This paper is concerned with the state estimation and sliding mode control problems for phase-type semi-Markovian jump systems. Using a supplementary variable technique and a plant transformation, a finite phase-type semi-Markov process has been transformed into a finite Markov chain, which is called its associated Markov chain. As a result, phase-type semi-Markovian jump systems can be equivalently expressed as its associated Markovian jump systems. A sliding surface is then constructed and a sliding mode controller is synthesized to ensure that the associated Markovian jump systems satisfy the reaching condition. Moreover, an observer-based sliding mode control problem is investigated. Sufficient conditions are established for the solvability of the desired observer. Two numerical examples are presented to show the effectiveness of the proposed design techniques.

In this work, a novel discrete-time fractional-order sliding mode control (SMC) scheme is proposed, which guarantees the desired tracking performance of a linear motor control system. By using Euler’s discretization method, a discrete-time model is firstly established for the linear motor, which includes the nonlinear friction and the uncertainties. Considering the practicability of the engineering application, a new discrete-time fractional-order sliding surface is constructed by taking the Grünwald–Letnikov definition based fractional-order difference of the tracking error into account. Compared to the classical integer-order sliding surface, by the proposed fractional-order sliding surface in this work, a better performance can be achieved due to the memory effect of the fractional calculus. To drive the system trajectories to the predefined sliding surface in finite sampling steps, a novel equivalent control is then designed, which can adjust the switching control input adaptively. Meanwhile, the theoretical analysis for the tracking error of the linear motor system is presented, and the practical reachability of the sliding surface is validated by numerical simulations. Finally, the effectiveness of the proposed control strategy is verified by a group of tracking experiments on a linear motor platform.

This paper is concerned with the generalized fault detection for two-dimensional (2-D) discrete-time Markovian jump systems. The mathematical model of the 2-D system is established upon the well-known Roesser model, and the measurement missing phenomenon, which appears typically in a network environment, is modeled by a stochastic variable satisfying the Bernoulli random binary distribution. In addition, the transition probabilities of the Markovian jump process are assumed to be partly accessed, that is, the transition probabilities are partly known. Our attention is focused on the design of a fault detection filter, or a residual generation system, which guarantees the fault detection system to be mean-square asymptotically stable and to have a prescribed generalized performance. Sufficient conditions for the existence of a desired fault detection filter are established in terms of linear matrix inequalities (LMIs). A numerical example is provided to illustrate the effectiveness of the proposed design method.

This paper is concerned with the problems of dissipativity analysis and synthesis for discrete-time Takagi-Sugeno fuzzy systems with stochastic perturbation and time-varying delay. First, a novel model transformation method is introduced to pull the time-varying delay uncertainty out of the original system. Consequently, the transformed model is composed of a linear time-invariant system and a norm-bounded uncertain subsystem. By using this model transformation method combined with the Lyapunov-Krasovskii technique, sufficient conditions of the dissipativity are established. Then, a fuzzy controller is designed to guarantee the dissipative performance of the closed-loop system. Finally, three examples are presented: one shows the effectiveness of model transformation method, the second performs the comparison with alternative approaches, and the third illustrates the applicability of the proposed dissipative control methods.

In this paper, a practical state estimation strategy against joint sensor and actuator attacks is proposed for cyber-physical systems (CPSs). The CPSs are formulated based on delta operator for the requirement of real control systems with high-sampling or variable-sampling periods. The attacks fulfilling sparse observation condition are supposed to be randomly injected into both sensor and actuator units. Various numbers of attacks on sensors and actuators can be properly handled. Based on multiple observations of the attacked measurements, a new estimator in delta-domain is proposed to estimate the states of the attacked CPS. The convergence of the corresponding estimation error is analyzed based on pole assignment analysis and some matrix equality constraints. Further, by utilization of a dissipativity approach, estimator-based time-driven controller and self-triggered controller are designed for the enhancement of the estimations and the resource saving. Simulations for the joint attack scenario are provided to testify the applicability and effectiveness of the proposed estimation methods.