Explore the vast range of titles available.

This paper presents a three‐layer control architecture designed to enhance vehicle lateral stability under uncertain and varying road conditions. The system utilizes output‐feedback‐based finite‐time controllers to track the desired yaw rate and longitudinal slip in the upper and lower layers, respectively. The middle layer incorporates a finite‐time robust dynamic control allocation to distribute longitudinal slips among the tires. This approach effectively handles uncertainties and changing road conditions without the need for direct estimation of unmeasurable variables such as tire‐road friction and sideslip angle. The proposed output feedback control law consists of a stabilizer component to ensure finite‐time stability, a compensator to eliminate the unknown function in the upper and lower layers, and an auxiliary tracking term. Key advantages of the proposed framework include: no requirement for additional sensors, finite‐time convergence, reduced computational complexity compared to optimization‐based methods, and the ability to perform finite‐time stability analysis for the integrated closed‐loop system. The system performance is evaluated using a validated 10‐degree‐of‐freedom vehicle dynamics model and CarSim simulations during a double‐lane change maneuver. Simulation results demonstrate the superiority of the proposed control structure over sliding mode control, offering improved tracking accuracy and robust performance under varying road conditions.

In this paper, we present a finite-time synchronization (FTS) for quantized Markovian-jump time-varying delayed neural networks (QMJTDNNs) via event-triggered control. The QMJTDNNs take into account the effects of quantization on the system dynamics and utilize a combination of FTS and event-triggered communication to mitigate the effects of communication delays, quantization error, and efficient synchronization. We analyze the FTS and convergence properties of the proposed method and provide simulation results to demonstrate its effectiveness in synchronizing a network of QMJTDNNs. We introduce a new method to achieve the FTS of a system that has input constraints. The method involves the development of the Lyapunov–Krasovskii functional approach (LKF), novel integral inequality techniques, and some sufficient conditions, all of which are expressed as linear matrix inequalities (LMIs). Furthermore, the study presents the design of an event-triggered controller gain for a larger sampling interval. The effectiveness of the proposed method is demonstrated through numerical examples.

This paper addresses the issue of finite-time stability (FTS) and finite-time contractive stability (FTCS) of nonlinear systems involving state-dependent delayed impulsive perturbation. Several sufficient conditions are obtained by using theories of impulsive control and Lyapunov stability. The relation between impulsive perturbation and state-dependent delay is established to achieve FTS and FTCS. For time-varying nonlinear system and nonlinear system with fixed parameters, we derive some sufficient conditions based on the main thought of this paper, respectively. Finally, three numerical examples are provided to illustrate the effectiveness and validity of achieved results.

In this study, we consider the finite time stability (FTS) and finite time contractive stability (FTCS) of the stochastic functional systems. For the framework of Lyapunov-Ruzumikhin method, the sufficient conditions of FTS and FTCS are given first. Then the theory is applied to FTS and FTCS of linear time-varying stochastic functional systems. Finally, experiments on the numerical examples have demonstrated the superiority of the proposed method.

In this article, the finite‐time stochastic stability of fractional‐order singular systems with time delay and white noise is investigated. First the existence and uniqueness of solution for the considered system is derived using the basic fractional calculus theory. Then based on the Gronwall's approach and stochastic analysis technique, the sufficient condition for the finite‐time stability criterion is developed. Finally, a numerical example is presented to verify the obtained theory. © 2016 Wiley Periodicals, Inc. Complexity 21: 370–379, 2016

This article deals with the fractional‐order modeling of a complex four‐dimensional energy supply‐demand system (FOESDS). First, the fractional calculus techniques are adopted to describe the dynamics of the energy supply‐demand system. Then the complex behavior of the proposed fractional‐order FOESDS is studied using numerical simulations. It is shown that the FOESDS can exhibit stable, chaotic, and unstable states. When it exhibits chaos, the FOESDS's strange attractors are plotted to validate the chaotic behavior of the system. Moreover, we calculate the maximal Lyapunov exponents of the system to confirm the existence of chaos. Accordingly, to stabilize the system, a finite‐time active fractional‐order controller is proposed. The effects of model uncertainties and external disturbances are also taken into account. An estimation of the stabilization time is given. Based on the latest version of the fractional Lyapunov stability theory, the finite‐time stability and robustness of the proposed method are proved. Finally, two illustrative examples are provided to illustrate the usefulness and applicability of the proposed control scheme. © 2014 Wiley Periodicals, Inc. Complexity 20: 74–86, 2015

This work investigates the finite-time stability (FTS) issue for a class of inertial neural networks (INNs) with mixed-state time-varying delays, proposing a novel analytical approach. Firstly, we establish a novel FTS lemma, which is entirely different from the existing FTS theorems, and extend the current research results. Secondly, an improved discontinuous reliable control mechanism is developed, which is more valid and widens the application scope compared to previous results. Then, by using a novel non-reduced order approach (NROA) and the Lyapunov functional theory, novel sufficient criteria are established using FTS theorems to estimate the settling time with respect to a finite-time stabilization of INNs. Finally, the simulation results are given to validate the usefulness of the theoretical results.

This article investigates the finite‐time stability, stabilization, and boundedness problems for switched nonlinear systems with time‐delay. Unlike the existing average dwell‐time technique based on time‐dependent switching strategy, largest region function strategy, that is, state‐dependent switching control strategy is adopted to design the switching signal, which does not require the switching instants to be given in advance. Some sufficient conditions which guarantee finite‐time stable, stabilization, and boundedness of switched nonlinear systems with time‐delay are presented in terms of linear matrix inequalities. Detail proofs are given using multiple Lyapunov‐like functions. A numerical example is given to illustrate the effectiveness of the proposed methods. © 2014 Wiley Periodicals, Inc. Complexity 21: 267–275, 2015

The second-order sliding mode control generates important properties for closed-loop systems, such as robustness, disturbance rejection and finite-time convergence. In this study, it is shown that the adding a power technique plus the nested saturation method will bring in a new second-order sliding mode control scheme for non-linear systems with relative degree two. Based on this, a second-order sliding mode controller is constructed by imposing a natural assumption on the sliding mode dynamics, that is, the uncertainty of the sliding mode dynamics can be bounded by a known function instead of a constant. Under the proposed sliding mode controller, it is proved that the closed-loop system is not only globally convergent, but also locally finite-time stable, which implies the global finite-time stability. Finally, the effectiveness of the proposed method is verified by a numerical example.

The aim of this paper is to study an adaptive neural finite-time resilient dynamic surface control (DSC) strategy for a category of nonlinear fractional-order large-scale systems (FOLSSs). First, a novelty fractional-order Nussbaum function and a coordinate transformation method are formulated to overcome the compound unknown control coefficients induced by the unknown severe faults and false data injection attacks. Then, an enhanced fractional-order DSC technology is employed, which can tactfully surmount the deficiency of explosive calculations exposed in the backstepping framework. Furthermore, the radial basis function neural network is applied to address the unknown items related to the nonlinear FOLSSs. Based on the fractional Lyapunov stability criterion, a decentralized finite-time control approach is developed, which can ensure that all states of the closed-loop system are bounded and that the stabilization errors of each subsystem tend toward a small area in finite time. At last, two simulation examples are given to confirm the put-forward control algorithm’s effectiveness.