On Stabilization of Quantized Sampled-Data Neural-Network-Based Control Systems

Abstract

This paper investigates the problem of stabilization of sampled-data neural-network-based systems with state quantization. Different with previous works, the communication limitation of state quantization is considered for the first time. More specifically, it is assumed that the sampled state measurements from sensor to the controller are quantized via a quantizer. To reduce conservativeness, a novel piecewise Lyapunov-Krasovskii functional (LKF) is constructed by introducing a line-integral type Lyapunov function and some useful terms that take full advantage of the available information about the actual sampling pattern. Based on the new LKF, much less conservative stabilization conditions are derived to obtain the maximal sampling period and the minimal guaranteed cost control performance. The desired quantized sampled-data three-layer fully connected feedforward neural-network-based controllers are designed by a linear matrix inequality approach. A search algorithm is given to find the optimal values of tuning parameters. The effectiveness and advantage of proposed method are demonstrated by the numerical simulation of an inverted pendulum.

An imperceptible & robust digital image watermarking scheme based on DWT, entropy and neural network

“…They demonstrated a basic neural system with electrical circuits [15]. With the progress of neural networks, researchers found advantages of using the neural network in various types of systems to improve the performance [16,17].…”

Effect of Proportional Delays and Continuously Distributed Leakage Delays on Global Exponential Convergence of CNNS

“… discussed the global asymptotic stability of stochastic fuzzy cellular neural networks with multiple time‐varying delays, Wang et al. focused on the problem of stabilization of sampled‐data neural‐ network‐based systems with state quantization. For more related works on these aspects, we refer the readers to and the references cited therein.…”

“…During the past decades, cellular neural networks (CNNs) have received much attention due to their potential applications in many fields such as speech, robotics, psychophysics, image processing perception, vision, adaptive pattern recognition and so on [1,2], many researchers focus on the investigation of cellular neural networks and some excellent achievements on cellular neural networks have been reported. For example, Xu et al [3] studied the exponential stability of almost periodic solutions for memristor-based neural networks with distributed leakage delays, Balasubramaniam et al [4] investigated the existence and global asymptotic stability of fuzzy cellular neural networks with time delay in the leakage term and unbounded distributed delays, Balasubramaniam Manuscript et al [5] discussed the global asymptotic stability of stochastic fuzzy cellular neural networks with multiple time-varying delays, Wang et al [39] focused on the problem of stabilization of sampled-data neuralnetwork-based systems with state quantization. For more related works on these aspects, we refer the readers to [6][7][8][9][21][22][23][24][25][36][37][38][41][42][43] and the references cited therein.…”

Quantized sliding mode control in delta operator framework

“…[3][4][5][6][7][8][9][10][11][12] In modern engineering control systems, a large number of information-processing devices are embedded between the plants and the controllers, thus quantization phenomena occur and have been paid great concern from the control community. [13][14][15][16][17][18][19][20][21][22][23][24][25] A natural and challenging problem in SMC is how to analyze quantization effects and develop an appropriate quantized feedback control strategy to guarantee the stability of system.…”