1,256 publications from this institution
In this paper, novel stability conditions for Takagi-Sugeno (T-S) fuzzy systems are presented. The so-called nonquadratic membership-dependent Lyapunov function is first proposed, which is formulated in a higher order form of both the system states and the normalized membership functions than existing techniques in the literature. Then, new membership-dependent stability conditions are developed by the new Lyapunov function approach. It is shown that the conservativeness of the obtained criteria can be further reduced as the degree of the Lyapunov function increases. Two numerical examples are given to demonstrate the effectiveness and less conservativeness of the obtained theoretical results.
The design of vibration control systems for the seismic protection of closely adjacent buildings is a complex and challenging problem. In this paper, we consider distributed multi-actuation schemes that combine interbuilding linking elements and interstory actuation devices. Using an advanced static output-feedback H ∞ approach, active and passive vibration control systems are designed for a multi-story two-building structure equipped with a selected set of linked and unlinked actuation schemes. To validate the effectiveness of the obtained controllers, the corresponding frequency responses are investigated and a proper set of numerical simulations is conducted using the full scale North–South El Centro 1940 seismic record as ground acceleration disturbance. The observed results indicate that using combined interstory-interbuilding multi-actuation schemes is an effective means of mitigating the vibrational response of the individual buildings and, simultaneously, reducing the risk of interbuilding pounding. These results also point out that passive control systems with high-performance characteristics can be designed using damping elements.
No abstract is provided for this article.
This paper presents a convex optimisation method for observer-based control design of LPV neutral systems. Utilising the polynomials parameter-dependent quadratic functions and a suitable change of variables, the required sufficient conditions with high precision for the design of a desired observer-based control are established in terms of delay-dependent parameter-independent linear matrix inequalities. An observer-based controller guaranteeing asymptotic stability of the closed-loop system and satisfying a prescribed level of performance to the LPV neutral system with constant delay parameters is developed. A Lyapunov-Krasovskii method underlies the observer-based H∞ control design. A numerical example with simulation results illustrates the effectiveness of the methodology.
This article focuses on the integral sliding mode control (SMC) issue for nonlinear singularly perturbed systems via T-S fuzzy models. A new sufficient criterion for the sliding surface design is attained by adopting convexification techniques, which also guarantees the asymptotic stability of the sliding motion. Then, a new dynamic SMC synthesis scheme is developed to ensure the finite-time reachability of the sliding surface. A simulation example is shown to illustrate the validity of the developed scheme.
In this note, the problem of robust fault detection filter (RFDF) design for a class of linear systems subjected to some nonlinear perturbations and mixed neutral and discrete time-varying delays is investigated based on an H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">¿</sub> performance condition. By introducing a descriptor technique, using Lyapunov-Krasovskii functional and a suitable change of variables, new required sufficient conditions are established in terms of delay-dependent linear matrix inequalities (LMIs) to synthesize the residual generation scheme. Based on Luenberger type observers, the explicit expression of the filters is derived for the fault such that both asymptotic stability and a prescribed level of disturbance attenuation are satisfied for all admissible nonlinear perturbations. A numerically example is provided to show performance of the proposed design approach.
The control of an automotive suspension system using hydraulic actuators is a highly complex nonlinear control task dealing with system nonlinearities, external disturbances, and uncertainties. In this work, an output feedback active suspension control scheme is proposed to achieve a ride comfort while maintaining the road holding for the vehicle. To design the controller, the states of the nonlinear system are first estimated using a highgain observer where the suspension stroke is the only measurable output. The controller is then designed using a recursive derivative nonsingular higher order terminal sliding mode approach that avoids singularity. The practical stability for the closed-loop observer-controller pair is established. Simulation results for the quarter-wheel vehicle over various road conditions demonstrate the effectiveness of the proposed control in improving the suspension performance in both the time and frequency domains.
Vehicle crashworthiness can be assessed by the variety of methods - the most common and direct one is a vehicle crash test. Visual inspection and obtained measurements, such as car acceleration, are used to examine impact severity of an occupant and overall car safety. However, those experiments are complex, time-consuming, and expensive. We propose a method to reproduce car kinematics during a collision using a feedforward neural network to estimate the system by use of nonlinear autoregressive (NAR) models. Specifically, feasibility of applying neural networks with an NAR model to the analysis of experimental data is explored by application to measurements of a vehicle crash test. This model allows us to predict the kinematic responses (acceleration, velocity, and displacement) of a given car during a collision. The major advantage of this approach is that those plots can be obtained without additional teaching of a network.
This chapter deals with the problem of observer-based event-triggered sliding mode control for fractional-order uncertain switched systems with a positive order less than one. First, a fractional-order state observer is designed, based on which a fractional-order integral sliding surface function is proposed. Then, utilizing the estimated observer and sliding mode error vectors, an event-triggered condition is constructed to decide whether the current control signal should be updated or not. Besides, sufficient conditions are derived in the form of linear matrix inequalities (LMIs) to ensure finite-time stability of the augmented closed-loop system by adopting an average dwell time approach. Thereafter, to avoid the occurrence of infinitely many triggers within a finite time, this chapter also discusses the Zeno behavior and refines the results in the previous literature. Finally, to illustrate the effectiveness and superiority of the proposed method, three numerical simulations are provided.
No abstract is provided for this article.
With the integration of artificial intelligence and traffic systems, intelligent traffic systems are utilizing enhanced perception coverage and computational capabilities to provide data-intensive solutions, achieving higher levels of performance than traditional systems. This paper combines the D3QN algorithm from deep reinforcement learning with practical issues and proposes an intelligent emergency traffic signal control system based on Deep Reinforcement Learning (DRL). The system takes into account pedestrian movement and utilizes real-time traffic data and environmental information to model traffic flow and road conditions within a novel state space. It employs the Dueling Double Deep Q-Network (D3QN) to optimize signal control strategies. The system dynamically adjusts signal timings to enhance operational efficiency at intersections. By using the Weibull distribution to simulate realistic traffic congestion and actual traffic data from Shanyin Road in Hangzhou for validation, the results demonstrate that this method converges faster and is more stable compared to other methods, significantly reducing traffic congestion. Furthermore, by incorporating pedestrian movement, this method reduces pedestrian waiting times by 44.736% during peak periods and 22.95% during off-peak periods, while maintaining comparable vehicle queue lengths, delay times, and carbon dioxide emissions. This approach shows the potential improvement of smart urban mobility and resolving intersection congestion challenges.
Summary This article proposes a novel fuzzy sliding‐mode control scheme under an adaptive control strategy for energy management mechanism in electric vehicles that are subject to a regenerative braking system. The effectiveness of the fuzzy logic controller is to adjust the sliding mode parameters according to the slip ratio tracking error between the optimal slip ratio and the actual slip ratio. Specifically, the proposed torque distribution strategy can integrate the best battery condition and energy recovery efficiency under the practical constraints through this control method by fixing the pneumatic braking torque and motor torque. Finally, an electric vehicle model is established in the Simulink environment to verify the applicability of the proposed control algorithm.
No abstract is provided for this article.
Intelligent fault diagnosis aims to build robust mechanical condition recognition models with limited dataset. At this stage, fault diagnosis faces two practical challenges: (1) the variability of mechanical working conditions makes the collected data distribution inconsistent, which brings about the domain shift; (2) some unpredictable unknown fault modes that do not observe in the training dataset may occur in the testing scenario, leading to a category gap. In order to cope with these two entangled challenges, an open set multi-source domain adaptation approach is developed in this study. Specifically, a complementary transferability metric defined on multiple classifiers is introduced to quantify the similarity of each target sample to known classes to weight the adversarial mechanism. By applying an unknown mode detector, unknown faults can be automatically identified. Moreover, a multi-source mutual-supervised strategy is further adopted to mine relevant information between different sources to enhance the model performance. Extensive experiments are conducted on three rotating machinery datasets, and the results show that the proposed method is superior to traditional domain adaptation approaches in the mechanical diagnosis issues that new fault modes occur.
This paper proposed a fuzzy non-fragile tracking control design method for flexible air-breathing hypersonic vehicles (FAHVs). The T-S fuzzy set approach is applied to the modeling of the complex nonlinear dynamics of FAHVs, and a T-S fuzzy model is constructed, which can represent the nonlinear hypersonic vehicle system. Then, by considering the actuator controller coefficients uncertainties and disturbances, the fuzzy non-fragile H ∞ tracking problem for FAHVs is proposed. A state-feedback controller design method is proposed to guarantee the tracking performance in the presence of actuator controller coefficients uncertainties. By a Lyapunov approach, the existence conditions for such a controller are established in terms of linear matrix inequalities (LMIs). An illustrative example is given to show the excellent tracking performance of the proposed controller design method.
