Publications

ABSTRACT In this article, the identifier–critic–actor neural adaptive optimal control issue is addressed for a class of fully nonaffine pure‐feedback nonlinear cyber‐physical systems with input quantization and time‐reference‐dependent output constraints. By constructing a time‐varying asymmetric barrier Lyapunov function and integrating it with the dynamic surface control method and a reinforcement learning algorithm, a controller is designed based on a neural network approximation of the identifier–critic–actuator structure. In this framework, the identifier estimates unknown dynamics, the critic evaluates system performance, and the actor executes the control action. The control scheme involves designing the actual control inputs for all virtual and dynamic surface controls as the optimal solutions of their corresponding subsystems. The updated law is derived by taking the negative gradient of a simple positive function, which is constructed from the partial derivatives of the Hamilton–Jacobi–Bellman equation. In parallel, the proposed quantizer combines the benefits of both hysteretic and uniform quantization. A key aspect of this article is the simultaneous consideration of constraint boundaries related to both the reference signal and time, which adds complexity to the design of the control algorithm. Stability analysis confirms that all signals remain bounded and adhere to the time‐ and reference‐dependent constraints imposed on output.

ABSTRACT This article addresses the stabilization problem of networked control systems (NCSs) under non‐periodic denial‐of‐service (DoS) attacks and actuator saturation. A non‐periodic DoS attack model is put forward, taking into account the minimum communication security time period, the maximum attack duration, and the frequency constraint. This model facilitates the analysis of system performance in relation to attack characteristics by establishing connections between two generalized assumption models. For the NCSs suffering from both non‐periodic DoS attacks and actuator saturation, a dynamic memory‐based event‐triggered mechanism (DMETM) is designed to maintain control performance and reduce network resource consumption. To deal with the actuator saturation phenomenon, a set of memory‐based primary‐auxiliary controllers is designed to compensate for actuator saturation effects via convex hull representation. A guaranteed‐cost function accounting for system states and input saturation is developed. Based on the obtained switched system model and the piecewise Lyapunov‐Krasovskii functional (LKF), sufficient conditions are derived to ensure local asymptotic stability and weighted disturbance attenuation with performance. The co‐design of DMETM weighting matrices and controller gains is presented in the form of linear matrix inequalities (LMIs). Additionally, methods for estimating the attraction domain and deriving the upper bound of the cost function are given by the obtained LMIs. Finally, the effectiveness and superiority of the proposed method are validated on a high‐speed train system.

This paper addresses the exponential stability problem for a class of delayed bidirectional associative memory (BAM) neural networks with delays. A sliding intermittent controller which takes the advantages of the periodically intermittent control idea and the impulsive control scheme is proposed and employed to the delayed BAM system. With the adjustable parameter taking different particular values, such a sliding intermittent control method can comprise several kinds of control schemes as special cases, such as the continuous feedback control, the impulsive control, the periodically intermittent control, and the semi-impulsive control. By using analysis techniques and the Lyapunov function methods, some sufficient criteria are derived for the closed-loop delayed BAM neural networks to be globally exponentially stable. Finally, two illustrative examples are given to show the effectiveness of the proposed control scheme and the obtained theoretical results.

This paper investigates a convex optimization approach to the problem of robust H ∞ filtering for uncertain linear systems connected over a common digital communication network.We consider the case where quantizers are static and the parameter uncertainties are norm bounded.Firstly, we propose a new model to investigate the effect of both the output quantization levels and the network conditions.Secondly, 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) for the existence of the desired network-based quantized filters with simultaneous consideration of network induced delays and measurement quantization.The explicit expression of the filters is derived to satisfy both asymptotic stability and a prescribed level of disturbance attenuation for all admissible norm bounded uncertainties.

This paper addresses the problem of bond graph methodology as a graphical approach for modeling wind turbine systems. In this case, we consider the modeling of a wind turbine system with individual pitch control scheme and the interaction with tower motions. Two different bond graph models are presented, one complex and one simplified. Furthermore, the purpose of this paper is not to validate a specific wind turbine model, but rather show the difference between modeling with a classical mechanical method and by using the bond graph approach. Simulation results illustrate the simplified system response obtained using implementation of the governing equations in MATLAB/Simulink and is compared with a bond graph implementation in the simulation program 20-sim.

In this paper, an improved recurrent neural network (RNN) scheme is proposed to perform the trajectory control of redundant robot manipulators using remote center of motion (RCM) constraints. Firstly, learning by demonstration is implemented to model the surgical operation skills in the Cartesian space. After that, considering the kinematic constraints associated with the optimization control of redundant manipulators, we propose a novel RNN-based approach to facilitate accurate task tracking based on the general quadratic performance index, which includes managing the constraints on RCM joint angle, and joint velocity, simultaneously. The results of the conducted theoretical analysis confirm that the RCM constraint has been established successfully, and accordingly. The corresponding end-effector tracking errors asymptotically converge to zero. Finally, demonstration experiments are conducted in a laboratory setup environment using KUKA LWR4+ to validate the effectiveness of the proposed control strategy.

The use of magnetorheological (MR) dampers for mitigating vibrations caused by seismic motions in civil engineering structures has attracted much interest in the scientific community because of the advantages of this class of device. It is known that MR dampers can generate high damping forces with low energy requirements and low cost of production. However, the complex dynamics that characterize MR dampers make difficult the control design for achieving the vibration reduction goals in an efficient manner. In this article, a semiactive controller based on the backstepping technique is proposed. The controller was applied to a three-story building with an MR damper at its first floor subjected to seismic motions. The performance of the controller was evaluated experimentally by means of real time hybrid testing.

In this article, the cluster synchronization problem for a class of the nonlinearly coupled delayed neural networks (NNs) in both finite- and fixed-time cases are investigated. Based on the Lyapunov stability theory and pinning control strategy, some criteria are provided to ensure the cluster synchronization of the nonlinearly coupled delayed NNs in both finite-and fixed-time aspects. Then, the settling time for stabilization that is dependent on the initial value and independent of the initial value is estimated, respectively. Finally, we illustrate the feasibility and practicality of the results via a numerical example.

This chapter addresses the Mittag-Leffler projective synchronization problem of fractional-order coupled systems (FOCS) on complex networks without strong connectedness by employing fractional sliding mode control (SMC). By integrating the hierarchical algorithm with graph theory, a novel SMC strategy is devised to achieve projective synchronization between the master and slave systems. This strategy encompasses both global complete synchronization and global antisynchronization, thereby providing a comprehensive solution for various synchronization scenarios. Additionally, new criteria are derived to ensure the Mittag-Leffler stability of the projective synchronization error system, offering robust theoretical guarantees for the proposed approach. Finally, a numerical example is provided to demonstrate the validity and effectiveness of the proposed method, highlighting its potential applications in complex network systems and its superiority in handling fractional-order dynamics.

In this paper we present the application of regressive models to simulation of car-to-pole impacts. Three models were investigated: RARMAX, ARMAX and AR. Their suitability to estimate physical system parameters as well as to reproduce car kinematics was examined. It was found out that they not only estimate the one quantity which was used for their creation (car acceleration) but also describe the car’s acceleration, velocity and crush. A virtual experiment was performed to obtain another set of data for use in further research. An AR model to predict the behavior of a low-speed car impacting a rigid barrier was created and verified.

As a complex process, vehicle crash is challenging to be described and estimated mathematically. Although different mathematical models are developed, it is still difficult to balance the complexity of models and the performance of estimation. The aim of this work is to propose a novel scheme to model and estimate the processes of vehicle-barrier frontal crashes. In this work, a piecewise model structure is predefined to represent the accelerations of vehicle in frontal crashes. Each segment in the model is corresponding to the energy absorbing component in the crashworthiness structure. With the help of Ensemble Empirical Mode Decomposition (EEMD), a robust scheme is proposed for parameter identification. By adjusting the model structure and parameters according to the initial velocity, crash processes in different conditions are estimated effectively. The estimation results exhibit good agreement with finite element (FE) simulations in three different cases. It is shown that, the proposed model keeps low complexity. Furthermore, the structure information of vehicle is involved in improving the accuracy and ability of crash estimation.