Adaptive Fuzzy Observer Research Articles

Antidisturbance Control for AUV Trajectory Tracking Based on Fuzzy Adaptive Extended State Observer.

In this paper, an output-feedback fuzzy adaptive dynamic surface controller (FADSC) based on fuzzy adaptive extended state observer (FAESO) is proposed for autonomous underwater vehicle (AUV) systems in the presence of external disturbances, parameter uncertainties, measurement noises and actuator faults. The fuzzy logic system is incorporated into both the observers and controllers to improve the adaptability of the entire system. The dynamics of the AUV system is established first, considering the external disturbances and parameter uncertainties. Based on the dynamic models, the ESO, combined with a fuzzy logic system tuning the observer bandwidth, is developed to not only adaptively estimate both system states and the lumped disturbances for the controller, but also reduce the impact of measurement noises. Then, the DSC, together with fuzzy logic system tuning the time constant of the low-pass filter, is designed using estimations from the FAESO for the AUV system. The asymptotic stability of the entire system is analyzed through Lyapunov’s direct method in the time domain. Comparative simulations are implemented to verify the effectiveness and advantages of the proposed method compared with other observers and controllers considering external disturbances, parameter uncertainties and measurement noises and even the actuator faults that are not considered in the design process. The results show that the proposed method outperforms others in terms of tracking accuracy, robustness and energy consumption.

Adaptive observer-based H∞ FTC for T-S fuzzy systems. Application to cart motion model

In this paper, an adaptive observer-based fault-tolerant control (FTC) strategy is proposed for a class of Takagi-Sugeno (T-S) fuzzy systems with both actuator and sensor faults under external disturbances. FTC approach is developed to compensate the actuator faults and to stabilize the faulty system. Furthermore, using H∞ optimization technique, an adaptive fuzzy observer is developed, not only to achieve a simultaneous estimation of system states, sensor and actuator faults, but also to attenuate the influence of disturbances. In terms of linear matrices inequalities (LMIs), sufficient conditions of the existence of observer and controller are derived. We overcome the drawback of two-step algorithm by proposing a single-step one which allows to solve only the strict LMIs. Therefore, the obtained results present an acceptable compromise between conservatism reduction and computational complexity. Finally, two numerical examples which one of them is an application to a cart motion model are presented to demonstrate the usefulness of the proposed method.

Adaptive output feedback control for a class of uncertain non-strict feedback systems with asymmetric time-varying output constraints

This study investigated a novel adaptive output feedback control scheme for non-strict feedback nonlinear systems with uncertainties, disturbances, and asymmetric time-varying output constraints. Because that the states of the system are unmeasurable, we used an adaptive fuzzy state observer to obtain the estimated values of the states. To make the output and tracking error satisfy their asymmetric time-varying constraints, an asymmetric time-varying barrier Lyapunov function was adopted. To overcome the “explosion of complexity” problem, we also adopted the dynamic surface control technology. The stability of the closed-loop system was proved by the Lyapunov method, and we give two simulation examples to show the effectiveness of the proposed control method.

Robust Tracking Control of Multijoint Robots based on Adaptive Fuzzy Observer

Robust Tracking Control of Multijoint Robots based on Adaptive Fuzzy Observer

Event-Triggered Composite Adaptive Fuzzy Output-Feedback Control for Path Following of Autonomous Surface Vessels

This article investigates the adaptive fuzzy output-feedback path-following control of autonomous surface vessels (ASVs) via the event-triggered mechanism, in which the fuzzy logic systems (FLSs) are employed to approximate the uncertainties. By transforming the estimation errors, an adaptive fuzzy observer is established to estimate the kinematic states of the ASV, and a serial-parallel estimation model is fabricated based on this observer. By combining the tracking errors with the prediction errors between the observer and the serial-parallel estimation model, the composite adaptive laws are devised for the weights of the FLSs. Then, the event-triggered controller is developed in the channel of sensor to controller, in which the estimated kinematic states, the positional coordinates, and the heading angle of the ASV are sampled only when the triggering condition is violated. In this article, we put forward the problem of “jumps of virtual control laws” at the triggering instants, which commonly exists in the backstepping-based event-triggered control (ETC) in the channel of sensor to controller. To solve this problem, the dynamic surface filters are employed to generate the continuous substitutes for the virtual control laws. The proposed scheme can guarantee at least the similar control performance compared with its continuous alternatives while it importantly reduces the communication burden through the ETC and can enhance the estimation accuracy of the observer by applying the composite learning. All the errors are ensured to be semiglobally uniformly ultimately bounded in the closed-loop system. Finally, the effectiveness of the proposed scheme is verified through the numerical experiment.

Robust Fault Estimation and Fault-Tolerant Control Based on Sliding Mode Observer for Takagi–Sugeno Fuzzy Systems Subject to Actuator and Sensor Faults

In this paper, the problems of fault estimation and fault-tolerant control for Takagi-Sugeno fuzzy system affected by simultaneous actuator faults, sensor faults and external disturbances are investigated. Firstly, an adaptive fuzzy sliding-mode observer is designed to simultaneously estimate system states and both actuator and sensor faults. Then, based on the online estimation information, a static output feedback fault-tolerant controller is designed to compensate for the effect of faults and to stabilize the closed-loop system. Moreover, sufficient conditions for the existence of the proposed observer and controller with an H∞ performance are derived based on Lyapunov stability theory and expressed in terms of linear matrix inequalities. Finally, a nonlinear inverted pendulum with cart system application is given illustrate the validity of the proposed method.

Observer-Based Adaptive Fuzzy Control for Intelligent Ship Autopilot with Input Saturation

In this paper, the design problem of intelligent ship autopilot with unmeasured yaw rate and input saturation is investigated based on adaptive fuzzy control. First, an adaptive fuzzy output feedback controller is proposed, then, to improve the convergence speed of the tracking error in the sense of finite time, a finite-time adaptive fuzzy output feedback controller is also designed to improve the control performance. By employing fuzzy logic system (FLS) to estimate the unknown nonlinear function, an adaptive fuzzy state observer is designed to estimate the unmeasured state. An adaptation auxiliary signal for input saturation is also established to compensate the mismatch between the controller signal and the actuator signal. Combining state observer model with backstepping method, two different adaptive fuzzy output-feedback controllers with composite parameter adaptive laws are constructed, respectively. Based on Lyapunov theory, it is proved that all signals in the closed-loop systems are bounded. Finally, the simulation results are conducted to demonstrate the effectiveness of the proposed schemes.

Anti-Disturbance Control for Quadrotor UAV Manipulator Attitude System Based on Fuzzy Adaptive Saturation Super-Twisting Sliding Mode Observer

Aerial operation with unmanned aerial vehicle (UAV) manipulator is a promising field for future applications. However, the quadrotor UAV manipulator usually suffers from several disturbances, such as external wind and model uncertainties, when conducting aerial tasks, which will seriously influence the stability of the whole system. In this paper, we address the problem of high-precision attitude control for quadrotor manipulator which is equipped with a 2-degree-of-freedom (DOF) robotic arm under disturbances. We propose a new sliding-mode extended state observer (SMESO) to estimate the lumped disturbance and build a backstepping attitude controller to attenuate its influence. First, we use the saturation function to replace discontinuous sign function of traditional SMESO to alleviate the estimation chattering problem. Second, by innovatively introducing super-twisting algorithm and fuzzy logic rules used for adaptively updating the observer switching gains, the fuzzy adaptive saturation super-twisting extended state observer (FASTESO) is constructed. Finally, in order to further reduce the impact of sensor noise, we invite a tracking differentiator (TD) incorporated into FASTESO. The proposed control approach is validated with effectiveness in several simulations and experiments in which we try to fly UAV under varied external disturbances.

Observer and fault‐tolerant controller design for discrete‐time multiple state‐delayed T–S fuzzy systems

This study is concerned with observer-based fault estimation (FE) and fault-tolerant controller design for a class of discrete-time Takagi–Sugeno (T–S) fuzzy systems. There exist multiple time-varying state delays, sensor and actuator faults, local non-linear dynamics and exogenous disturbances in the systems. In comparison with the existing results, the approach suggested in this study is more flexible and feasible. By means of the n-step induction FE, a novel fuzzy adaptive descriptor observer is developed to obtain the n-step error functions. Then, an active dynamic output feedback fault-tolerant controller is designed to stabilise the closed-loop fuzzy system. Furthermore, a set of delay-dependent sufficient conditions are provided by the fuzzy Lyapunov function which utilises the form of linear matrix inequalities. The stability results from the observer and the controller in this study have less conservatism compared with the ones from the existence of observers and fault-tolerant controllers. At last, a simulation example is presented to demonstrate the advantages and effectiveness of the approach proposed in the study.

Hybrid Fault Diagnosis of Bearings: Adaptive Fuzzy Orthonormal-ARX Robust Feedback Observer

Rolling-element bearings (REBs) make up a class of non-linear rotating machines that can be applied in several activities. Conceding a bearing has complicated and uncertain behavior that exhibits substantial challenges to fault diagnosis. Thus, the offered anomaly-diagnosis algorithm, based on a fuzzy orthonormal-ARX adaptive fuzzy logic-structure feedback observer, is developed. A fuzzy orthonormal-ARX algorithm is presented for non-stationary signal modeling. Next, a robust, stable, reliable, and accurate observer is developed for signal estimation. Therefore, firstly, a classical feedback observer is implemented. To address the robustness drawback found in feedback observers, a multi-structure technique is developed. Furthermore, to generate signal estimation performance and reliability, the fuzzy logic technique is applied to the structure feedback observer. Also, to improve the stability, reliability, and robustness of the fuzzy orthonormal-ARX fuzzy logic-structure feedback observer, an adaptive algorithm is developed. After generating the residual signals, a support vector machine (SVM) is developed for the detection and classification of the bearing fault conditions. The effectiveness of the proposed procedure is validated using two different datasets for single-type fault diagnosis based on the Case Western Reverse University (CWRU) vibration dataset and multi-type fault diagnosis of bearing using the Smart Health Safety Environment (SHSE) Lab acoustic emission dataset. The proposed algorithm increases the classification accuracy from 86% in the SVM-based fuzzy orthonormal-ARX feedback observer to 97.5% in single-type fault and from 80% to 98.3% in the multi-type faults.

Dissipativity-Based Disturbance Attenuation Control for T-S Fuzzy Markov Jumping Systems With Nonlinear Multisource Uncertainties and Partly Unknown Transition Probabilities.

This article is concerned with the dissipativity-based disturbance attenuation control for a class of Takagi-Sugeno (T-S) fuzzy Markov jump systems (FMJSs) suffering from nonlinear multisource disturbances. The considered system possesses nonlinear and stochastic jumping disturbances generated by multiple sources, constituting the main challenge to control design and dissipativity analysis. By proposing an adaptive fuzzy disturbance observer and a hybrid feedback controller, a novel fuzzy disturbance attenuation control structure has been constructed. In terms of strict linear matrix inequalities (LMIs), a new sufficient condition is established to guarantee the (Z, Y, X)-ε- dissipative and stochastic exponentially stability of the closed-loop FMJSs. Furthermore, for the concerned FMJSs with partly unknown transition probabilities, the sufficient conditions are also derived and the gains of controller or observer can be computed immediately. Finally, a numerical example is provided to verify the effectiveness of the proposed theory.

Induction Motor Performance Improvement Using Twelve Sectors DTC and Fuzzy Logic Speed Regulation

The direct torque control (DTC) was proposed as an alternative to the vector control in the middle of 1980s. This strategy is based on the direct determination of inverter switching states and offers a simpler scheme and less sensitivity to machine parameters. However, the variable switching frequency of DTC causes high flux and torque ripples which lead to an acoustical noise and degrade the performance of the control scheme, especially at low-speed regions. In the objective of improving the performance of DTC for the induction motor, a fuzzy logic based non-linear speed controller is inserted to ensure a robust control against different uncertainties and external disturbances. Moreover, the sensorless control can increase the reliability and decrease the cost of the control system. Therefore, a fuzzy adaptive Luenberger observer is used to improve the speed and the flux estimation.

Adaptive Prescribed Performance Output Regulation of Nonlinear Systems with Nonlinear Exosystems

The paper studies the prescribed performance output regulation problem for a class of nonlinear uncertain strict-feedback systems which are driven by nonlinear exosystems. Fuzzy logic systems (FLSs) are used to approximate the unknown functions. A fuzzy adaptive observer is designed to estimate the system state. A fuzzy internal model is designed to reject disturbance. Simultaneously, a parameter-type Lyapunov function and L function are designed to ensure the prescribed performance of the system. As illustrated by example, the proposed prescribed performance fuzzy adaptive output feedback control scheme is shown to guarantee semi-globally uniformly ultimately bounded, and the dynamic performance and steady-state performance of the tracking error are dependent on the prescribed performance functions.

Fault Estimation and Tolerant Control for Discrete-Time Multiple Delayed Fuzzy Stochastic Systems With Intermittent Sensor and Actuator Faults

This article is concerned with observer-based fault estimation (FE) and tolerant controller design for a series of discrete-time Takagi-Sugeno (T-S) fuzzy stochastic systems. There exist multiple time-varying state delays, intermittent sensor and actuator faults, nonlinear dynamics, and exogenous disturbances in the systems. Compared with the results of the existence, this approach suggested in this article is more flexible and feasible. By means of the FE information, a novel fuzzy adaptive descriptor observer is developed to obtain the error dynamics. Then, an active observer-based fault-tolerant controller is designed to stabilize the closed-loop fuzzy system. Furthermore, a set of delay-dependent sufficient conditions are provided by the fuzzy Lyapunov function with the way of linear matrix inequalities (LMIs), which has less conservatism compared with the ones of the existing observers and fault-tolerant controllers. Finally, a simulation example is shown to illustrate the advantages and effectiveness of this approach depicted in this article.

Adaptive Fuzzy Output-Feedback Control for Nonaffine MIMO Nonlinear Systems With Prescribed Performance

This article proposes an adaptive fuzzy output-feedback control approach for a class of nonaffine multi-input multi-output nonlinear systems with prescribed performance and immeasurable states. For most of the existing fuzzy approximation-based approaches, there are many parameters in the fuzzy systems that need to be estimated online with adaptive laws. To overcome this difficulty, we propose a new fuzzy system; the remarkable advantage of this fuzzy system is that only one parameter needs to be estimated online. The fuzzy system is used to approximate the unknown nonlinear function, and an adaptive fuzzy state observer is developed to estimate the unmeasured states. By utilizing the state observer and the prescribed performance control scheme, an adaptive fuzzy prescribed performance output-feedback control method is developed. The number of the adaptive laws both for the state observer and the controller is reduced to the number of subsystems. It is proved that all the signals in the closed-loop systems are semiglobally uniformly ultimately bounded, and that the prescribed performance error constraints can be achieved. Simulation examples are provided to highlight the effectiveness of the proposed approach.

Observer-based adaptive fuzzy prescribed performance control for feedback linearizable MIMO nonlinear systems with unknown control direction

This paper investigates the prescribed performance tracking control problem for a class of feedback linearizable multi-input and multi-output (MIMO) nonlinear systems with immeasurable states and unknown control direction. The fuzzy systems are employed to approximate the unknown nonlinear functions, an adaptive fuzzy state observer is developed to estimate the unmeasured states. By utilizing the state observer, an adaptive fuzzy output-feedback controller equipped with the Nussbaum-type function is developed to solve the unknown control direction issues. It is proved that all the signals in the closed-loop systems are bounded and that the prescribed performance error constraints can be achieved. Simulation examples are provided to highlight the effectiveness of the proposed approach.

Adaptive Fuzzy Nonsmooth Backstepping Output-Feedback Control for Hypersonic Vehicles With Finite-Time Convergence

It is commonly believed that uncertainties are obstacles to the tracking performances of flexible air-breathing hypersonic vehicles (FAHVs). In addition, the estimation of unmeasured states in an FAHV makes this issue much more complicated. To deal with these difficulties, this article explores a novel adaptive fuzzy nonsmooth backstepping output-feedback control scheme for the FAHV with closed-loop finite-time convergence. First, to approximate the unknown dynamics of the FAHV, some function approximators are constructed by utilizing an interval type-2 (IT2) fuzzy logic system. On this basis, a fixed-time convergent adaptive IT2 fuzzy observer is designed to estimate the unmeasured flight path angle and the unmeasured angle of attack of the FAHV accurately and rapidly. Consequently, the estimation errors of the designed observer are convergent in a fixed time independent of their initial estimation errors. Furthermore, based on the estimated states, velocity and altitude tracking controllers are designed by using a finite-time adaptive IT2 fuzzy nonsmooth backstepping control technique, which avoids the problem of “explosion of complexity” in traditional backstepping methods. Subsequently, rigorous Lyapunov stability analysis is conducted to show the finite-time convergence of the closed-loop signals of the FAHV control system. Finally, the robustness and superiority of the proposed control scheme is validated by several simulations in representative scenarios.

Adaptive fuzzy observer based hierarchical sliding mode control for uncertain 2D overhead cranes

ABSTRACTThis paper proposes a new approach to robustly control a 2D under-actuated overhead crane system, where a payload is effectively transported to a destination in real time with small sway angles, given its inherent uncertainties such as actuator nonlinearities and external disturbances. The control law is proposed to be developed by the use of the robust hierarchical sliding mode control (HSMC) structure in which a second-level sliding surface is formulated by two first-level sliding surfaces drawn on both actuated and under-actuated outputs of the crane. The unknown and uncertain parameters of the proposed control scheme are then adaptively estimated by the fuzzy observer (FO), where the adaptation mechanism is derived from the Lyapunov theory. More importantly, stability of the proposed strategy is theoretically proved. Effectiveness of the proposed adaptive FO-based HSMC approach was extensively validated by implementing the algorithm in both synthetic simulations and real-life experiments, where the results obtained by our method are highly promising.

A modeling error-based adaptive fuzzy observer approach with input saturation analysis for robust control of affine and non-affine systems

In this paper, a robust control approach is applied for both MIMO/SISO affine/non-affine nonlinear systems based on a modeling error-based adaptive fuzzy observer controller, in the presence of input saturation. In the proposed scheme, non-affine nonlinear systems can be transformed to affine systems and unknown higher-order term of expansion (HOTE) that appears due to the use of this method can be estimated by an adaptive fuzzy technique. Using the modeling error between the system states observer and a serial–parallel estimator model, a modeling error-based adaptive fuzzy observer estimator is proposed that uses the modeling error as the input of fuzzy system to approximate and adaptively compensate the unknown HOTE and also the external disturbance. The proposed scheme is able to hold control performance in the presence of input saturation. An analysis of the controlled system is presented to verify the stability of the system under control. The stability of the closed-loop system is provided based on the strictly positive real condition and Lyapunov theory. The proposed approach is effectual and robust. The simulation results demonstrate the usefulness of the proposed method for both MIMO and SISO systems.

Fuzzy State Observer‐Based Adaptive Dynamic Surface Control of Nonlinear Systems with Time‐Varying Output Constraints

In this paper, a new fuzzy dynamic surface control approach based on a state observer is proposed for uncertain nonlinear systems with time‐varying output constraints and external disturbances. An adaptive fuzzy state observer is used to estimate the states that cannot be measured in the systems. In our method, a time‐varying Barrier Lyapunov Function (BLF) is used to ensure that the output does not violate time‐varying constraints. In addition, dynamic surface control (DSC) technology is applied to overcome the problem of “explosion of complexity” in a backstepping control. Finally, the stability and signal boundedness of the system are confirmed by the Lyapunov method. The simulation results show the effectiveness and correctness of the proposed method.