Chengwei Wu

Control of Nonlinear Networked Systems With Packet Dropouts: Interval Type-2 Fuzzy Model-Based Approach

In this paper, the problem of fuzzy control for nonlinear networked control systems with packet dropouts and parameter uncertainties is studied based on the interval type-2 fuzzy-model-based approach. In the control design, the intermittent data loss existing in the closed-loop system is taken into account. The parameter uncertainties can be represented and captured effectively via the membership functions described by lower and upper membership functions and relative weighting functions. A novel fuzzy state-feedback controller is designed to guarantee the resulting closed-loop system to be stochastically stable with an optimal performance. Furthermore, to make the controller design more flexible, the designed controller does not need to share membership functions and amount of fuzzy rules with the model. Some simulation results are provided to demonstrate the effectiveness of the proposed results.

Filtering of Interval Type-2 Fuzzy Systems With Intermittent Measurements

In this paper, the problem of fuzzy filter design is investigated for a class of nonlinear networked systems on the basis of the interval type-2 (IT2) fuzzy set theory. In the design process, two vital factors, intermittent data packet dropouts and quantization, are taken into consideration. The parameter uncertainties are handled effectively by the IT2 membership functions determined by lower and upper membership functions and relative weighting functions. A novel fuzzy filter is designed to guarantee the error system to be stochastically stable with H∞ performance. Moreover, the filter does not need to share the same membership functions and number of fuzzy rules as those of the plant. Finally, illustrative examples are provided to illustrate the effectiveness of the method proposed in this paper.

Observer-Based Fuzzy Control for Nonlinear Networked Systems Under Unmeasurable Premise Variables

The problem of fuzzy observer-based controller design is investigated for nonlinear networked control systems subject to imperfect communication links and parameter uncertainties. The nonlinear networked control systems with parameter uncertainties are modeled through an interval type-2 (IT2) Takagi-Sugeno (T-S) model, in which the uncertainties are handled via lower and upper membership functions. The measurement loss occurs randomly, both in the sensor-to-observer and the controller-to-actuator communication links. Specially, a novel data compensation strategy is adopted in the controller-to-actuator channel. The observer is designed under the unmeasurable premise variables case, and then, the controller is designed with the estimated states. Moreover, the conditions for the existence of the controller can ensure that the resulting closed-loop system is stochastically stable with the predefined disturbance attenuation performance. Two examples are provided to illustrate the effectiveness of the proposed method.

Adaptive Fuzzy Control for Nonstrict-Feedback Systems With Input Saturation and Output Constraint

This paper presents an adaptive fuzzy control approach for a category of uncertain nonstrict-feedback systems with input saturation and output constraint. A variable separation approach is introduced to overcome the difficulty arising from the nonstrict-feedback structure. The problem of input saturation is solved by introducing an auxiliary design system, and output constraint is handled by utilizing a barrier Lyapunov function. Combing fuzzy logic system with the adaptive backstepping technique, the semi-global boundedness of all variables in the closed-loop systems is guaranteed, and the tracking error is driven to the origin with a small neighborhood. The stability of the closed-loop systems is proved, and the simulation results reveal the effectiveness of the proposed approach.

Observer-based adaptive fuzzy tracking control of nonlinear systems with time delay and input saturation

This paper studies the problem of adaptive output tracking control for a class of nonlinear systems subject to unknown time delay and input saturation. To address the delay and the input saturation, some reasonable assumptions and an auxiliary system are introduced. The fuzzy logic approach is employed to approximate the unknown functions in the system. An observer is designed to estimate the unavailable system states. The dynamic surface control (DSC) technique is utilized to avoid the problem of explosion of complexity. By combining the backstepping and DSC methods, a bounded control input is designed to ensure that all signals of the closed-loop system are bounded and the tracking error can fluctuate around the origin within a small neighborhood. Finally, two examples are presented to demonstrate the effectiveness and potential of the proposed new design techniques.

Robust control of uncertain semi-Markovian jump systems using sliding mode control method

The problem of robust adaptive sliding mode control for semi-Markovian jump systems with actuator faults is investigated in this paper. The uncertainties considered in this paper satisfy norm-bounded form, and bounds of nonlinearity, actuator faults and external disturbance are unknown. Then, the influences of the actuator faults, unknown nonlinearity and disturbance can be effectively attenuated via a novel adaptive sliding mode controller. The reachability of sliding mode surface can be guaranteed by the adaptive sliding mode controller. Using Lyapunov stability theory, sufficient conditions are derived to guarantee the stochastic stability of the sliding mode dynamics. Finally, a numerical example is exploited to demonstrate the effectiveness of the proposed method.

Adaptive fuzzy tracking control for a class of pure-feedback nonlinear systems with time-varying delay and unknown dead zone

Abstract This paper investigates the problem of adaptive fuzzy tracking control for a category of pure-feedback nonlinear systems with time-varying delay and unknown dead zone. Fuzzy logic systems are used to identify unknown functions existing in systems. Mean-value theorem is introduced to overcome the difficulty arising from the pure-feedback structure in designing controller. Based on the information of dead-zone slopes, dead zone is handled. Only one adaptive parameter needs updating online by considering the norm of membership function vectors rather than all sub-vectors. Moreover, utilizing the adaptive backstepping technique, a novel adaptive fuzzy tracking control scheme is developed to guarantee all signals of the closed-loop systems are semi-globally uniformly ultimately bounded, and the tracking error can be regulated to the origin with a small neighborhood. The stability of the closed-loop system is proved and simulation results are given to demonstrate the effectiveness of the proposed control approach.

Robust finite-time state estimation of uncertain neural networks with Markovian jump parameters

In this paper, the robust finite-time state estimation problem of the uncertain Markovian jump neural networks with partly unknown transition probabilities is investigated. In the neural networks, there are a set of modes, which are determined by Markov chain. First, we design a state observer to estimate the neuron states. Second, based on Lyapunov stability theory, a robust stability sufficient condition is derived such that the uncertain Markovian jump neural networks with partly unknown transition probabilities are robust finite-time stable. Then, the robust stability condition is expressed in terms of linear matrix inequalities (LMIs), which can be easily solved by standard software. Finally, a numerical example is given to demonstrate the effectiveness of the proposed new design techniques.

Fuzzy guaranteed cost output tracking control for fuzzy discrete‐time systems with different premise variables

This article investigates the problem of output tracking control for a class of discrete-time interval type-2 (IT2) fuzzy systems subject to mismatched premise variables. Based on the IT2 Takagi–Sugeno (T–S) fuzzy model, the criterion to design the desired controller is obtained, which guarantees the closed-loop system to be asymptotically stable and satisfies the predefined cost function. Moreover, the controller to be designed does not need to share the same premise variables of the system, which enhances the flexibility of controller design and reduces the conservativeness. Finally, two examples are provided to demonstrate the effectiveness of the method proposed in this article.

New dissipativity condition of stochastic fuzzy neural networks with discrete and distributed time-varying delays

This paper deals with the dissipativity problem for interval type-2 (IT2) stochastic fuzzy neural networks subject to discrete and distributed time-varying delays. Firstly, a new type of IT2 stochastic fuzzy neural network with parameter uncertainties is proposed. The parameter uncertainties can be efficiently tackled by lower and upper membership functions and relative weighting functions. Secondly, according to Ito differential formula and stochastic analysis scheme, a new dissipativity condition is obtained. In the design process, the dissipativity condition can be transformed to convex optimization problem. Finally, a numerical example is proposed to reveal the feasibility of the proposed approach.