This article is concerned with the dissipative asynchronous filtering for a class of networked Takagi–Sugeno fuzzy Markov jumping systems subject to packet dropouts. The system measurements are quantized with a logarithmic quantizer and a Bernoulli model is introduced to deal with the packet dropouts occurred in the communication channel from the quantizer to the filter. Both the quantizer and the filter are mode dependent, and their modes are asynchronous with those of the physical plant, governed by two hidden Markov models. Based on the mode-and fuzzy-rule-dependent Lyapunov function method, a sufficient condition is derived such that the resultant filtering error system is stochastically stable in the mean-square sense and strictly dissipative. The slack-matrix approach and Finsler’s lemma are further exploited to design suitable dissipative filters if a set of linear matrix inequalities are feasible. Finally, two examples including a tunnel-diode circuit system are taken to demonstrate the efficiency of our proposed results.

For the networked control system with limited bandwidth and flexible workload, a dynamic intelligent feedback scheduling strategy is proposed. Firstly, a monitor is used to acquire the current available network bandwidth. Then, the new available bandwidth in the next interval is predicted by using LS_SVM approach. At the same time, the dynamic performance indices of all control loops are obtained with a two-dimensional fuzzy logic modulator. Finally, the predicted network bandwidth is dynamically allocated by the bandwidth manager and the priority allocator in terms of the loops' dynamic performance indices. Simulation results show that the sampling periods and priorities of control loops are adjusted timely according to the network workload condition and the dynamic performance of control loops, which make the system running in the optimal state all the time.

Pre-Bötzinger complex (PBC) neurons located in mammalian brain are the necessary conditions to produce respiratory rhythm, which has been widely verified experimentally and numerically. At present, one of the two different types of bursting mechanisms found in PBC mainly depends on the calcium-activated of non-specific cation current (I_CaN). In order to study the influence of I_CaN and stimulus current I_exc in PBC inspiratory neurons, a single compartment model was simplified, and firing patterns of the model was discussed by using stability theory, bifurcation analysis, fast, and slow decomposition technology combined with numerical simulation. Under the stimulation of different somatic applied currents, the firing behavior of neurons are studied and exhibit multiple mix bursting patterns, which is helpful to further understand the mechanism of respiratory rhythms of PBC neurons.

This paper investigates the observer-based controller design problem for a class of nonlinear networked control systems with random time-delays. The nonlinearity is assumed to satisfy a global Lipschitz condition and two dependent Markov chains are employed to describe the time-delay from sensor to controller (S-C delay) and the time-delay from controller to actuator (C-A delay), respectively. The transition probabilities of S-C delay and C-A delay are both assumed to be partly inaccessible. Sufficient conditions on the stochastic stability for the closed-loop systems are obtained by constructing proper Lyapunov functional. The methods of calculating the controller and the observer gain matrix are also given. Two numerical examples are used to illustrate the effectiveness of the proposed method.