This study addresses the maneuvering control problem of a wheeled mobile robot (WMR) operating in dynamic and uncertain environments, where smooth error convergence and robustness against external disturbances are critical challenges. An enhanced Lyapunov-based model predictive control (ELMPC) method is developed, in which an improved tunnel performance function is incorporated into an auxiliary controller to impose stricter contraction constraints on the WMR system, enabling smoother and faster error convergence. In addition, an improved tunnel performance constraint (ITPC) scheme is introduced to eliminate the initial-condition dependence of conventional prescribed performance control. By employing a performance transition function, the ITPC ensures that performance bounds are satisfied under arbitrary initial errors while avoiding excessive transient responses. To compensate for unknown and time-varying disturbances, a neural network–based disturbance predictor is integrated into the control architecture. The predictor estimates disturbances online and transforms tracking errors into prediction errors, thereby accelerating convergence and enhancing robustness. The proposed control strategy is implemented within a Lyapunov-based model predictive control framework with explicit consideration of actuator constraints, where contraction constraints guarantee closed-loop stability and recursive feasibility. Experimental results demonstrate improved tracking performance, robustness, and constraint satisfaction under complex operating conditions.

The multiple hybrid strategies are developed in this article to investigate the$H_\infty$filtering problem of fuzzy impulse switched systems (ISSs) with multipath quantizations and packet dropouts. Every subsystem of the fuzzy ISSs is represented by the Takagi–Sugeno fuzzy model. “Multipath quantizations and packet dropouts” mean that both quantizations and packet dropouts exist in the measurable outputs and performance outputs simultaneously. The static quantization strategy is adopted. The packet dropouts are modeled as two random sequences, which are mutually independent and obey the Bernoulli distribution. The multiple hybrid strategies have the following two aspects. First, the filter state update laws are designed besides designing the switching filters for every subsystem, which are specially designed for the impulsive behaviors existing in the ISSs. Second, the mode dependent and mode independent hybrid filters are designed since the filters may not always access the mode switching information of ISSs owing to the quantizations and packet dropouts. Combining the average dwell-time approach and multiple Lyapunov functions, sufficient conditions based on linear matrix inequalities are derived to design the multiple hybrid$H_\infty$filters. Based on the designed filters, the filtering error systems are exponentially mean-square stable and achieve a weighted$H_\infty$performance index. Lastly, a practical example is given.

This work researches the resilent mixed$H_{\infty }$and energy-to-peak filter design problem of switched Takagi–Sugeno (T–S) fuzzy systems with asynchronous switching and multiple state impulsive jumps. The novelties include three points. First, a novel mixed$H_{\infty }$and energy-to-peak performance index is proposed, which covers the$H_{\infty }$performance index and energy-to-peak performance index as special cases. Second, in addition to designing the switching filters, the filter state jump rules are constructed at filter switching instants. Finally, both system states and filter states jump in a asynchronous manner. The switching law is devised through the mode-dependent average dwell time (MDADT) approach. A new type of Lyapunov-like functionals is constructed, which will increase when the subsystem is running with its mismatched filter and jump while the subsystem or the filter is switching. Then, new conditions are deduced to ensure the filtering error systems with multiple state impulsive jumps to be asymptotically stable with a mixed$H_{\infty }$and energy-to-peak performance level. Filter design conditions expressing as linear matrix inequality (LMI) are obtained. Finally, the effectiveness of the derived results is illustrated by two examples.

This article investigates the asynchronous nonfragile mixed$H_\infty$and$L_{2}-L_\infty$dynamical output feedback (DOF) control problem for switched Takagi–Sugeno (T–S) fuzzy systems with multiple state impulsive jumps. The novelties lie in the following three aspects. First, the novel mixed$H_\infty$and$L_{2}-L_\infty$performance index is adopted, which can include the$H_\infty$performance and$L_{2}-L_\infty$performance indices as special cases. Second, besides the switching DOF control law for every subsystem, an additional controller state jump rule is employed at controller switching instant. Third, the “multiple state impulsive jumps” means that not only the system states, but also the controller states will jump, and these two different types of switching perform asynchronously. The average dwell time approach is used to design the switching law. By employing a new type of Lyapunov-like functions permitting to increase during the asynchronous period and jump at system switching and controller switching instants, new criteria are established to guarantee the asymptotical stability with a mixed$H_\infty$and$L_{2}-L_\infty$performance index of the switched T–S fuzzy systems with multiple state impulsive jumps and asynchronous switching. Then, controller design conditions are obtained in the form of linear matrix inequalities. Finally, two examples are provided to illustrate the effectiveness of the derived results.

The quantized guaranteed cost static output feedback control problem is investigated for a class of discrete-time nonlinear networked control systems in this article. In this article, the Takagi–Sugeno fuzzy model is put to use for the representation of considered nonlinear networked control systems, where local nonlinear models instead of local linear models are used in the Takagi–Sugeno fuzzy model. Two different dynamic quantizers are applied to quantize the control input and measurement output, respectively. Different from some previous work, a novel guaranteed cost performance function including the quantized control input is used in this article. Through using the$S$-procedure and introducing some auxiliary scalars, sufficient conditions for the design of guaranteed cost static output feedback controller and dynamic quantizers are obtained in the form of linear matrix inequalities. Finally, the applicability of the proposed method is illustrated through the application in nonlinear mass-spring-damper mechanical system.

This article investigates the nonfragile guaranteed cost (GC) control problem of discrete-time impulsive switched Takagi–Sugeno (T–S) fuzzy systems with input quantization and asynchronous switching. The model-dependent dynamic quantizers are applied to obtain the quantized input signal. To deeply study the GC performance analysis and GC control problems in the presence of quantization, asynchronous switching, and impulses, a novel piecewise cost function containing the quantized input instead of normal input is applied in this article. By using the mode-dependent average dwell time approach and introducing a class of Lyapunov-like functions allowing to increase during the asynchronous period, new sufficient conditions are established to guarantee the asymptotical stability with the GC performance index for the impulsive switched T–S fuzzy systems with quantized control input and asynchronous switching. Then, new design conditions about the nonfragile GC controllers and dynamic quantizers of impulsive switched T–S fuzzy systems are obtained in the form of linear matrix inequalities. Finally, a numerical example and a practical example are provided.

This article investigates stochastic stability of positive singular Markovian jump systems (PSMJSs) with mode-dependent derivative-term coefficient. Different from the existing results, the condition on stochastic stability of PSMJSs associated with state jumps behavior at switching instants is given by means of linear co-positive Lyapunuov function. On the basis of it, the conditions on stochastic stability under the cases of partially known transition rates and uncertain transition rates have been presented, respectively. All obtained conditions can be solved by linear programming (LP) technique. Finally, an example is introduced to illustrate the effectiveness and the merits of the results proposed in this article.

This paper is devoted to the H∞ control design of positive discrete-time systems with multiple delays. Novel bounded real lemma is presented first via linear matrix inequality technique, which reveals that H∞ norms of a discrete-time positive system with time delays both in dynamic and output equations are identical to that of the corresponding delay-free system. Necessary and sufficient conditions for positivity preserving H∞ stabilization via a dynamic output feedback control are established in the forms of matrix equalities, that guaranteeing the closed-loop system not only to be asymptotically stable and positive, but also to have a desired H∞ performance. The proposed results are extended to interval uncertain positive systems with time delay. Finally, an example is given to illustrate the effectiveness of the obtained design scheme.

This paper proposes a new approach, namely, the delay partitioning approach, to solving the problems of stability analysis and stabilization for continuous time-delay Takagi-Sugeno fuzzy systems. Based on the idea of delay fractioning, a new method is proposed for the delay-dependent stability analysis of fuzzy time-delay systems. Due to the instrumental idea of delay partitioning, the proposed stability condition is much less conservative than most of the existing results. The conservatism reduction becomes more obvious with the partitioning getting thinner. Based on this, the problem of stabilization via the so-called parallel distributed compensation scheme is also solved. Both the stability and stabilization results are further extended to time-delay fuzzy systems with time-varying parameter uncertainties. All the results are formulated in the form of linear matrix inequalities (LMIs), which can be readily solved via standard numerical software. The advantage of the results proposed in this paper lies in their reduced conservatism, as shown via detailed illustrative examples. The idea of delay partitioning is well demonstrated to be efficient for conservatism reduction and could be extended to solving other problems related to fuzzy delay systems.