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1.
For a class of switched nonlinear systems with unmatched external disturbances and unknown backlash-like hysteresis, an adaptive fuzzy-based control strategy is proposed to handle the anti-disturbance issue. The unmatched external disturbances come from a switched exosystem. Our aim is to achieve the output tracking performance and the disturbance attenuation by using the adaptive fuzzy-based composite anti-disturbance control technique. First, based on the fuzzy logics, we design a switching adaptive fuzzy disturbance observer to estimate unmatched external disturbances. Second, a composite switching adaptive anti-disturbance controller is constructed. By means of the backstepping technique, disturbance estimations are added in each virtual control to offset the unmatched disturbances, which results in the different coordinate transformations. At last, the availability of the proposed approach is illustrated by a mass-spring-damper system.  相似文献   

2.
Model reference adaptive control algorithms with minimal controller synthesis have proven to be an effective solution to tame the behaviour of linear systems subject to unknown or time-varying parameters, unmodelled dynamics and disturbances. However, a major drawback of the technique is that the adaptive control gains might exhibit an unbounded behaviour when facing bounded disturbances. Recently, a minimal controller synthesis algorithm with an integral part and either parameter projection or σ-modification strategies was proposed to guarantee boundedness of the adaptive gains. In this article, these controllers are experimentally validated for the first time by using an electro-mechanical system subject to significant rapidly varying disturbances and parametric uncertainty. Experimental results confirm the effectiveness of the modified minimal controller synthesis methods to keep the adaptive control gains bounded while providing, at the same time, tracking performances similar to that of the original algorithm.  相似文献   

3.
Although the drive-response synchronization problem of memristive recurrent neural networks (MRNNs) has been widely investigated, all the existing results are based on the assumption that the parameters of the drive system are known in prior, which are difficult to implement in real-life applications. In the present paper, a Stop and Go adaptive strategy is proposed to investigate the synchronization control of chaotic delayed MRNNs with unknown memristive synaptic weights. Firstly, by defining a series of measurable logical switching signals, a switched response system is constructed. Subsequently, by utilizing the logical switching signals, several suitable parameter update laws are proposed, then some different adaptive controllers are devised to guarantee the synchronization of unknown MRNNs. Since the parameter update laws are weighted by the logical switching signals, they will work or stop automatically with the switch of the unknown weights of drive system. Finally, two numerical examples with their computer simulations are provided to illustrate the effectiveness of the proposed adaptive synchronization schemes.  相似文献   

4.
This paper focuses on the optimal control of a DC torque motor servo system which represents a class of continuous-time linear uncertain systems with unknown jumping internal dynamics. A data-driven adaptive optimal control strategy based on the integration of adaptive dynamic programming (ADP) and switching control is presented to minimize a predefined cost function. This takes the first step to develop switching ADP methods and extend the application of ADP to time-varying systems. Moreover, an analytical method to give the initial stabilizing controller for policy iteration ADP is proposed. It is shown that under the proposed adaptive optimal control law, the closed-loop switched system is asymptotically stable at the origin. The effectiveness of the strategy is validated via simulations on the DC motor system model.  相似文献   

5.
In this paper, we consider the consensus problem of a class of heterogeneous multi-agent systems composed of the linear first-order and second-order integrator agents together with the nonlinear Euler–Lagrange (EL) agents. First, we propose a distributed consensus protocol under the assumption that the parameters of heterogeneous system are exactly known. Sufficient conditions for consensus are presented and the consensus protocol accounting for actuator saturation is developed. Then, by combining adaptive controller and PD controller together, we design a protocol for the heterogeneous system with unknown parameters (in the nonlinear EL dynamics). Based on graph theory, Lyapunov theory and Barbalat's Lemma, the stability of the controllers is proved. Simulation results are also provided to illustrate the effectiveness of the obtained results.  相似文献   

6.
A leader-following synchronous control is proposed in multiple electrohydraulic actuators (MEHAs) under distributed switching topologies to guarantee the follower electrohydraulic actuators (EHAs) tracking the leader motion. Each EHA has a 3-orders nonlinear dynamics with lumped uncertainties involving uncertain hydraulic parameters and unknown external load. Then a quasi-synchronization controller together with a high-gain disturbance observer is designed by Lyapunov techniques to guarantee the synchronous errors asymptotically convergence to a zero neighborhood. Finally, the effectiveness of the proposed quasi-synchronous controller is verified by both simulation and experimental bench such that the finite EHA nodes achieve leader-following synchronous motion under distributed switching topologies.  相似文献   

7.
This paper deals with the synchronization control of power complex networks with switching parameters. In the meantime, the node state constraints are considered during the synchronization process. Admittedly, synchronization problem encountered in power complex networks is becoming progressively important due to the increasing connection and disconnection operations resulting from sustainable energy and controllable load. Hereon, the network model considering switching parameters of each node is established to describe the topology variation of power systems that may be confronted in practical terms. Then, by utilizing the adaptive backstepping technique with a barrier Lyapunov function (BLF), a novel synchronization controller is constructed recursively which accomplishes the nodes full states tracking within the predefined transient behavior. Owing to the characteristic of BLF, the designed controller as well as its adaptive law could guarantee both the constrained state of each node restricted by a prescribed range and the synchronization performance. Meanwhile, the bounded output of the system could track the desired trajectory. Finally, scenario simulations are performed to demonstrate the effectiveness and superiority of the proposed method.  相似文献   

8.
This paper presents the design of a hysteresis band controller to regulate the switching frequency in a sliding mode controlled nonlinear Boost power converter. The proposed architecture relies on a piecewise linear modeling of the switching function behavior within the hysteresis band, and consists of a continuous-time integral-type controller that modifies the amplitude of the hysteresis band of the comparator in accordance with the error between the desired and the actually measured switching period. The study provides the dynamical models of the converter operating in sliding mode and the switching frequency control loop. Moreover, the design of the parameters of both the sliding mode control and the switching frequency controller guarantee the fulfilment of the desired output voltage regulation of the Boost converter and the steady state setting of the switching frequency with a known, taylored dynamics. A Boost power converter prototype has been built to validate the proposal. Experimental results confirm the predicted good performance of the controllers, as well as the robustness with respect to changes in the switching frequency reference and the system parameters.  相似文献   

9.
This work aims to design a neural network-based fractional-order backstepping controller (NNFOBC) to control a multiple-input multiple-output (MIMO) quadrotor unmanned aerial vehicle (QUAV) system under uncertainties and disturbances and unknown dynamics. First, we investigated the dynamic of QUAV composed of six inter-connected nonlinear subsystems. Then, to increase the convergence speed and control precision of the classical backstepping controller (BC), we design a fractional-order BC (FOBC) that provides further degrees of freedom in the control parameters for every subsystem. Besides, designing control is a challenge as the FOBC requires knowledge of accurate mathematical model and the physical parameters of QUAV system. To address this problem, we propose an adaptive approximator that is a radial basis function neural network (RBFNN) included in FOBC to fix the unknown dynamics problem which results in the new approach NNFOBC. Furthermore, a robust control term is introduced to increase the tracking performance of a reference signal as parametric uncertainties and disturbances occur. We have used Lyapunov's theorem to derive adaptive laws of control parameters. Finally, the outcoming results confirm that the performance of the proposed NNFOBC controller outperforms both the classical BC , FOBC and a neural network-based classical BC controller (NNBC) and under parametric uncertainties and disturbances.  相似文献   

10.
In this paper, we develop two new model reference adaptive control (MRAC) schemes for a class of nonlinear dynamic systems that is robust with respect to an uncertain state (output) dependent nonlinear perturbations and/or external disturbances with unknown bounds. The design is based on a controller parametrization with an adaptive integral action. Two types of adaptive controllers are considered—the state feedback controller with a plant parameter identifier, and the output feedback controller with a linear observer.  相似文献   

11.
This paper proposes an approach to identify the topological structure and unknown parameters for uncertain general complex networks with non-derivative and derivative coupling. By designing effective adaptive controller, the unknown network topological structure and system parameters of uncertain general complex dynamical networks are identified simultaneously in the process of synchronization. Several useful criteria for synchronization are given. Finally, numerical simulations are presented to verify the effectiveness of the theoretical results obtained in this paper.  相似文献   

12.
Previously proposed adaptive fuzzy sliding mode control (AFSMC) and adaptive fuzzy sliding mode observer (AFSMO) methods are mixed and extended for the case of affine systems in which the input gain matrix is state-dependent, non-diagonal and non-positive definite. The proposed Extended AFSMCO (E-AFSMCO) method is then applied for position control of a Stewart Manipulator (SM), whose parameters are strongly state-dependent and complex and not suitable for practical control purposes. A robust observer-based control method which can work with a simplified model of the plant, and at the same time can preserve the stability and performance of the overall complex system is of great need. In this study, the SM dynamic model is simplified by removing the dynamic effects of the legs and the neglected terms are considered as un-modeled dynamics, for which the upper bound of the uncertainty is progressively estimated using the proposed adaptation rules. The final controller is comprised of a fuzzy controller in parallel with a robust switching controller. The second Lyapunov theorem is used to prove the closed-loop asymptotic stability. The proposed E-AFSMCO method is verified numerically and experimentally, depicting the effectiveness of the method for real-time industrial applications.  相似文献   

13.
The issue of adaptive sliding mode controller design via output knowledge is studied for discrete-time Markov jump systems in this paper by means of using singular system scheme. To force the system state onto the sliding motion, an appropriate switching surface depended on the system output is established. Meanwhile, the reachability of the sliding manifold is guaranteed by synthesizing the robust sliding mode controller and adaptive sliding mode controller for the accessible and inaccessible upper bounds of sliding patch, respectively. By using Lyapunov functional technique, sufficient criteria to guarantee the sliding motion to be stochastically admissible are proposed. Then the reachability conditions of the predesigned switching surface are developed. Finally, simulation results are provided to illustrate the effectiveness of the proposed approach.  相似文献   

14.
In this paper, the adaptive sliding mode control issue for switched nonlinear systems with matched and mismatched uncertainties is addressed, where the persistent dwell-time switching rule is introduced to describe the switching of parameters. Besides, considering the case that the upper bound of the matched uncertainty is unknown, the purpose of this paper is to utilize an adaptive control method to estimate its upper bound parameters. To begin with, a linear sliding surface is constructed, and then the reduced-order sliding mode dynamics can be obtained through a reduced-order method. Next, sufficient conditions can be derived based on the Lyapunov stability and the persistent dwell-time switching analysis techniques ensuring that the reduced-order sliding mode dynamics is globally uniformly exponentially stable. Moreover, a switched adaptive sliding mode control law is designed, which can not only ensure the reachability of the sliding surface but also estimate the upper bound parameters of the matched uncertainty. Finally, a numerical example and a circuit model are introduced to verify the effectiveness of the proposed method.  相似文献   

15.
This paper focuses on the problem of chaos control for the permanent magnet synchronous motor with chaotic oscillation, unknown dynamics and time-varying delay by using adaptive sliding mode control based on dynamic surface control. To reveal the mechanism of motor system and facilitate controller design, the dynamic behavior of the system is investigated. Nonlinear items of system model, upper bounds of time delays and their derivatives are taken as unknown in the overall process. A RBF neural network with an adaptive law, which eliminates restrictions on accurate model and parameters, is employed to cope with unknown dynamics. In order to solve issues such as chaotic oscillation, ‘explosion of complexity’ of backstepping, and chattering associated with sliding mode control, a sliding mode controller is developed within the framework of dynamic surface control by the hybrid of adaptive technology and RBF neural network. In addition, an appropriate Lyapunov function is employed to demonstrate the system stability. Finally, the feasibility of the proposed scheme is testified by simulation.  相似文献   

16.
《Journal of The Franklin Institute》2019,356(18):11345-11363
In this paper, the problem of adaptive neural network control design is addressed for a kind of discrete-time nonlinear interconnected systems with unknown dead-zone. The control purpose of this paper is to design an adaptive neural network controller to ensure the systems stability and achieve the desired control performance. The neural networks are utilized to approximate the unknown functions. On the basis of utility functions, the critic signals are considered in the designed control signals. In order to offset the impact of unknown asymmetric dead-zone in the controlled system, the adaptive assistant signal is constructed. Based on the gradient descent rule, the weight tuning laws are obtained. The difference Lyapunov function theory is adopted to prove the studied system stability. The viability of the devised control strategy is further testified via some simulation results.  相似文献   

17.
《Journal of The Franklin Institute》2021,358(18):10029-10051
This paper is concerned with the problem of generating stable limit cycles for a class of nonlinear sandwich systems with the sandwiched dead-zone nonlinearity. In this study, the considered sandwich systems are restricted to the potential problems of non-symmetric input saturation nonlinearity and unknown parameters. In this regard, based on the set stabilization approach and the shape of the desired limit cycle, an adaptive state feedback controller is constructed by using the backstepping technique in such a way that forces the system's output to oscillate with the wanted amplitude and frequency. Besides, to estimate the value of unknown parameters, the adaptive laws are extracted and the problem of the explosion of complexity in the traditional backstepping approach is solved via an effective differentiator. The Lyapunov stability analysis proves that all signals of the closed-loop system keep bounded. Finally, the simulation results of a practical example are provided to demonstrate the effectiveness of the proposed method.  相似文献   

18.
In this paper, a L’ Hopital’s rule-based adaptive dynamic surface control (L-ADSC) scheme is developed for a class of strict-feedback systems with unknown parameters using backstepping technique. The L-ADSC-derived backstepping technique is deployed to remove differentiation of complex virtual controller, thereby efficiently avoiding ”exlosion of complexity”. The L’ Hopital’s Rule is resorted to tackle singularity problem within controller synthesis. As a consequence, the proposed L-ADSC scheme guarantees that all signals of the closed-loop control system are semi-globally uniformly ultimately bounded. Simulation results show remarkable effectiveness.  相似文献   

19.
This paper investigates the adaptive fuzzy control design problem of multi-input and multi-output (MIMO) non-strict feedback nonlinear systems. The considered control systems contain unknown control directions and dead zones. Fuzzy logic systems (FLSs) are utilized to approximate the unknown nonlinear functions, and the state observers are designed to estimate immeasurable states. By constructing a dead zone compensator and introducing a Nussbaum gain function into the backstepping technique, an adaptive fuzzy output feedback control method is developed. The proposed adaptive fuzzy controller is proved to guarantee the semi-globally uniformly ultimately bounded (SGUUB) of the closed-loop system, and can solve the control design problems of unmeasured states, unknown control directions and dead zones. The simulation results are given to demonstrate the effectiveness of the proposed control method.  相似文献   

20.
This paper investigates the problem of decentralized adaptive backstepping control for a class of large-scale stochastic nonlinear time-delay systems with asymmetric saturation actuators and output constraints. Firstly, the Gaussian error function is employed to represent a continuous differentiable asymmetric saturation nonlinearity, and barrier Lyapunov functions are designed to ensure that the output parameters are restricted. Secondly, the appropriate Lyapunov–Krasovskii functional and the property of hyperbolic tangent functions are used to deal with the unknown unmatched time-delay interactions, and the neural networks are employed to approximate the unknown nonlinearities. At last, based on Lyapunov stability theory, a decentralized adaptive neural control method is proposed, and the designed controller decreases the number of learning parameters. It is shown that the designed controller can ensure that all the closed-loop signals are 4-Moment (or 2 Moment) semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges to a small neighborhood of the origin. Two examples are provided to show the effectiveness of the proposed method.  相似文献   

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