying global filtering transformation. Subsequently, the DREM-based parameter estimator and parameter estimation-based observer are developed to simultaneously estimate the first substate and disturbance in finite time. Then, under a unidirectional communication mechanism between adjacent nodes, the subsequent nodes can obtain the estimates of disturbance and substates of all precursor subsystems and only need to estimate the state of current subsystem. Finally, the effectiveness of the proposed methods is illustrated by a numerical example and double-pendulum overhead crane system.

T–S Fuzzy Observer-Based Output Feedback Lateral Control of UGVs Using a Disturbance Observer

This paper introduces a novel observer-based fuzzy tracking controller that integrates disturbance estimation to improve state estimation and path tracking in the lateral control systems of Unmanned Ground Vehicles (UGVs). The design of the controller is based on linear matrix inequality (LMI) conditions derived from a Takagi–Sugeno fuzzy model and a relaxation technique that incorporates additional null terms. The state observer is developed to estimate both the vehicle’s state and external disturbances, such as road curvature. By incorporating the disturbance observer, the proposed approach effectively mitigates performance degradation caused by discrepancies between the system and observer dynamics. The simulation results, conducted in MATLAB and a commercial autonomous driving simulator, demonstrate that the proposed control method substantially enhances state estimation accuracy and improves the robustness of path tracking under varying conditions.

Adaptive arbitrary time synchronisation control for fractional order chaotic systems with external disturbances

In this paper, adaptive synchronisation of fractional order chaotic systems with external disturbances is studied. For this purpose, first, a new integer order system is introduced and its convergence to zero exponentially within an arbitrary time is proven. Using this system, a novel adaptive exponential arbitrary time sliding mode controller is established to synchronise the fractional order chaotic systems. Then, to estimate the disturbance terms of the fractional systems an exponential arbitrary time disturbance observer is developed. The use of the disturbance observer can lead to simple controller design, low chattering control input, and more accurate response. Finally, a disturbance observer based sliding mode control is presented, that by incorporating the disturbance estimation in controller design ensures synchronisation of chaotic systems. The exponential arbitrary time synchronisation and the stability of the closed loop control system are confirmed using the Lyapunov theory. Simulation results on synchronisation of different fractional order systems, validate the proposed control schemes. It is noteworthy that the introduced adaptive sliding mode controllers can be used for controlling a wide variety of fractional order systems.

Refined Metamodel Disturbance Observer-Based Control for Coarse Pointing Assembly Under Constraints

The target tracking performance of the coarse pointing assembly (CPA) is a critical factor in determining the data transmission efficiency of the inter-satellite laser communication. However, under the influence of multiple disturbances, such as gimbal coupling torque, friction, and satellite-transmitted vibration, the angle tracking performance of the CPA can be decreased, which then affects the safety of the system in terms of angular velocity. To address the performance and safety requirements of the CPA, this paper proposes a refined metamodel disturbance observer-based state constraints controller. First, a refined metamodel disturbance observer is proposed to achieve accurate disturbance estimation by combining the data-driven state-space Kriging metamodeling method with a refined disturbance observer. Both disturbance numerical data and partially known information (e.g. vibration frequency and structure) are fully utilized to reduce estimation conservatism. Second, based on the observer, a state constraint controller is designed to ensure the angle tracking performance and angular velocity constraints. Finally, the effectiveness and robustness of the proposed control method are validated through numerical simulations, indicating an enhanced angle tracking performance compared to the traditional disturbance observer-based control method.