This paper considers the problem of asynchronous fault detection filter (FDF) design for T-S fuzzy singular systems via dynamic event-triggered scheme. A mode-dependent dynamic event-triggered scheme is adopted to alleviate the communication load. Besides, a hidden Markov model is introduced to describe the asynchronous phenomenon between the system and the FDF. Firstly, some sufficient criteria are established to ensure that the residual system is stochastically admissible (SA) with a certain $H_\infty$ performance. Secondly, solvability criteria are presented to codesign the desired FDF gains and the event-triggered matrices. Finally, the correctness of the proposed method is shown by two examples.

This paper investigates the tracking problem of event-triggered prescribed performance fuzzy fault-tolerant control (FTC) for unknown Euler-Lagrange systems with actuator faults and external disturbances. Firstly, the barrier Lyapunov functions (BLFs) and prescribed performance functions are synthesized to guarantee that the tracking errors satisfy the preset transient performance. Different from existing prescribed performance control methods, which require the initial values of the tracking errors to be within the prescribed performance functions, an error transformation method is introduced to ensure that the tracking errors with any bounded initial values can enter the preset boundaries within a preset time. Then, considering the unavailability of system parameters, the fuzzy logic systems (FLSs) are used to approximate unknown parameters of the system. What's more, to solve the problem of limited communication and computing resources in practical systems, an improved event-triggered control (ETC) scheme is proposed, which can reduce the communication and computation burden without satisfying the input-to-state stability (ISS) assumption. Meanwhile, the Zeno phenomenon can be avoided. Furthermore, the effects of actuator faults and the event-triggered mechanism are handled by Nussbaum gain technology. Finally, the superiority of the proposed control algorithm is verified by simulation results.

This article addresses the exponential synchronization problem for a class of fuzzy inertial memsirtor-based neural networks with mixed time-varying delays (FMINNs). Firstly, the inertial items are described as second-order systems and transformed into first-order systems by utilizing a appropriate variable substitution. Then the fuzzy state-feedback control strategy and fuzzy adaptive control strategy are designed to ensure the exponential synchronization under the framework of Filippov solutions. The exponential synchronization algebraic conditions are obtained by choosing a proper Lyapunov-Krasovskii functional (LKF). Finally, two numerical simulations are provided to validate the effectiveness and benefit of the proposed results.

This article investigates the neural-network-based (NN-based) set-membership filtering issue for nonlinear systems. In order to lighten the network transmission burden and avoid data collisions, the weighted try-once-discard (WTOD) protocol is employed to regulate the signal transmission process, which provides higher transmission priority to the most needed data. Considering the data discarding problem of the WTOD protocol, a novel event-triggered compensation mechanism is proposed to compensate the measurement output processed by the WTOD protocol, thereby improving the filtering performance. Next, considering the nonlinear dynamics of the system and the unknown-but-bounded (UBB) noise interference, an NN-based set-membership filter is designed to solve the state estimation problem. In a unified set-membership framework, an neural-network (NN) weight adaptive tuning law and a state estimation algorithm are designed. Sufficient conditions are derived for the existence of the adaptive NN parameters and the NN-based set-membership filter, and two optimization problems are put forward to seek the optimal NN parameters and filtering parameters that make the filter performance optimal. Finally, illustrative examples demonstrate the effectiveness of the proposed compensation mechanism and filtering algorithm.

To study on the thermoelectric power generation for industrial waste heat recovery applied in a hot-air blower, an experimental thermoelectric generator (TEG) bench with the hexagonal heat exchanger and commercially available Bi₂Te₃ thermoelectric modules (TEMs) was established, and its performance was analyzed. The influences of several important influencing factors such as heat exchanger material, inlet gas temperature, backpressure, coolant temperature, clamping pressure and external load current on the output power and voltage of the TEG were comparatively tested. Experimental results show that the heat exchanger material, inlet gas temperature, clamping pressure and hot gas backpressure significantly affect the temperature distribution of the hexagonal heat exchanger, the brass hexagonal heat exchanger with lower backpressure and coolant temperature using ice water mixture enhance the temperature difference of TEMs and the overall output performance of TEG. Furthermore, compared with the flat-plate heat exchanger, the designed hexagonal heat exchanger has obvious advantages in temperature uniformity and low backpressure. When the maximum inlet gas temperature is 360 °C, the maximum hot side temperature of TEMs is 269.2 °C, the maximum clamping pressure of TEMs is 360 kg/m², the generated maximum output power of TEG is approximately 11.5 W and the corresponding system efficiency is close to 1.0%. The meaningful results provide a good guide for the system optimization of low backpressure and temperature-uniform TEG, and especially demonstrate the promising potential of using brass hexagonal heat exchanger in the automotive exhaust heat recovery without degrading the original performance of internal combustion engine.