Adaptive Disturbance Observer Research Articles

Fault-tolerant control for Markov jump nonlinear systems based on an observer and finite-time fast integral terminal sliding mode control

This paper develops a novel fault-tolerant control based on an observer for underactuated robot manipulators with unknown external disturbances, uncertainties, and actuator faults. First, unlike the traditional approach of treating the robot system as a parameter time-varying system, the underactuated robot manipulator with different drive modes can be considered as one of the classical Markov jump nonlinear systems (MJNSs). Second, an adaptive disturbance observer is designed to estimate the state and disturbances of the system. Finally, based on the observation results, a nonsingular fast integral terminal sliding mode controller (NFITSMC) is utilized to implement fault-tolerant control of the system. Compared with traditional observer and terminal sliding mode controller, the adoption of the novel controller and observer can improve response time and reduce chattering. Especially, in order to eliminate chattering, the integral term is introduced into the nonsingular fast terminal sliding mode controller. Structuring Lyapunov–Krasovskii functional (LKF) and based on linear matrix inequalities (LMIs) techniques, the convergence of control strategy and tracking errors is proved. The simulation results show that the actuator faults can be observed successfully and the error system is finite-time stable.

A novel cascaded trajectory tracking control for UUVs under time-varying disturbances

In this paper, a novel cascaded trajectory tracking control strategy based on the characteristics of kinematics and dynamics is proposed for unmanned underwater vehicles (UUVs) under complex disturbances. A kinematic controller based on an improved model predictive control (MPC) is designed to solve the speed jump problem by considering system constraints. Moreover, an improved whale optimizer with elite strategy (EIWOA) is used to the rolling optimization process of MPC, ensuring that the global best solution is derived by obtaining the elite individuals in the population. Besides, a dynamic controller that consists of an adaptive disturbance observer and a fuzzy integral sliding mode control (ADO-FISMC) is proposed to obtain optimal thrust force under time-varying disturbances. The benefit of ADO lies in that it can estimate time-varying disturbances without specific information about disturbances. The fuzzy logic system is introduced to alleviate the chattering of sliding mode control. The simulation results demonstrate the great tracking performance of the cascaded trajectory tracking controller. The viability of the proposed algorithm is also confirmed by trials conducted in water pools.

Fixed-time nonsingular terminal sliding mode control for trajectory tracking of uncertain robot manipulators

Fixed-time nonsingular terminal sliding mode control for trajectory tracking of uncertain robot manipulators

The Speed Control of PMLSM with Discontinuous Driving Coils Based on Multi-loop DOB Model

A permanent magnet linear synchronous motor (PMLSM) with discontinuous driving coils potentially applied in the underground pipeline freight delivery system is introduced in this article. Firstly, the detailed structure of the PMLSM with discontinuous driving coils is designed and presented, and the dynamic models of the PMLSM during three different processes are developed. In addition, based on the proportional-integral (PI) and the adaptive disturbance observer (DOB), a multi-loop control strategy is designed for the PMLSM with discontinuous driving coils. The critical performances of PMLSM, such as the speed precision and anti-disturbance ability, are testified in the experiment, and the results validate that the multi-loop control model with adaptive DOB used in the PMLSM with discontinuous driving coils has better speed precision than the PI+PI model by suppressing the detent force.

Adaptive recursive sliding mode based trajectory tracking control for cable-driven continuum robots

To improve the transient response, accuracy and robustness of trajectory tracking control for cable-driven continuum robots (CDCRs), a recursive integral terminal sliding mode control combined with an adaptive disturbance observer (ADO-RITSMC) is proposed. The recursive integral terminal sliding mode control (RITSMC) guarantees fast transient response and high tracking accuracy with a fast zero convergence of the tracking error without chattering. To attenuate the effect of uncertain dynamics, an adaptive disturbance observer (ADO) is constructed to derive uncertain dynamics. Particularly, an improved grey wolf optimizer (IGWO) is merged into the ADO to enhance the estimating accuracy of uncertain dynamic factors. Simulation and experiment results demonstrate the superiority of the ADO-RITSMC in enabling fast transient response, high accuracy and strong robustness of trajectory tracking control.

Parameters self‐turning controller fused with an adaptive nominal model for disturbance observer: Application to direct drive manipulator with significant payload changes

SummaryThe direct drive manipulator, which directly links the joint shaft to the motor rotor, is susceptible to external disturbances and model perturbations. When the direct drive manipulator operates with varying payloads, the disturbance observer (DOB) with a constant nominal model struggles to achieve satisfactory performance. To minimize estimation errors caused by disturbance, enhance trajectory tracking accuracy, and improve system robustness, this article proposes a controller parameter self‐tuning strategy with settling time, fused with a nominal model adaptive disturbance observer. Specifically, by optimizing the gain coefficient of the model reference adaptive identification (MRAI), the significant changes in payload at the manipulator's end joint can be quickly and accurately estimated. The inertia introduced by the payload is then employed to continuously update the nominal model of the manipulator, incorporating information about the inertia of the coupling joints. In addition, the controller parameters are updated using a self‐tuning link with settling time, which guarantees that the closed‐loop characteristics of the system remain unaffected by payload changes. Finally, the proposed method was validated using a two‐joint direct drive manipulator. The results demonstrate the robustness of the proposed method in the presence of significant payload changes, and the accuracy of disturbance estimation and the tracking performance of the proposed control strategy is significantly improved.

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.

STA-based design of an adaptive disturbance observer for autonomous underwater vehicles: From concept to real-time validation

In this paper, we propose to improve the robustness of the well-known PD controller through an adaptable disturbance observer. The proposed disturbance observer is based on the Super-Twisting Algorithm, and is designed with adaptive gains. The stability analysis of the resulting closed-loop controller/observer is achieved using Lyapunov arguments. Real-time experiments in different operating conditions are conducted to demonstrate the effectiveness and robustness of the proposed methodology, compared to the nominal PD control.

Frequency Adaptive Disturbance Observer for Sensorless Control of a 3 × 3-Phase PMA-SynRM Driven by Mono-Inverter

Frequency Adaptive Disturbance Observer for Sensorless Control of a 3 × 3-Phase PMA-SynRM Driven by Mono-Inverter

Fixed-time command-filtered composite adaptive neural fault-tolerant control for strict-feedback nonlinear systems

The research investigates the fixed-time command-filtered composite adaptive neural fault-tolerant (FCCANF) control issue of strict-feedback nonlinear systems (SFNSs). There exist unknown functions and bounded disturbances in the considered systems. Radial basis function neural networks (RBFNNs) will be used in the estimate of the unknown functions. By the serial–parallel estimation models (SPEMs), the forecast biases and the track biases can change the weights of RBFNNs and the approximate characteristics of RBFNNs will be improved. Then, utilizing the novel fixed-time command filter and adaptive disturbance observers, the issue of complex explosion will be effectively solved and the external disturbance is effectively compensated. Subsequently, by utilizing the adaptive control technique, a novel FCCANF controller is developed. Additionally, we have that the system internal variables are bounded and the output variable inclines to a little interval around zero in fixed time which is not determined by the system initial variables. Eventually, numerical and practical examples are shown to prove the availability of the obtained control technique.

Collision avoidance in target encirclement and tracking of unmanned aerial vehicles under a dynamic event-triggered formation control

This paper addresses the dynamic event-triggered fixed-time distributed control problem of unmanned aerial vehicle (UAV) formation for target encirclement and tracking with collision avoidance. A fixed-time radial basis function (RBF) neural network (NN) adaptive disturbance observer is designed for faster estimation and compensation of the lumped disturbances from the UAV formation system. By extending the lemmas of interconnected systems and fixed-time, a novel fixed-time interconnected system lemma is proposed. Then, a dynamic event-triggered control algorithm is proposed to establish the stability of the complex closed-loop interconnected system for target encirclement and formation tracking in fixed time and to demonstrate that the convergence time of the system is independent of the initial state. Subsequently, a risk avoidance mechanism is incorporated into the event-triggered fixed-time control framework to avoid collisions within the UAV formation. The proposed control strategy has been rigorously mathematically demonstrated. Finally, using a visual simulation platform, several simulations are presented to illustrate the remarkable performance and superiority of the proposed control method.

Adaptive Anti-Disturbance Control of Dissolved Oxygen in Circulating Water Culture Systems

In the three-dimensional culture model, the breeding basket of the culture area is symmetrical and it is important to control the dissolved oxygen in the symmetrical region to improve the culture efficiency. Practical engineering issues, such as the influence of flow rate, pH, water temperature, and biological oxygen consumption on the dissolved oxygen content in the circulating water culture system, must be considered along with the presence of modeling errors in the control model. The authors propose an adaptive anti-disturbance control strategy for dissolved oxygen that combines nonlinear disturbance observation with an adaptive sliding model control. Initially, a dynamic model for controlling dissolved oxygen in a recirculating water aquaculture system was developed. The model considers external disturbances like artificial oxygenation, abrupt changes in system flow, and variations in culture oxygen consumption. Secondly, to enhance the robustness and accuracy of controlling dissolved oxygen concentration, the paper introduces a nonlinear adaptive disturbance observer for real-time estimation and observation of external disturbances and system uncertainties. This is accompanied by a sliding-mode control-based adaptive anti-disturbance strategy. Lastly, the simulation results demonstrate that the control strategy proposed in this paper shows resistance to system uncertainties and unknown external disturbances. Furthermore, it reduces the model accuracy requirements for the controller and proves to be suitable for accurately controlling dissolved oxygen in circulating water systems.

Reinforcement learning-based fixed-time resilient control of nonlinear cyber physical systems under false data injection attacks and mismatch disturbances

This paper presents a fixed-time adaptive resilient control framework based on reinforcement learning (RL), mismatch disturbance observer and nonsingular fast terminal sliding mode scheme for nonlinear cyber-physical systems (CPS) to enhance cyber-security under False Data Injection (FDI) attacks, as well as match and mismatch disturbances. The RL algorithm contains an actor-critic neural network (NN) in which the actor NN is employed to estimate the uncertainty while the critic NN is applied to evaluate the performance cost function. Next, to compensate the detrimental effects of attacks and mismatch disturbances, a new sliding mode-based adaptive disturbance observer is designed. To achieve high precision trajectory tracking within a fixed-time interval, a nonsingular fast terminal sliding mode scheme is designed. This scheme ensures fixed-time convergence of the tracking error and facilitates disturbance attenuation and attack mitigation which are the key features of the proposed fixed-time secure control strategy. Finally, the fixed-time stability analysis of is performed through Lyapunov theory. Results reveal the effectiveness of the proposed resilient control for wind turbine system as the nonlinear CPS.

Sensorless Force Estimator for Bilateral Control Systems Using Adaptive Sliding-Mode-Assisted Disturbance Observer

Superior transparency, which includes precise force feedback and position tracking, is the main objective of bilateral operating systems. Therefore, force information estimation or measurement plays a critical role in improving the transparency of bilateral operating systems. This paper proposes a bilateral control scheme combined with an adaptive sliding-mode-assisted disturbance observer (ASMADO) for bilateral operating systems. First, ASMADO, comprising an adaptive disturbance observer and adaptive sliding mode-assisted control law, is used as a force detector to estimate the time-varying force in bilateral control systems. The adaptive disturbance observer with a fixed-time convergent auxiliary dynamic variable is designed without the prior knowledge of the upper bound of the force information derivative, which drives the force estimation error into a small bounded set. Then, the adaptive sliding mode-assisted control law is used to eliminate the estimated residuals of the adaptive disturbance observer in the inner loop of the force estimator. Stability analysis proves that the proposed ASMADO is practical finite-time stable. Subsequently, a bilateral control scheme based on the proposed ASMADO is developed to further improve the system transparency with force feedback and position tracking. Finally, comparative experimental studies are conducted to verify the superior performance of the proposed bilateral controller based on ASMADO.

Disturbance observer-based adaptive neural control for underactuated surface vehicle with constraint of input saturation

In this paper, a robust adaptive neural control algorithm is provided to solve the problem of path-following control for underactuated surface vehicle (USV) subject to input saturation, in the presence of model uncertainties and unknown marine environmental disturbance.In the algorithm, through the application of neural network (NN), an adaptive disturbance observer (ADO) is proposed to observe the unknown disturbance, which does not need the precise information of the upper bound of the disturbance, and the model uncertainty can be appropriated simultaneously. Specially, since the ADO and control law share the same set of NN, the number of adaptive parameters of the controller can be greatly reduced. After that, dynamic surface control (DSC) method is used to avoid repeated derivative in the process of back-stepping, which solve the “calculation explosion” problem. An auxiliary system is designed to compensate errors caused by input saturation, which solve the input saturation problem of actuators. Through Lyapunov stability analysis, it is proved that all signals of the closed-loop system are semi-globally ultimately uniformly bounded (SGUUB). Finally, some experiments are conducted to demonstrate the effectiveness of the algorithm.

Reinforcement learning‐based adaptive consensus control for a class of non‐affine nonlinear multiagent systems with semi‐Markov switching topographies

AbstractA reinforcement learning‐based adaptive consensus control method has been proposed for the non‐affine nonlinear multiagent systems with semi‐Markov switching topographies in this paper. The considered system contains the nonlinear features, multiple source disturbances and non‐affine inputs, which makes it difficult to design the consensus controller. Firstly, to overcome the adverse influence caused by the multiple disturbances existing in the nonlinear system, a reinforcement learning based adaptive disturbance observer has been designed. Secondly, to handle the unknown nonlinear terms and the non‐affine features of the control inputs, an auxiliary loop has been introduced and a reinforcement learning based adaptive control law has been designed. Finally, aiming at the semi‐Markov switching topographies possessed by the multiagent systems, a novel adaptive consensus controller integrating the reinforcement learning approximator, the adaptive disturbance observer and the consensus control law, has been proposed. Through the Lyapunov direct method, it has been proven that the tracking errors are ultimately uniformly bounded. Moreover, within an acceptable margin of error, the state of all the followers can proved to be consistent with the leader. Several numerical experiments are conducted to illustrate the feasibility and effectiveness of the proposed reinforcement learning‐based leader‐following consensus control law.

Finite-time control for systems subject to multiple disturbances

Finite-time stabilisation is studied for a class of nonlinear systems subject to multiple disturbances, in which, the disturbances are divided into two classes, one is the disturbance with partly known information, another is uncertain bounded disturbances. The first one is estimated by constructing an adaptive disturbance observer (ADO), the unknown nonlinear function is approximated by the fuzzy logic systems. Then a finite-time composite controller is constructed by incorporating the disturbance observer-based control (DOBC) with the finite-time stability theory, under which the system state tends to be stable within a finite time. Eventually, the manoeuvrability and validity of the presented tactics are verified by simulation examples.

State and adaptive disturbance observer co‐design for incrementally quadratic nonlinear descriptor systems with nonlinear outputs

AbstractThe problem of designing state and adaptive disturbance observers for a class of nonlinear descriptor systems with the disturbance generated by an unknown exogenous system is addressed in this paper. Especially, the nonlinear terms satisfy incremental quadratic constraints parameterized by a bunch of multiplier matrices, which can represent various classes of nonlinearities. For three different cases in the output equation, the state and adaptive disturbance observers are proposed. Some sufficient conditions formulated in terms of linear matrix inequalities and algebraic constraints are derived to guarantee that state and disturbance error systems, simultaneously, are asymptotically stable with performance level. The availability of proposed observers is validated by two practical examples.

Three-Dimensional Prescribed Performance Tracking Control of UUV via PMPC and RBFNN-FTTSMC

To address the search-and-docking problem in multi-stage prescribed performance switching (MPPS) scenarios, this paper presents a novel compound control method for three-dimensional (3D) underwater trajectory tracking control of unmanned underwater vehicles (UUVs) subjected to unknown disturbances. The proposed control framework can be divided into two parts: kinematics control and dynamics control. In the kinematics control loop, a novel parallel model predictive control (PMPC) law is proposed, which is composed of a soft-constrained model predictive controller (SMPC) and hard-constrained model predictive controller (HMPC), and utilizes a weight allocator to enable switching between soft and hard constraints based on task goals, thus achieving global optimal control in MPPS scenarios. In the dynamics control loop, a finite-time terminal sliding mode control (FTTSMC) method combining a finite-time radial basis function neural network adaptive disturbance observer (RBFNN-FTTSMC) is proposed to achieve disturbance estimation and fast convergence of velocity tracking errors. The simulation results demonstrate that the proposed PMPC-FTTSMC approach achieved an average improvement of 33% and 80% in the number of iterations compared with MPC with sliding mode control (MPC-SMC) and traditional MPC methods, respectively. Furthermore, the approach improved the speed of response by 35% and 44%, respectively, while accurately achieving disturbance observation and enhancing the system robustness.

A novel nonsingular fixed-time control for uncertain bridge crane system using two-layer adaptive disturbance observer

A novel nonsingular fixed-time control for uncertain bridge crane system using two-layer adaptive disturbance observer