This paper develops a new fuzzy dynamic outputfeedback control scheme for Takagi-Sugeno (T-S) fuzzy systems with time-varying input delay and output constraints based on (Q, S, R)-α-dissipativity. The proposed controller, called a (Q, S, R)-α-dissipative output-feedback fuzzy controller, takes into consideration the abstract energy, storage function, and supply rate for the disturbance attenuation and provides a unified framework that can incorporate existing results for H∞ and passivity controllers as special cases for T-S fuzzy systems with time-varying input delay and output constraints. A dynamic parallel distributed compensator is used to design the (Q, S, R)-α-dissipative output-feedback fuzzy controller to ensure the asymptotic stability and strict (Q, S, R)-α-dissipativity of closed-loop systems described by a T-S fuzzy model that satisfies some output constraints. By employing the reciprocally convex approach, a new set of delay-dependent conditions for the desired controller is formulated in terms of the linear matrix inequality. The effectiveness and the applicability of the proposed design techniques are validated by an example of control for active suspension systems for different road conditions.

This paper studies delay-dependent exponential dissipative and l ₂ -l _∞ filtering problems for discrete-time switched neural networks (DSNNs) including time-delayed states. By introducing a novel discrete-time inequality, which is a discrete-time version of the continuous-time Wirtinger-type inequality, we establish new sets of linear matrix inequality (LMI) criteria such that discrete-time filtering error systems are exponentially stable with guaranteed performances in the exponential dissipative and l ₂ -l _∞ senses. The design of the desired exponential dissipative and l ₂ -l _∞ filters for DSNNs can be achieved by solving the proposed sets of LMI conditions. Via numerical simulation results, we show the validity of the desired discrete-time filter design approach.

The state of charge (SoC) estimation is essential for many battery-related applications, such as electric vehicles, unmanned aerial vehicles, and uninterruptible power supplies. This article presents a novel deep neural network for the SoC estimation on the time–frequency domain. Contrary to previous studies operating only in the time domain or extracting features using a 1-D convolutional neural network (CNN), the proposed model extracts high-level information features for more accurate SoC estimation through 2-D time–frequency domain spectrogram analysis using CNN. The spectrogram helped improve the model's generalization performance through the SpecAugment technique. The proposed model aggregates intermediate features and captures long-term hierarchical context information by introducing modified atrous spatial pyramid pooling. In addition, by introducing CNN with depthwise separable operations, the proposed model improves the estimation error score and reduces the computational cost compared with existing competing models. Experimental results indicate that the proposed approach outperforms the previous baseline methods and achieves remarkable performance in SoC estimation.