This paper addresses co-design control strategies for continuous-time Markov jump systems where subsets of transition rate matrices are fixed a priori, challenging conventional co-design methodologies. A synchronously mode-dependent parametric framework is developed to address partial transition rate optimization alongside output feedback controller synthesis. Novel criterion is derived to guarantee mean-square stability by reconstructing adjustable switching parameters while preserving fixed system transitions. Stability analysis and controller design are unified through hybrid control principles. A numerical case studies validate the proposed approach, demonstrating enhanced feasibility compared to existing methods.

This paper tackles the problem of event-triggered unified performance state estimation in neural networks with time-varying delays. A novel event-triggered methodology is introduced, aiming to balance the performance of the state estimator and the network's communication bandwidth. The proposed method leverages a triggered-parameter-dependent integral inequality with matrices that consider the event-triggered mechanism, capturing the interplay between the time-varying delay and system states. This innovative approach guarantees the asymptotic stability of the estimation error system, thereby meeting the$H$∞ performance criterion. The efficacy of the proposed condition is demonstrated by a numerical example.

This brief studies the stability problem of recurrent neural networks with time-varying delay. Based on one tunable parameter$\alpha $, a flexible terminal interpolation method is proposed to change the interval with fixed terminals as$2^{k+1}-3$ones with flexible terminals. Associated with the flexible subintervals, a novel Lyapunov–Krasovskii functional with more delay information is constructed. In order to estimate the Lyapunov–Krasovskii functional, a quadratic reciprocally convex inequality is proposed, which covers some existing ones as its special cases. Based on these ingredients, a new stability criterion is derived in the form of linear matrix inequalities. A comprehensive comparison of results is given to illustrate the newly proposed stability criterion.

Six-wheel skid-steer vehicles are widely used in the real world because of their special steering structure and load-carrying capacity. In this article, we aim to study the event-triggered fuzzy yaw control of six-wheel skid-steer vehicles. The nonlinear lateral dynamics of a six-wheel skid-steer vehicle is modeled using the Takagi-Sugeno (T-S) fuzzy system with irregular fuzzy rules, in which more membership function information can be captured. Considering the high-frequency transmission mode used in vehicle motion systems due to high-precision control requirements, an event-triggered scheme with a dynamic threshold is proposed to ensure control accuracy while reducing the data transmission. Then, a flexible yaw control scheme is constructed in which two adjustable weighting factors are introduced to improve the flexibility of the fuzzy yaw controller. By combining the flexible yaw control scheme and dynamic event-triggered scheme, we design a set of event-triggered fuzzy yaw controllers that satisfy the finite-time stability of lateral motion tracking for six-wheel skid-steer vehicles. The advantages and effectiveness of the proposed method are verified by conducting the TruckSim-MATLAB joint simulation.

This brief addresses the stability analysis problem of a class of delayed recurrent neural networks (DRNNs). In previously published studies, the slope information of activation function (SIAF) is just reflected in three slope information matrices, i.e., the upper and lower boundary matrices and the maximum norm matrix. In practice, there are$2^{n}$possible combination cases on the slope information matrices. To exploit more information about SIAF, first, an activation function separation method is proposed to derive$n$slope-information-based uncertainties (SIBUs) containing SIAF; second, a quadratic matrix convex combination approach is proposed to dispose$n$SIBUs using$2^{n}$combination slope information matrices. Third, a stability criterion with less conservatism is established based on the proposed approach. Finally, two simulation examples are used to testify the validity of theoretical results.

This article investigates the finite-time extended dissipative filtering for singular T–S fuzzy Markov jump systems with time-varying transition probabilities (TPs). The time-varying TPs are considered to reside in a polytope. By resorting to a generalized performance index, the$H_{\infty }$,$L_{2}-L_{\infty }$, passive, and dissipative performance can be solved in a unified framework. Combining the free-weighting method and the proposed recursive method, a sufficient condition on singular stochastic extended dissipative finite-time boundedness (SSEDFTB) for a fuzzy filtering error system is obtained. By proposing a decoupling principle called double variables-based decoupling principle (DVDP) and a variable substitution principle (VSP), a novel condition on the existence of the fuzzy filter is presented in terms of linear matrix inequalities (LMIs). Compared with the existing works, the assumption on state variables and the constraints of slack matrices are overcome, which leads to more practical and less conservative results. A practical example is provided to demonstrate the effectiveness of the design methods.

In this article, stability analysis and controller synthesis problems for Takagi–Sugeno (T–S) fuzzy systems with time-varying delay are studied. A generalized parameter-dependent reciprocally convex inequality (GPDRCI) is presented to handle the derivative of triple integral terms, which is more general than some existing ones. By choosing suitable flexible polynomials with tunable parameters, novel flexible polynomial-based functions (FPFs) are proposed in delay-product types, which overcome the incompletely slack matrices, higher-order time delay and insufficient parameters in the existing functions. Benefitting from completely slack matrices and lower-order time delay, coupling relationship among system states and time delay is fully linked. Based on the GPDRCI and FPFs, a stability condition is derived for T–S fuzzy systems. Based on the stability criterion, considering both the time-varying delay and its bounds, a generalized memory controller is designed for T–S fuzzy systems, which covers the memoryless and traditional memory ones as its special cases. In addition, the constraints on introduced slack matrices in some existing works are avoided with the help of a matrix inequality decoupling technique. These provide extra free dimensions in the solution space. Some examples are employed to illustrate the effectiveness of the proposed methods.

This article studies the problem of the stability for Markovian neural networks (MNNs) with time delay. The transition rate is considered to be generally hybrid, which treats those existing ones as its special cases. The introduced generally hybrid transition rates (GHTRs) make these systems more general and practical. Apropos of the GHTRs, a double-boundary approach rather than the traditional estimation method is introduced to make full use of the error information in GHTRs. In order to fully capture system information, a parameter-type-delay-dependent-matrix (PTDDM) approach is proposed, in which the PTDDM approach removes some zero components on slack matrices in previous works. Thus, the PTDDM approach can fully link the relationship among time delay and state-related vectors. Based on these ingredients, a novel stochastic stability condition is proposed for MNNs with GHTRs. A numerical example is illustrated to demonstrate the effectiveness of the proposed approaches.

This article is concerned with the asynchronous reliable extended dissipative filtering problem for a class of continuous-time T–S fuzzy Markov jump systems. The modes of the encountered sensor failures and the designed filter are considered to be asynchronous with the original systems, which can be described by two mutually independent hidden Markov processes. By proposing double variables-based decoupling principle and variable substitution principle, a new condition is presented to guarantee the filtering error system to be stochastically stable and extended dissipative. Compared with the existing works, the proposed method does not impose constraints on Lyapunov variables and slack variables, and some unnecessary constraints on the system structure are removed. These directly lead to less conservative and more general results. An example is provided to illustrate the effectiveness of the proposed design method.

This brief studies the problem of extended dissipativity analysis for the Markovian jump neural networks (MJNNs) with time-varying delay. A double-integral-based delay-product-type (DIDPT) Lyapunov functional is first constructed in this brief, which makes full use of the information of time delay. Moreover, some unnecessary constraints on the system structure are removed, which leads to more general results. A numerical example is employed to illustrate the advantages of the proposed method.

Abstract Sharp features are important characteristics in surface modelling. However, it is still a significantly difficult task to create complex sharp features for Non‐Uniform Rational B‐Splines compatible subdivision surfaces. Current non‐uniform subdivision methods produce sharp features generally by setting zero knot intervals, and these sharp features may have unpleasant visual effects. In this paper, we construct a non‐uniform subdivision scheme to create complex sharp features by extending the eigen‐polyhedron technique. The new scheme allows arbitrarily specifying sharp edges in the initial mesh and generates non‐uniform cubic B‐spline curves to represent the sharp features. Experimental results demonstrate that the present method can generate visually more pleasant sharp features than other existing approaches.