Prakash Mani

Bio: Prakash Mani is an academic researcher from Kunsan National University. The author has contributed to research in topics: Fuzzy logic & Lyapunov stability. The author has an hindex of 4, co-authored 7 publications receiving 145 citations. Previous affiliations of Prakash Mani include VIT University.

Adaptive Synchronization of Reaction–Diffusion Neural Networks and Its Application to Secure Communication

Abstract: This paper is mainly concerned with the synchronization problem of reaction–diffusion neural networks (RDNNs) with delays and its direct application in image secure communications. An adaptive control is designed without a sign function in which the controller gain matrix is a function of time. The synchronization criteria are established for an error model derived from master–slave models through solving the set of linear matrix inequalities derived by constructing the suitable novel Lyapunov–Krasovskii functional candidate, Green’s formula, and Wirtinger’s inequality. If the proposed sufficient conditions are satisfied, then the global asymptotic synchronization of the error model is guaranteed. The numerical illustrations are provided to demonstrate the validity of the derived synchronization criteria. In addition, the role of system parameters is picturized through the chaotic nature of RDNNs and those unprecedented solutions is utilized to promote better security of image transactions. As is evident, the enhancement of image encryption algorithm is designed with two levels, namely, image watermarking and diffusion process. The contributions of this paper are discussed as concluding remarks.

Digital Controller Design via LMIs for Direct-Driven Surface Mounted PMSG-Based Wind Energy Conversion System

Abstract: The main concern of this paper is to design the efficient sampled-data controller scheme that resolves the stabilization issue of a surface-mounted permanent magnet synchronous generator (PMSG)-based wind energy conversion system (WECS). Distinct to the existing controller schemes on WECS, the present scheme contains both continuous (plant) and discrete (control) type of signals which outperforms the traditional scheme with continuous or discrete signals. Besides that the fundamental analysis of the closed-loop system under the designed controllers explore the dynamical characteristics of the considered PMSG-based WECS. The stability and stabilization of the proposed closed-loop system have guaranteed through the Lyapunov stability theory and solvable linear matrix inequalities (LMIs). In detail, first, the nonlinear PMSG model has equivalently expressed into linear submodels via the Takagi–Sugeno (T–S) fuzzy approach based on suitable membership rules. Second, the sufficient conditions have been derived as LMIs that ensure the stability and stabilization of the formulated T–S fuzzy PMSG-based WECS. Finally, the effectiveness of the designed controller as well as the consistency of sufficient conditions has demonstrated through numerical evaluations of the closed-loop system.

Design of Observer-Based Event-Triggered Fuzzy ISMC for T–S Fuzzy Model and its Application to PMSG

Abstract: The main aim of this article is to design the observer-based event-triggered (ET) fuzzy integral sliding mode control (ETFISMC) for the generalized Takagi–Sugeno (T–S) fuzzy system which is formulated from a nonlinear system through blending the membership grades altogether. Distinct to the existing controller schemes, the proposed fuzzy integral sliding mode control (FISMC) scheme contains the ET condition which needs to be satisfied for the activation of the controller. Besides that, the network-induced communication constraints are considered into the derivation of sufficient conditions, and then the corresponding stabilization issue is attenuated in the sense of $H_{\infty }$ control performance. In addition, the appropriate Lyapunov–Krasovskii functional (LKF) candidate is constructed and evaluated through convex matrix inequality approach that ensures the stable $H_{\infty }$ performance of the closed-loop system in terms of solvable linear matrix inequalities (LMIs). Further, instead of considering the general problem for the validation of the proposed result, the stabilization problem of nonlinear chaotic permanent magnet synchronous generator (PMSG) model is taken into account for the validation of the proposed sufficient conditions. The purpose of considering the PMSG model is because of its significance in the wind energy conversion systems.

Adaptive Reinforcement Learning Neural Network Control for Uncertain Nonlinear System With Input Saturation

Abstract: In this paper, an adaptive neural network (NN) control problem is investigated for discrete-time nonlinear systems with input saturation. Radial-basis-function (RBF) NNs, including critic NNs and action NNs, are employed to approximate the utility functions and system uncertainties, respectively. In the previous works, a gradient descent scheme is applied to update weight vectors, which may lead to local optimal problem. To circumvent this problem, a multigradient recursive (MGR) reinforcement learning scheme is proposed, which utilizes both the current gradient and the past gradients. As a consequence, the MGR scheme not only eliminates the local optimal problem but also guarantees faster convergence rate than the gradient descent scheme. Moreover, the constraint of actuator input saturation is considered. The closed-loop system stability is developed by using the Lyapunov stability theory, and it is proved that all the signals in the closed-loop system are semiglobal uniformly ultimately bounded (SGUUB). Finally, the effectiveness of the proposed approach is further validated via some simulation results.

Network-based passive estimation for switched complex dynamical networks under persistent dwell-time with limited signals

Abstract: In this paper, the state estimation issue for a set of switched complex dynamic networks affected by quantization is studied, in which the switching process is assumed to follow persistent dwell-time switching regulation. Thereinto, the switching regulation aforementioned describes the switchings among different parameters on complex dynamic networks. Meanwhile, for the network-based model, in the communication channels from the sensor to the estimator, quantization is inevitable to be taken into consideration. To track partially inaccessible information in the target system, a state estimator is thoroughly reconstructed. Intensive attention is that a set of sufficient conditions can be derived by using some simple matrix transformation methods, linear matrix inequality and Lyapunov stability theory, to further assure the error dynamic obtained is globally uniformly exponentially stable and meets passive property. The serviceability of the state estimator gains solved is finally verified and the effectiveness of the proposed design approach is further illustrated.