Publications

This paper is concerned with the problems of disturbance tolerance and rejection of discrete switched systems with time-varying delay and saturating actuator. Using the switched Lyapunov function approach, a sufficient condition for the existence of a state feedback controller is proposed such that the disturbance tolerance capability of the closed-loop system is ensured. By solving a convex optimization problem with linear matrix inequality (LMI) constraints, the maximal disturbance tolerance is estimated. In addition, the problem of disturbance rejection of the closed-loop system is solved. Two examples are given to illustrate the effectiveness of the proposed method.

Notice of Violation of IEEE Publication Principles <br><br> After careful consideration by a duly constituted committee, an author of this article, Hamid Reza Karimi, was found to have acted in violation of the IEEE Principles of Ethical Publishing by artificially inflating the number of citations to this article. <br/> This brief deals with the problem of master-slave synchronization for chaotic Lur'e systems with aperiodic sampled data. Specifically, a novel aperiodic adaptive event-triggered communication mechanism is introduced to reduce the transmission load, which covers the previous ones as special cases. By partially resorting to the time-dependent Lyapunov function, a new synchronization criterion is derived, which depends on both the upper and lower bounds of variable sampling interval. Finally, Chua's circuit system is chosen as an illustrative example to show the virtue and effectiveness of the achieved synchronization strategies.

This paper investigates the cooperative trajectory tracking (CTT) control problem of multiple autonomous underwater vehicles (AUVs). The multi‐AUV system is characterized by uncertain dynamics, being subjected to the impact about input saturation constraints and unmeasurable disturbances. First, a neural network‐based data‐driven control algorithm is proposed for the multi‐AUV system with unmeasurable disturbances and model parameters uncertain. The radial basis function neural network is employed to estimate the primary pseudo parameters of an equivalent data model, established through dynamic linearization methods. Subsequently, an iterative learning control approach based on adaptive gain is designed to act as a feedforward scheme along the iteration axis to enhance the tracking accuracy within a time constraint. Third, to prove that the resulting CTT control system fulfills the bounded stability under the proposed control approach, a formal stability analysis is provided. Finally, a simulation case study is conducted to illustrate the effectiveness of the proposed CTT control approach.

Semiglobal practical integral input-to-state stability (SP-iISS) for a feedback interconnection of two discrete-time subsystems is given. We construct a Lyapunov function from the sum of nonlinearly-weighted Lyapunov functions of individual subsystems. In particular, we consider two main cases. The former gives SP-iISS for the interconnected system when both subsystems are semiglobally practically integral input-to-state stable. The latter investigates SP-iISS for the overall system when one of subsystems is allowed to be semiglobally practically input-to-state stable. Moreover, SP-iISS for discrete-time cascades and a feedback interconnection including a semiglobally practically integral input-to-state stable subsystem and a static subsystem are given. As an application of the results, these can be exploited in controller design for a sampled-data system in the framework proposed in Nešić et al. (1999) and Nešić and Angeli (2002). We illustrate such a controller design via an example.

In this paper, a novel approach is proposed to design a robust observer for a class of Takagi-Sugeno (T-S) fuzzy models with unknown inputs and delays. The main contribution of this paper is to consider unknown inputs and a mixed neutral and discrete delay in the model. Also, the system is subject to disturbances, which are imposed on both state and output signals. Delay-dependent sufficient conditions for the design of an unknown input T-S observer with time delays are given in terms of linear matrix inequalities (LMIs). Some relaxations are introduced by using intermediate variables. A numerical example is given to illustrate the effectiveness of the given results.

This paper investigates an adaptive consensus problem of a class of nonlinear multiagent systems in which the states are unmeasurable and the dynamics of all agents are supposed to be in strict-feedback form with unknown time-varying control coefficients. Due to the presence of uncertain nonlinearities in agents' dynamics, radial basis function neural networks are used to approximate the unknown nonlinear functions, and a neural-network-based observer is designed to estimate the unmeasured states. The adaptive observer-based protocols are based on the relative output information of neighbors, and are constructed by adopting the dynamic surface control technique. It is proved that practical consensus of the system can be achieved with the proposed protocols. A simulation example is given to show the effectiveness of the proposed method.

I am honored to serve as the Editor-in-Chief of the new journal of Designs.[...]

In this study, a novel two‐step methodology is applied in designing static output‐feedback controllers for a class of vehicle suspension systems. Following this approach, an effective synthesis of static output‐feedback controllers can be carried out by solving two consecutive linear matrix inequality optimisation problems. To illustrate the main features of the proposed design strategy, two different static output‐feedback H ∞ controllers are designed for a quarter‐car suspension system. The first of those controllers uses the suspension deflection and the sprung mass velocity as feedback information, whereas the second one only requires the sprung mass velocity to compute the control actions. Numerical simulations indicate that, despite the restricted feedback information, the proposed static output‐feedback H ∞ controllers exhibit a good behaviour in terms of both frequency and time responses, when compared with the corresponding state‐feedback H ∞ controller.

This paper presents an integrated self-triggered control strategy with convergence guarantees for model-free continuous-time systems using reinforcement learning. To consider the control cost and triggering consumption in the self-triggered scheme simultaneously, an integrated cost function is proposed. With this integrated cost function, the trade-off between the triggering occupation and control performance could be adjusted according to different requirements. Then, the actor-critic framework of reinforcement learning is employed to learn the control inputs and triggering intervals by minimizing the corresponding integrated cost function. Considering the divergent characteristics between the control inputs and triggering intervals, two different actors are utilized to learn the triggering strategy and control policy, respectively. Also, the convergence of the developed model-free self-triggered control learning algorithm is proved to ensure the limited learning duration of both the control policy and triggering strategy. The proposed framework can be used to design self-triggered controllers for a wide range of engineering systems with unknow dynamics, including control of aircraft, robots, chemical processes, and other automated systems. Finally, the effectiveness and superiorities of the proposed method are verified by an illustrative example.

This paper deals with the modelling of engine-body vibration structure to robust control of bounce and pitch vibrations using Haar wavelets. The authors' attention is focused on development of the Haar wavelet-based robust optimal control for vibration reduction of the engine-body system computationally that guarantee desired L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> gain performance. The Haar wavelet properties are introduced and utilized to find the approximate solutions of trajectories and robust optimal control by solving only algebraic equations instead of solving the Riccati differential equation. Numerical results are presented to illustrate the advantage of the approach

A method is proposed to retrieve mathematical formula in LaTeX documents. Firstly, we represent the retrieved mathematical formula by binary tree according to its LaTeX description, normalize the structure of the binary tree, and obtain the structure code and then search the mathematical formula table that is named by the structure code and the formula elements of the first two levels of the binary tree in the mathematical formula database. If the table exists, then we search the normalizing variable name preorder traversing sequence of the binary tree in the table and display the document information that contain the mathematical formula. The experimental results show that the algorithm realizes the retrieval of mathematical formula in LaTeX documents and has higher retrieval precision and faster retrieval speed.

This paper deals with a computational optimization approach to the problem of state-feedback control design for a wind turbine system. The first step of the study is to develop a reduced order model for the system by considering the most important physical phenomena of aerodynamics and structural dynamics. Moreover, the behavior of the system can be influenced by the coupled dynamics between the tower motions and the blade pitch and turbine speed which can cause instabilities in the control loops in the worst case. By using a suitable wavelet function, called Haar functions, a recursive computational procedure is established for finding the system dynamics approximately by solving only algebraic equations instead of solving the Riccati differential. Simulation results are given to illustrate the usefulness of the proposed control methodology.

In this paper an attempt is made to explain how and why the muon neutrinos flow faster than the speed of light in the OPERA neutrino experiment by using the theory of quantized space and time and internal structure of elementary particles derived from a new model of mine [1]. Also this paper shows that: 1-This motion is achieved by two velocities. The first is a speed of in the time and length quanta. Then there is a velocity greater than that of light in the super dimension. 2-In these tests, muon neutrinos move with a velocity . Only 23.98 m of this happens in the super dimension [2].

This paper is using the theory of quantized time - length and a new model of internal structure of elementary particles by my expression [1] to solve Einstein twin's paradox.

The problem of adaptive sliding mode control for a class of continuous-time Takagi–Sugeno fuzzy systems regulated by the event of switching rules relying on neural network estimation method is put forward in this paper, where the plant suffers from state delay, internal structure uncertainty, and unknown nonlinearity. By proposing a switching surface in integral type, it obtains a sliding motion with desired property. In addition, to compensate the plant unknown nonlinearity and to meet the reaching condition, a radial basis function neural-network-based adaptive law is designed to ensure the existence of sliding motion in finite time. Furthermore, for the purpose of exponential stabilization of the sliding motion, a linear matrix inequality condition accompanied with switching signal characterized by an average dwell time is put forward. Finally, two numerical examples, one with all subsystems unstable and the other with stable subsystems and unstable systems, are shown to confirm the validity.