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1.
This paper investigates a novel strategy which can address the fault-tolerant control (FTC) problem for nonlinear strict-feedback systems containing actuator saturation, unknown external disturbances, and faults related to actuators and components. In such method, the unknown dynamics including faults and disturbances are approximated by resorting to Neural-Networks (NNs) technique. Meanwhile, a back-stepping technique is employed to build a fault-tolerant controller. It should be stressed that the main advantage of this strategy is that the NN weights are updated online based on gradient descent (GD) algorithm by minimizing the cost function with respect to NNs approximation error rather than regarding weights as adaptive parameters, which are designed according to Lyapunov theory. In addition, the convergence proof of NN weights and the stability proof of the proposed FTC method are given. Finally, simulation is performed to demonstrate the effectiveness of the proposed strategy in dealing with unknown external disturbances, actuator saturation and the faults related to the components and actuators, simultaneously.  相似文献   

2.
This paper focuses on the problem of adaptive output feedback control for a class of uncertain nonlinear systems with input delay and disturbances. Radial basis function neural networks (NNs) are employed to approximate the unknown functions and an NN observer is constructed to estimate the unmeasurable system states. Moreover, an auxiliary system is introduced to compensate for the effect of input delay. With the aid of the backstepping technique and Lyapunov stability theorem, an adaptive NN output feedback controller is designed which can guarantee the boundedness of all the signals in the closed-loop systems. Finally, a simulation example is given to illustrate the effectiveness of the proposed method.  相似文献   

3.
This paper studies the problem of adaptive neural network (NN) output-feedback control for a group of uncertain nonlinear multi-agent systems (MASs) from the viewpoint of cooperative learning. It is assumed that all MASs have identical unknown nonlinear dynamic models but carry out different periodic control tasks, i.e., each agent system has its own periodic reference trajectory. By establishing a network topology among systems, we propose a new consensus-based distributed cooperative learning (DCL) law for the unknown weights of radial basis function (RBF) neural networks appearing in output-feedback control laws. The main advantage of such a learning scheme is that all estimated weights converge to a small neighborhood of the optimal value over the union of all system estimated state orbits. Thus, the learned NN weights have better generalization ability than those obtained by traditional NN learning laws. Our control approach also guarantees the convergence of tracking errors and the stability of closed-loop system. Under the assumption that the network topology is undirected and connected, we give a strict proof by verifying the cooperative persisting excitation condition of RBF regression vectors. This condition is defined in our recent work and plays a key role in analyzing the convergence of adaptive parameters. Finally, two simulation examples are provided to verify the effectiveness and advantages of the control scheme proposed in this paper.  相似文献   

4.
This paper studies the event-triggered consensus control problem for high-order uncertain nonlinear multi-agent systems with actuator saturation. By using a smooth Lipschitz function to approximate the saturation nonlinearity, an augment system and the Nussbaum function are adopted to deal with the residual terms of saturation nonlinearity based on adaptive backstepping method. Since excessive energy and communication resources will be consumed during the procedure to handle actuator saturation, two event-triggered mechanisms are proposed to save the communication resources and reduce the controllers’ update frequency. Whenever the triggered conditions are satisfied, the control signals transmitted to the actuators are updated and broadcasted to the neighboring area. A ’disturbance-like’ term is integrated so that the event-triggered control problem with actuator saturation can be transformed into a robust problem while the unknown disturbances are tackled by adaptive update laws. Moreover, the requirement for global communication topology known by all the agents is relaxed by introducing new estimators. All the signals in the closed-loop system are uniformly bounded and the consensus tracking errors are exponentially converged to a bounded set. Meanwhile, the Zeno behavior is excluded. Simulation results are employed to validate the advantages of our proposed methods.  相似文献   

5.
This paper is devoted to adaptive neural network control issue for a class of nonstrict-feedback uncertain systems with input delay and asymmetric time-varying state constraints. State-related external disturbances are involved into the system, and the upper bounds of disturbances are assumed as functions of state variables instead of constants. Additionally, during the approximations of unknown functions by neural networks, the online computation burdens are declined sharply, since the norms of neural network weight vectors are only estimated. In the process of dealing with input delay, an auxiliary function is applied such that the conditions for time delay are more general than the ones in existing literature. A novel adaptive neural network controller is designed by constructing the asymmetric barrier Lyapunov function, which guarantees that the output of system has a good tracking performance and the state variables never violate the asymmetric time-varying constraints. Finally, numerical simulations are presented to verify the proposed adaptive control scheme.  相似文献   

6.
This paper is concerned with the problem of adaptive disturbance attenuation for a class of nonlinear systems. The traditional adaptive methods are almost impossible to compensate the time-varying unknown disturbance by designing parameter adaptive laws without a priori knowledge about the bounds of external disturbances. To solve the problem, a new strategy is proposed by constructing an augmented system where the external disturbance is considered as another component of the augmented state vector. Based on this, a double-gain nonlinear observer is employed to estimate the state of the augmented nonlinear system. Further, an output feedback control strategy is designed, and it is proved that the proposed strategy ensures that all the signals are bounded and the tracking error exponentially converges to an adjustable compact set. Finally, an example is performed to demonstrate the validity of the proposed scheme.  相似文献   

7.
This paper studies the problem of composite control for a class of uncertain Markovian jump systems (MJSs) with partial known transition rates, multiple disturbances and actuator saturation. Compared with the existing results, a novel robust composite control scheme is put forward by virtue of adaptive neural network technique. For MJSs, the partial unknown information on transition rates and the actuator saturation influence the design of disturbance observer and the robust H∞ controller. Firstly, without taking account of external disturbances, the network reconstruction error and saturation, a novel robust adaptive control strategy is established to ensure that all the signals of the closed-loop system are asymptotically bounded in mean square. Secondly, the solvability condition for ensuring the robust H∞ performance is given by using a modified adaptive law, where the saturation is treated as a disturbance-like signal. Finally, the simulations for a numerical example and an application example are performed to validate the effectiveness of the proposed results.  相似文献   

8.
This paper studies the adaptive asymptotically stabilizing control problem for a class of uncertain nonlinear systems by the partial-state feedback. The input-to-state stability (ISS) is used to describe the dynamic uncertainties, and a novel Nussbaum function is resorted to counteract the unknown identical control directions. The damping terms with the estimates of unknown disturbance bounds are inserted in control design to handle the nonvanishing external disturbances. It can be seen that all signals in closed-loop are bounded and the system states converge to zero asymptotically in spite of the uncertainties. Finally, a simulation example is introduced to show the effectiveness of the presented control scheme.  相似文献   

9.
In this paper, an adaptive attitude coordination control problem for spacecraft formation flying is investigated under a general directed communication topology containing a directed spanning tree with a leader as the root. In the presence of unknown time-varying inertia, persistent external disturbances and control input saturation, a novel robust adaptive coordinated attitude control algorithm with no prior knowledge of inertia for spacecraft is proposed to coordinately track the common time-varying reference states. Aiming at optimizing the control algorithm, a dynamic adjustment function is introduced to adjust the control gain according to the tracking errors. The effectiveness of the proposed control scheme is illustrated through numerical simulation results.  相似文献   

10.
Aiming at the trajectory tracking of a free-flying flexible-joint space robot (FFSR) with unknown time-varying disturbances and input saturation, we develop a robust control law with prescribed performance constraints via backstepping technique. A disturbance observer is employed to estimate the unknown time-varying disturbances and two auxiliary systems are introduced to handle input saturation. Moreover, we use the dynamic surface control (DSC) technique to deal with the complexity explosion caused by multiple derivatives of the virtual control signals. The performance function and transformation function are utilized to improve the tracking performance. It is proved that the designed control law can maintain the tracking error of the FFSR within a predefined region, while guaranteeing the uniform ultimate boundedness of all signals in the FFSR closed-loop control system. Finally, simulations are carried out to demonstrate the effectiveness of the developed prescribed performance tracking control.  相似文献   

11.
In this paper, an adaptive concave barrier function scheme coupled with the non-singular terminal sliding mode control technique is proposed for finite-time tracking control of the under-actuated nonlinear system in the existence of model uncertainty, external disturbance and input saturation. Firstly, the dynamical equation of under-actuated nonlinear n-order system is expressed under model uncertainty, external disturbance and input saturation. Secondly, for the improvement of stability performance of the system in the existence of input saturation, a compensation system is designed to overcome the constraint on the control input. Afterward, the tracking errors between actual states of the system and differentiable reference signals are defined and the sliding surface based on the defined tracking errors is presented. Then, for gaining the better transient and steady-state performance of the closed-loop system, the prescribed performance control scheme is adopted. Based on this method, the transformed prescribed form of the previous determined sliding surface is obtained to ensure that the sliding surface can reach to a predefined region. Afterward, for assurance of the finite-time reachability of transformed sliding surface, the nonsingular terminal sliding surface is recommended. In addition, for the compensation of the model uncertainty and external disturbance existed in the system, the adaptive-based concave barrier function technique is used to estimate the unknown bounds of uncertainty and exterior disturbance. Finally, for demonstration of the proposed control method, the simulations and experimental implementation are done on the air levitation system.  相似文献   

12.
In this paper, the adaptive prescribed performance tracking control of nonlinear asymmetric input saturated systems in strict-feedback form is addressed under the consideration of model uncertainties and external disturbances. A radial basis function neural network (RBF-NN) is utilized to handle the model uncertainties. By prescribed performance functions, the transient performance of the system can be guaranteed. The continuous Gaussian error function is represented as an approximation of asymmetric saturation nonlinearity such that the backstepping technique can be leveraged in the control design. Based on the Lyapunov synthesis, residual function approximation inaccuracies and external disturbances are compensated by constructed adaptive control laws. As a consequence, all the signals in the closed-loop system are uniformly ultimately bounded and the tracking errors bounded by prescribed functions converge to a small neighbourhood of zero. The proposed method is applied to the autonomous underwater vehicles (AUVs) with extensive simulation results demonstrating the effectiveness of the proposed method.  相似文献   

13.
In this paper, a new framework of the robust adaptive neural control for nonlinear switched stochastic systems is established in the presence of external disturbances and system uncertainties. In the existing works, the design of robust adaptive control laws for nonlinear switched systems mainly relies on the average dwell time method, while the design and analysis based on the model-dependent average dwell time (MDADT) method remains a challenge. An improved MDADT method is developed for the first time, which greatly relaxes the requirements of Lyapunov functions of any two subsystems. Benefiting from the improved MDADT, a switched disturbance observer for discontinuous disturbances is proposed, which realizes the real-time gain adjustment. For known and unknown piecewise continuous nonlinear functions, a processing method based on the tracking differentiator and the neural network is proposed, which skillfully guarantees the continuity of the control law. The theoretical proof shows that the semiglobal uniform ultimate boundedness of all closed-loop signals can be guaranteed under switching signals with MDADT property, and simulation results of the longitudinal maneuvering control at high angle of attack are given to further illustrate the effectiveness of the proposed framework.  相似文献   

14.
In this paper, the problem of adaptive tracking control is investigated for nonlinear systems with asymmetric actuator backlash. We assume that the nonlinearities of the systems are unknown and the external disturbances are bounded. First, the control input will be quantized by a hysteresis-type quantizer, which can reduce the communication rate of the control signal. Then, the asymmetric actuator backlash is approximated to a new model, and a novel adaptive controller with the quantizer is designed via an adaptive backstepping technique to guarantee all the signals of the closed-loop tracking error system are uniform ultimate boundedness. Finally, the simulation results are presented to demonstrate the effectiveness of the proposed algorithm.  相似文献   

15.
The comprehensive effect of external disturbance, measurement delay, unmeasurable states and input saturation makes the difficulties and challenges for a HAGC system. In this paper, an adaptive fuzzy output feedback control scheme is designed for a HAGC system under the simultaneous consideration of those factors. At the first place, by state transformation technique, the dynamic model of a HAGC system is simply expressed as a strict feedback form, where measurement delay is converted into input delay. Then, an auxiliary system is employed to compensate for the effect of input delay. Furthermore, an asymmetric barrier Lyapunov function (BLF) is constructed to ensure the output error constraint requirement of thickness error and the fuzzy observer is established to solve unmeasurable states, unknown nonlinear functions at the same time. With the aid of backstepping method, adaptive fuzzy controller is developed to assure that the closed-loop system is semi-globally boundedness and the output error of thickness error doesn’t violate its constraint. At the end, compared simulations are carried out to verify the efficiency of the proposed control scheme.  相似文献   

16.
In this paper, an adaptive finite-time funnel control for non-affine strict-feedback nonlinear systems preceded by unknown non-smooth input nonlinearities is proposed. The input nonlinearities include backlash-like hysteresis and dead-zone. Unknown nonlinear functions are handled using fuzzy logic systems (FLS), based on the universal approximation theorem. An improved funnel error surface is utilized to guarantee the steady-state and transient predetermined performances while the differentiability problem in the controller design is averted. Using the Lyapunov approach, all the adaptive laws are extracted. In addition, an adaptive continuous robust term is added to the control input to relax the assumption of knowing the bounds of uncertainties. All the signals in the closed-loop system are shown to be semi-globally practically finite-time bounded with predetermined performance for output tracking error. Finally, comparative numerical and practical examples are provided to authenticate the efficacy and applicability of the proposed scheme.  相似文献   

17.
This paper focuses on the optimal tracking control problem (OTCP) for the unknown multi-input system by using a reinforcement learning (RL) scheme and nonzero-sum (NZS) game theory. First, a generic method for the OTCP of multi-input systems is formulated with steady-state controls and optimal feedback controls based on the NZS game theory. Then a three-layer neural network (NN) identifier is introduced to approximate the unknown system, and the input dynamics are obtained by using the derivative of the identifier. To transform the OTCP into a regulation optimal problem, an augmentation of the multi-input system is constructed by using the tracking error and the commanded trajectory. Moreover, we use an NN-based RL method to online learn the optimal value functions of all the inputs, which are then directly used to calculate the optimal tracking controls. All the NN weights are tuned synchronously online with a newly introduced adaptation based on the estimation error. The convergence of the NN weights and the stability of the closed-loop system are analyzed. Finally, a two-motor driven servo system and another nonlinear system are presented to illustrate the feasibility of the algorithm for both linear and nonlinear multi-input systems.  相似文献   

18.
《Journal of The Franklin Institute》2022,359(18):10483-10509
In this paper, a fast fixed-time vertical plane motion controller is proposed for autonomous underwater gliders (AUGs) gliding in shallow water. The influence of speed-sensorless conditions, model uncertainties, unknown time-varying external disturbances, input saturations, and state delay are taken into account. To improve control performance, a fast fixed-time stable system is first presented. Based on the system, an adaptive extended state observer (ESO) is developed for estimating speed, model uncertainties, and external disturbances. A fast fixed-time controller is designed for improving the gliding efficiency and reducing the risk of hitting the ocean floor. Moreover, an input saturation auxiliary system and an advance compensation method are presented to cope with input saturations and state delay. According to Lyapunov theory, it is proved that the AUG states can converge into a small neighborhood within a fixed time. Finally, simulation results demonstrate the rapidity and effectiveness of the designed control method.  相似文献   

19.
In this paper, global practical tracking is investigated via output feedback for a class of uncertain nonlinear systems subject to unknown dead-zone input. The nonlinear systems under consideration allow more general growth restriction, where the growth rate includes unknown constant and output polynomial function. Without the precise priori knowledge of dead-zone characteristic, an input-driven observer is designed by introducing a novel dynamic gain. Based on non-separation principle, a universal adaptive output feedback controller is proposed by combining dynamic high-gain scaling approach with backstepping method. The controller proposed guarantees that the closed-loop output can track any smooth and bounded reference signal by any small pre-given tracking error, while all closed-loop signals are globally bounded. Finally, simulation examples are given to illustrate the effectiveness of our dynamic output feedback control scheme.  相似文献   

20.
This paper proposes an observer-based fuzzy adaptive output feedback control scheme for a class of uncertain single-input and single-output (SISO) nonlinear stochastic systems with quantized input signals and arbitrary switchings. The SISO system under consideration contains completely unknown nonlinear functions, unmeasured system states and quantized input signals quantized by a hysteretic quantizer. By adopting a new nonlinear disposal of the quantized input, the relationship between the control input and the quantized input is established. The hysteretic quantizer that we take can effectively avoid the chattering phenomena. Furthermore, the introduction of a linear observer makes the estimation of the states possible. Based on the universal approximation ability of the fuzzy logic systems (FLSs) and backstepping recursive design with the common stochastic Lyapunov function approach, a quantized output feedback control scheme is constructed, where the dynamic surface control (DSC) is explored to alleviate the computation burden. The proposed control scheme cannot only guarantee the boundedness of signals but also make the output of the system converge to a small neighborhood of the origin. The simulation results are exhibited to demonstrate the validity of the control scheme.  相似文献   

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