In the synchronization of cyber-physical systems (CPSs), modeling the nonlinear dynamics of physical plants is a challenging task. To address this challenge, we propose a novel H∞ controller design method that leverages a data-driven approach to robustly synchronize CPSs and ensure their stability. In the proposed approach, the input-output relationship of the physical system is learned using long short-term memory (LSTM) networks to approximate the unknown dynamics of CPSs. Furthermore, we exploit an effective control scheme for trained LSTM networks to effectively handle the nonlinearity of activation functions. To ensure stability and performance in the convergence of synchronization error, a controller design criterion is derived for the trained LSTM network in terms of linear matrix inequalities, and the controller gain is computed using convex optimization techniques. In addition, we present an anomaly detection algorithm using the proposed method, which can synchronize CPSs and detect abnormal signals without requiring any prior physical model information. Consequently, the stability of the synchronization control system can be ensured, enabling its application to anomaly detection. Finally, the effectiveness of the proposed method is validated through an experiment on a motor control system even in abnormal operating conditions.

Although robot manipulators are widely used in advanced industrial applications, their dynamics has very high complexity and uncertainties, making exact mathematical modeling difficult and preventing high-precision tracking control. Herein, we propose a practical high-performance model-free controller for robot manipulators that attenuates the undesirable side effects of a time-delayed state-based estimation (TDE) technique in terms of the receding-horizon$\mathcal {H}_\infty$performance. By constructing tracking error dynamics with a sliding variable, the effects of TDE errors are identified and suppressed each time in terms of the fixed-horizon$\mathcal {H}_\infty$performance. All initial states are shown to converge to a certain bounded set within a precomputed finite time. The proposed approach is beneficial for sudden transient responses and the temporarily unbounded TDE errors that cannot be handled by existing TDE-based controllers. Finally, the stability of the proposed controller is proven based on the comparison Lemma, and simulations and experiments show that its tracking performance and robustness are superior to those of conventional control algorithms.

This article investigates a novel sampled-data synchronization controller design method for chaotic neural networks (CNNs) with actuator saturation. The proposed method is based on a parameterization approach which reformulates the activation function as the weighted sum of matrices with the weighting functions. Also, controller gain matrices are combined by affinely transformed weighting functions. The enhanced stabilization criterion is formulated in terms of linear matrix inequalities (LMIs) based on the Lyapunov stability theory and weighting function's information. As shown in the comparison results of the bench marking example, the presented method much outperforms previous methods, and thus the enhancement of the proposed parameterized control is verified.

This article presents a new sampled-data fuzzy filter design method for Takagi–Sugeno fuzzy systems with the asynchronous premise variables. In the new fuzzy filter design method, the membership functions of the filter are affine transformed by scaling and biasing the system’s membership functions. Taking the advantage of the newly proposed method, the asynchronous problem of the premise variables between the system and filter is easily resolved. Based on the looped function and a modified free-weighting matrix inequality, the sampled system’s output is handled, and the filter design condition is formulated in terms of a parameterized linear matrix inequality with affine matched fuzzy parameter vectors. Additionally, a modified Finsler’s lemma is devised to handle the affine matched fuzzy parameter vectors. By utilizing the relationship between the transformed membership functions, the filter design condition for$H_{\infty }$performance is enhanced with larger allowable maximum bounds of variable sampling intervals. Lastly, the superiority of the presented method is verified by comparing the numerical simulations with existing methods.

This article proposes a new Luenberger-type state estimator that has parameterized observer gains dependent on the activation function, to improve the$H_{\infty }$state estimation performance of the static neural networks with time-varying delay. The nonlinearity of the activation function has a significant impact on stability analysis and robustness/performance. In the proposed state estimator, a parameter-dependent estimator gain is reconstructed by using the properties of the sector nonlinearity of the activation functions that are represented as linear combinations of weighting parameters. In the reformulated form, the constraints of the parameters for the activation function are considered in terms of linear matrix inequalities. Based on the Lyapunov–Krasovskii function and the improved reciprocally convex inequality, enhanced conditions for designing a new state estimator that guarantees$H_{\infty }$performance are derived through a parameterization technique. The compared results with recent studies demonstrate the superiority and effectiveness of the presented method.

This paper proposed a robust H∞fault estimation method and a fault-tolerant control(FTC) of vapor compression cycle(VCC) systems. Based on a moving boundary model(MBM), linearization and model reduction were conducted. Actuator faults were estimated by a proportional-integral(PI)-observer and it was designed using a H∞estimation theory. Robust fault-tolerant controller was designed using a convex optimazation and input constraints of VCC system were considered. Simulation result was presented to show the effectiveness of the proposed method.