Fuzzy Control Of Nonlinear Systems Research Articles

Switched Fuzzy Control for Nonlinear Systems via a Fuzzy-Rule-Dependent Adaptive Event-Triggered Mechanism

Switched Fuzzy Control for Nonlinear Systems via a Fuzzy-Rule-Dependent Adaptive Event-Triggered Mechanism

Fixed Time Adaptive Fuzzy Control of Nonlinear Systems with Time-Varying State Constraints

Fixed Time Adaptive Fuzzy Control of Nonlinear Systems with Time-Varying State Constraints

Finite-time adaptive fuzzy control of nonlinear systems with actuator faults and input saturation

Finite-time adaptive fuzzy control of nonlinear systems with actuator faults and input saturation

Stability analysis of nonlinear fuzzy hybrid control systems subject to saturation and delays via step‐function method

AbstractWithin the framework of the step‐function method, the stability of a nonlinear fuzzy hybrid control system that incorporates both an impulsive controller and a continuous state feedback controller is investigated. Both the two controllers are assumed to be subject to both actuator saturation and time‐varying delays, which has been given scant attention, if any, in the existing studies. A new assumption is introduced in this article, thereby enabling the use of generalized sector conditions to tackle the double saturation, and the conservatism inherent in stability results is remarkably reduced owing to the improved step‐function approach employed. The stability theorem proposed in this article removes constraints imposed on the time delays of both controllers, which can be also applied to wider scopes of systems, encompassing hybrid control systems with both stabilizing and instabilizing impulses, systems with varying impulsive gain, and systems with Zeno behavior. Numerical simulations of stabilization for different systems by delayed saturated hybrid control have been conducted, which demonstrate the validity of the proposed theorems.

Output-Feedback Adaptive Fuzzy Control for Nonlinear Systems Under Time-Varying State Constraints

Output-Feedback Adaptive Fuzzy Control for Nonlinear Systems Under Time-Varying State Constraints

Observer-Based Finite-Time Prescribed Performance Adaptive Fuzzy Control for Nonlinear Systems with Hysteresis Nonlinearity

Observer-Based Finite-Time Prescribed Performance Adaptive Fuzzy Control for Nonlinear Systems with Hysteresis Nonlinearity

Adaptive Fuzzy Control of Nonlinear Systems With Predefined Time and Accuracy

In this article, an adaptive fuzzy predefined time and accuracy tracking control scheme is proposed for strict-feedback nonlinear systems. A sufficient condition that can be used to determine whether the tracking error converges to a predefined region within a predefined time is first derived based on the definition of predefined time stability. Under the proposed stability criterion, an adaptive fuzzy controller is developed by applying the backstepping technique, and the stability time and convergence accuracy of the tracking error can be set previously, which facilitates the engineering application with requirements for convergence time and accuracy. Especially, to reduce chattering and decrease energy consumptions on the premise of ensuring the stability of the system, an inequality is introduced to construct a continuous term which can replace the sign function. Finally, a practical example is presented to illustrate the feasibility of main results.

Adaptive Fuzzy Control of Nonlinear Systems With Function Constraints Based on Time-Varying IBLFs

In this article, an adaptive tracking control approach is developed for a class of strict-feedback nonlinear systems with time-varying full state constraints. As a breakthrough in this system, the special function constraints (whose constraint boundary is relevant to both state variables and time) are considered, which are rarely studied by research work. And there is no doubt that this method increases the complexity of designing this scheme. Furthermore, the time-varying integral barrier Lyapunov functions combining with backstepping technique is introduced to break the limitation of traditional methods as well as achieve the full state constraints. Meanwhile, fuzzy logic systems are selected to approximate unknown nonlinear functions. It is verified that all closed-loop signals are bounded and all states are forced in the time-varying boundness. In addition, the proposed control strategy has a good performance. The effectiveness of the theoretical analysis results is proved via a simulation example.

Robust fuzzy control for non-linear systems with uncertainties: A Takagi- Sugeno model approach

This article studies the problem of robust control design for a class of uncertain nonlinear systems using the Takagi–Sugeno (TS) fuzzy models. The objective of this study is to design state feedback and an observer-based controller such that the closed-loop system is asymptotically stable. For this purpose, sufficient conditions are derived, and the corresponding controllers are designed by solving a set of linear matrix inequalities (LMIs). The effectiveness of the proposed design approach is provided via numerical simulations for a permanent magnet synchronous motor (PMSM).

Adaptive fixed‐time fuzzy control for nonlinear systems with actuator faults

SummaryThis article copes with the adaptive fixed‐time funnel control problem for a class of nonlinear systems with actuator faults. To deal with the difficulties due to actuator faults, which include both lock‐in‐place and loss‐of‐effectiveness models, an adaptive fault‐tolerant control scheme is proposed. By introducing the fuzzy logic system and backstepping technique, an adaptive fixed‐time controller is developed to ensure that the tracking error converges into a pre‐specified performance funnel within a fixed time, which is independent on the initial state, and in the meantime, the controlled closed‐loop system remains semi‐global fixed‐time bounded stable and all the signals are bounded. Simulation results verify the proposed approach.

Iterative Learning Fuzzy Control for Nonlinear Systems with Adaptive Gain and without Resetting Condition

In this paper, we present two ILC schemes. The first one is a PD-type iterative learning control with an initial state algorithm to solve the trajectory tracking problem for nonlinear systems with uncertainties and without satisfying the classical resetting condition. -norm method is used to prove the asymptotic stability of the closed loop system and the simulation results on perturbed nonlinear system have been given. The second approach is a simple P-type iterative learning fuzzy control scheme to solve the trajectory tracking problem for MIMO nonlinear systems. The control design is applicable to deal with a class of nonlinear systems without satisfying the global Lipschitz continuity condition, for which a fuzzy logic term is added to cope with unknown parameters. In addition, the swarm optimization algorithm is used to design the optimum iterative learning fuzzy control (ILFC). Using Lyapunov theory, the asymptotic stability of the closed loop system is guaranteed over the whole finite time. Finally, an illustrative example on two-link manipulator is provided to illustrate the effectiveness of the proposed controller.

Improved sampled data fuzzy controller for nonlinear system: The event‐triggered approach

AbstractThis paper mainly focuses on the stability analysis and sampled data controller synthesis for nonlinear system represented by the Takagi–Sugeno (T‐S) fuzzy model. To avoid network congestion and save control resource, the event‐triggered communication strategy is applied. A novel fuzzy time‐dependent Lyapunov–Krosovskii functional (LKF) is proposed by compromising the fuzzy membership function and the sampling pattern; an improved stability criterion with less conservativeness and the sampled data controller synthesis manner are proposed with the help of the extended reciprocally convex inequality. Besides that, a collaborative algorithm to optimize the corresponding control variables of sampled data control gain and the event‐triggered communication mechanism is established. Finally, the practical applicability and validity as well as the advantage of the proposed method are illustrated in the simulation part.

Adaptive disturbance observer-based event-triggered fuzzy control for nonlinear system

This paper investigates the issue of tracking control of nonlinear system with external disturbance, an event-triggered fuzzy backstepping sliding-mode control strategy is proposed. Firstly, a fuzzy state estimator is designed to estimate the unmeasurable state, and a sliding-mode disturbance observer is presented on the estimation error. In which, consider the conservativeness of the observer parameter, a parameter adaptation law with a double nested structure is proposed to mitigate chattering problems caused by parameter conservativeness. Then an event-triggered mechanism along with backstepping control and sliding-mode strategy is designed to achieve tracking control and save the network resources, and the Zeno phenomenon can be avoided. Furthermore, the backstepping law combines with Lyapunov theory is used to prove that the tracking error can converge to a small bounded set and all the signals of closed-loop plant are bounded. Finally, simulation results are carried out to illustrate the potential of designed approaches.

Observer-based robust fuzzy control of nonlinear networked systems with actuator saturation

In this article, the aim is to study the observer-based robust fuzzy control of nonlinear systems subject to actuator saturation via network communication. Unlike most existing results, system outputs are sufficiently processed by an adaptive event-triggered mechanism in an aperiodic sampling manner. By utilizing Takagi–Sugeno (T–S) fuzzy description, a fuzzy observer is established based on the sampled outputs suffering from network-induced delays. A saturated fuzzy control law is then derived from the estimated states of the observer. Moreover, by using ℒ∞ performance index, the adverse effect of persistent bounded disturbance is substantially attenuated. A novel Lyapunov functional, fully considering the characteristics of aperiodic event-triggered scheme and transmission delays, is investigated to analyze system stability and synthesize the desired controller. In view of the imperfect premise matching, the knowledge of asynchronous membership functions is imported into the derivation of a novel set of sufficient conditions for controller synthesis. Finally, the proposed observer-based control algorithm is verified by an illustrative example and simulation results.

Finite-Time Adaptive Fuzzy Control for Nonlinear Systems with Unknown Backlash-Like Hysteresis

This study considers the tracking control problem of the nonstrict-feedback nonlinear system with unknown backlash-like hysteresis, and a finite-time adaptive fuzzy control scheme is developed to address this problem. More precisely, the fuzzy systems are employed to approximate the unknown nonlinearities, and the design difficulties caused by the nonlower triangular structure are also overcome by using the property of fuzzy systems. Besides, the effect of unknown hysteresis input is compensated by approximating an intermediate variable. With the aid of finite-time stability theory, the proposed control algorithm could guarantee that the tracking error converges to a smaller region. Finally, a simulation example is provided to further verify the above theoretical results.

Indirect Fuzzy Control of Nonlinear Systems With Unknown Input and State Hysteresis Using an Alternative Adaptive Inverse

It is interesting to study the problem of adaptive fuzzy control for uncertain nonlinear systems with input and state hysteresis. However, since the hysteresis behavior, especially the state hysteresis, present at the sensors is complicated, mainly in view of its multivalues and rate-dependent features, it is a challenging task to develop the backstepping-based control design. So far, there is still no available result in addressing the problem. In this article, we will pursue this task. A novel indirect fuzzy control scheme is proposed with an alternative adaptive hysteresis inverse, which is used to cancel the unknown input hysteresis. To tackle the effects of state hysteresis, a new dynamic compensation is developed to adaptively accommodate the uncertain dynamics involved in the sensors. It is proved that in addition to system stability, the proposed scheme enables the tracking error to approach a prescribed interval asymptotically. Illustrative examples are used to verify the method developed.

Type-2T-S Fuzzy Control for Nonlinear Systems with Niche Equality Index Function

In this paper, a novel Type–2T-S fuzzy control scheme is proposed for a class of nonlinear systems with uncertain parameters. In this scheme, the fuzzy controller with biological characteristics is firstly constructed by taking the niche equality index function as the consequent of type-2 fuzzy rules, which combines the adaptive characteristics of biological evolution with type-2 fuzzy control. Then, the gradient descent method is used to optimize the parameters of the consequent and adaptive laws are obtained. Furthermore, the stability and convergence of the system are analyzed. Finally, the simulation results from an example show the effectiveness of the proposed method. Obviously, the proposed method is better than the Type-1. This method can reflect the organism being adaptive, self-development ability, the degree of development, and the utilization of biological environment (i.e., a physical background of fuzzy control is given).

An experimental study on inverse adaptive neural fuzzy control for nonlinear systems

In this paper, a type-2 fuzzy neural network is used to adaptive inverse control of a class of nonlinear systems. The proposed method has proper performance for real-time control of nonlinear and time-invariant systems and has a quick response to following changes. In this method, the high approxim ation ability of type 2 fuzzy systems is used to reverse system modeling and this inverse model is used immediately to control the system. In order to measure the efficiency of the proposed method, the control of dynamic, highly nonlinear and time varying water both temperature system with certain and uncertain water pressure is experimentally considered. The results show the accuracy of the proposed method.

Adaptive finite-time fuzzy control for nonlinear systems with input quantization and unknown time delays

We investigate the finite-time adaptive control issue for a class of nonlinear systems with quantized input signals and unknown time delays in this paper. In the control design process, the nonlinear terms are approximated by the fuzzy logic systems. A hysteretic quantizer is introduced to avoid chattering in the quantized signals, and the fuzzy state observer is constructed to estimate the unmeasurable states of the system. In order to propose the finite-time control strategy, firstly, a semi-global practical finite-time stability criterion is given. On this basis, a fuzzy adaptive controller is designed by combining the dynamic surface control technology with backstepping method. The controller can not only ensure that the observer and tracking error converge to a small neighborhood of the origin in a finite time, but also keep all the signals in the closed-loop system bounded. Finally, the effectiveness and feasibility of the control method are verified by two simulation examples.

Reliable Dissipative Interval Type-2 Fuzzy Control for Nonlinear Systems with Stochastic Incomplete Communication Route and Actuator Failure

This paper investigates the dissipative interval type-2 (IT2) fuzzy control issue for a group of uncertain nonlinear systems subject to randomly occurring sensor saturation and quantization. By utilizing the both lower and upper membership functions with relevant tradeoff moduli, uncertainties, which exist in the considered systems, can be captured effectively. During the design of the IT2 fuzzy controller, we take the actuator failure into consideration. Considering the randomicities for the occurrences of sensor saturation and quantization, two separate Bernoulli stochastic distributed courses are utilized to construct the corresponding complicated transmission route. In term of Lyapunov stability theory, sufficient criteria to design dissipative IT2 fuzzy controller are presented to assure the stochastic stability with strict dissipativity of resulting closed-loop systems. Finally, illustrative example verifies the effectiveness and merits of the control strategy proposed in this paper.