This paper deals with the H∞control problem based on event-triggered protocol (ETP) for nonlinear hybrid stochastic delayed systems (NHSDSs) subject to denial of service (DoS) attacks. Taking into account the impact of DoS attacks, the main purpose of this paper is to propose an ETP-basedsed H∞control strategy for NHSDSs. The criteria to ensure that the controlled NHSDSs have a unique solution and meet the predetermined H∞performance are derived. The occurrence of the Zeno phenomenon in the ETP is excluded. Finally, the validity of the theoretical findings is verified by the mass-spring-damping model.

This paper presents a design method for converting serial-input feedback shift registers (FSRs) into parallel circuits using the Semi-Tensor Product (STP) technique. First, the algebraic expression of the serial-input FSR is derived via STP. Then, based on the parallelization level of the input sequence, the algebraic expression for the parallel-input conversion circuit is derived from the serial-input FSR expression, along with a sufficient condition to ensure that the outputs of both circuits are equal. Furthermore, by extracting the logical expression from the algebraic form of the parallelization circuit, a Boolean function bi-decomposition algorithm based on STP is applied to facilitate efficient hardware implementation on a field-programmable gate array (FPGA). Numerical experiments, including tests on a nonlinear serial-input FSR and cyclic redundancy check (CRC) validation, demonstrate the effectiveness and feasibility of the method in terms of latency reduction.

This paper develops a self-learning sliding mode control (SMC) scheme for nonholonomic mobile robots (NMRs) trajectory tracking under uncertainties such as noise and disturbances. Firstly, a neural network-based observer is provided to handle noise, and an integral SMC is presented to suppress noise. Subsequently, an optimal H∞control based on an event-triggered strategy is developed with the aim to further reduce communication consumption. Finally, Lyapunov theory is employed to ensure the system stability, and simulation results further validate the approach for practical control on NMRs.

A new class of kernels has been designed to enhance the usability of prior knowledge. Prior knowledge is shown to improve the generalization ability of kernel algorithms for binary classification problems. The prior knowledge is expressed in natural language via fuzzy rules. First, the concepts of a fuzzy rule base and prior-confidence region are proposed to formulate the prior knowledge. Then, the new kernels, which are referred to as natural classification kernels (NCKs), are represented by fuzzy equivalence relations based on the formulation of prior knowledge. An NCK is interpreted as a measure of similarities between samples. It is proven that NCKs have two desired properties: 1) transitivity with respect to the triangular norms and 2) the ability to provide higher similarities to spatially closer samples from the same class. Using transitivity, a large number of NCKs may be directly obtained by means of triangular norms. Additionally, the theoretical results show that the second property makes it possible for the support vector machine (SVM) and convex hull separation algorithm to generalize from training samples to test samples in the prior-confidence region. Finally, some synthetic datasets and a benchmark dataset are employed to validate the proposed approach.