Publications

This paper is devoted to design an event-triggered data-driven control for a class of disturbed nonlinear systems with quantized input. A uniform quantizer reconstructed with decreasing quantization intervals is employed to reduce the quantization error. A neural network-based estimation strategy is proposed to estimate both the pseudo partial derivative and disturbances. Consequently, an input triggering rule for single-input single-output systems is provided by incorporating the estimated disturbances, the quantization error bound and tracking errors. Resorting to the Lyapunov method, sufficient conditions for synthesized error systems to be uniformly ultimately bounded are presented. The validity of the proposed scheme is demonstrated via a simulation example.

This paper investigates the design of hybrid interval observers (IOs) of switched linear systems involving additive disturbances, whose subsystems need not to be cooperative. We first propose the definition of IOs for switched systems and extend the coordinate transformation for non-switched linear systems into switched ones. Based on the switched coordinate transformation, the considered switched linear system is converted into a hybrid cooperative system, whose states jump at each switching times. Then, by using the multiple Lyapunov functions method, IOs for switched linear systems are designed in two cases, that is, all of the subsystem matrices are Hurwitz and some of the subsystem matrices are not Hurwitz. Moreover, by exploiting the stabilization property of switching behaviors, IOs for switched systems, whose subsystem matrices are not all Hurwitz, are proposed based on multiple discretized Lyapunov functions. The constructed IOs for the considered switched systems are hybrid dynamical systems and the initialized states of the IOs at each switching time are coupled, since the transformation matrices for different subsystems are different. Finally, we afford an example to illustrate the validity of the derived results.

The aim of this study is focusing the issue of traditional clustering algorithm subjects to data space distribution influence, a novel clustering algortihm combined with rough set theory is employed to the normal clustering. The proposed rough clustering algorithm takes the condition attributes and decision attributes displayed in the information table as the consistency principle, meanwhile it takes the data supercubic and information entropy to realize data attribute shortcutting and discretizing. Based on above discussion, by applying assemble feature vector addition principle computiation only one scanning information table can realize clustering for the data subject. Experiments reveal that the proposed algorithm is efficient and feasible.

Deep neural networks and in particular, deep neural classifiers have become an integral part of many modern applications. Despite their practical success, we still have limited knowledge of how they work and the demand for such an understanding is evergrowing. In this regard, one crucial aspect of deep neural network classifiers that can help us deepen our knowledge about their decision-making behavior is to investigate their decision boundaries. Nevertheless, this is contingent upon having access to samples populating the areas near the decision boundary. To achieve this, we propose a novel approach we call Deep Decision boundary Instance Generation (DeepDIG). DeepDIG utilizes a method based on adversarial example generation as an effective way of generating samples near the decision boundary of any deep neural network model. Then, we introduce a set of important principled characteristics that take advantage of the generated instances near the decision boundary to provide multifaceted understandings of deep neural networks. We have performed extensive experiments on multiple representative datasets across various deep neural network models and characterized their decision boundaries. The code is publicly available at https://github.com/hamidkarimi/DeepDIG/.

With the development of new energy vehicles, EVs have received ever-increasing research attention as an essential strategic orientation for the world to face climate change and energy issues. EVs have significant energy-saving and emission-reduction advantages, but power battery state estimation accuracy has always been a bottleneck restricting its promotion. Centered on power battery cloud management and control methodology, this work systematically examines the development of battery cloud models, formulates battery life and safety management strategies, and investigates the integration of cloud management technology within advanced electronic and electrical architectures. Firstly, the overall framework of the device–cloud fusion technology is introduced. Secondly, aiming at the complex problem of power battery state estimation, the models and fusion estimation methods of the cloud and vehicle battery models are summarized. Then, the joint estimation method is outlined for the power battery states, including the state of charge and state of health. Finally, a viable cloud-based management solution is elucidated through a comprehensive comparison and analysis of the current battery management technologies' strengths and limitations. This offers a theoretical framework for advancing power battery cloud management and control technology.

Mathematical control of complex systems have already become an ideal research area for control engineers, mathematicians, computer scientists, and biologists to understand, manage, analyze, and interpret functional information/ dynamical behaviours from real-world complex dynamical systems, such as communication systems, process control, environmental systems, intelligent manufacturing systems, transportation systems, and structural systems.This special issue aims to bring together the latest/innovative knowledge and advances in mathematics for handling complex systems.Topics include, but are not limited to the following: control systems theory (behavioural systems, networked control systems, delay systems, distributed systems, infinite-dimensional systems, and positive systems); networked control (channel capacity constraints, control over communication networks, distributed filtering and control, information theory and control, and sensor networks); and stochastic systems (nonlinear filtering, nonparametric methods, particle filtering, partial identification, stochastic control, stochastic realization, system identification).We have solicited submissions to this special issue from control engineers, electrical engineers, computer scientists, and mathematicians.After a rigorous peer review process, 31 papers have been selected that provide overviews, solutions, or early promises, to manage, analyze, and interpret dynamical behaviours of complex systems.These papers have covered both the theoretical and practical aspects of complex systems

In this paper, a new strategy to design static output-feedback controllers for a class of vehicle suspension systems is presented. A theoretical background on recent advances in output-feedback control is first provided, which makes possible an effective synthesis of static output-feedback controllers by solving a single linear matrix inequality optimization problem. Next, a simplified model of a quarter-car suspension system is proposed, taking the ride comfort, suspension stroke, road holding ability, and control effort as the main performance criteria in the vehicle suspension design. The new approach is then used to design a static output-feedback<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mi>∞</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math>controller that only uses the suspension deflection and the sprung mass velocity as feedback information. Numerical simulations indicate that, despite the restricted feedback information, this static output-feedback<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mi>∞</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math>controller exhibits an excellent behavior in terms of both frequency and time responses, when compared with the corresponding state-feedback<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mrow><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mi>∞</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math>controller.

We address the problem of downlink throughput improvement for IEEE 802.11a/g systems by using a modified access point (AP) equipped with multiple antennas. The main restriction is that standard terminals should not be modified in any way. An alternating time-offset space division multiple access (SDMA) solution is proposed to overcome restrictions imposed by the legacy terminals requirement. Simulations based on the IEEE 802.11 channel models demonstrate that a near doubling of downlink capacity can be achieved in a conference room environment.

With increasing and tightening requirements on the control and optimization performance, the traditional control and optimization strategies may not qualify in many application problems.To meet the challenges and requirements, advanced control and optimization methodologies should be employed or developed.Here, the advanced control and optimization denote the ones which, in principle, can best achieve the objectives in the presence of nonlinearities, highly interactivities, or newly emerged operating circumstances.As the advanced control and optimization will provide a basis for the design and operation of practical systems, these advanced techniques would result in substantial and sustainable benefits.The overall aims of this special issue are twofold: (1) to provide an up-to-date overview of the research directions in the advanced control and optimization; (2) to illustrate how to formulate problems from automotive systems and develop suitable theory to solve the corresponding problems.Of particular interest the papers in this special issue are devoted to the development of advanced control and optimization including network control, nonlinear model predictive control, dynamic programming, and integral programming, with applications to complex automotive systems including vehicle dynamics and control, combustion and emission control, and vehicle integration and optimization, for instance.Topics in this special issue include, but are not limited to: (1) network analysis and protocol optimization, (2) fast nonlinear model predictive control and optimization, (3) advanced vehicle

We propose a method to improve the performance of evolutionary algorithms (EA). The proposed approach defines operators which can modify the performance of EA, including Levy distribution function as a strategy parameters adaptation, calculating mean point for finding proper region of breeding offspring, and shifting strategy parameters to change the sequence of these parameters. Thereafter, a set of benchmark cost functions is utilized to compare the results of the proposed method with some other well-known algorithms. It is shown that the speed and accuracy of EA are increased accordingly. Finally, this method is exploited to optimize fuzzy control of truck backer-upper system.