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Summary: Joint estimation of states and time‐varying parameters of linear stochastic systems is of practical importance for fault diagnosis and fault tolerant control. The known fact is that measurements have outliers. They can significantly degrade the properties of linearly recursive algorithms, which are designed to work in presence of Gaussian noises. This article proposes two kinds of strategies for joint parameter‐state robust estimation of linear stochastic models in presence of all possible faults and non‐Gaussian noises. In the form of Theorem, joint robust algorithm for systems with sensor and component faults, as well as the algorithm for systems with parameter faults are proposed. Because of their good features in robust filtering, Masreliez‐Martin filter represents a cornerstone for realization of the proposed robust algorithms for joint state‐parameter estimation. The good features of proposed robust estimation algorithms, in relation to algorithms based on other widely‐used filters, are illustrated by simulation results. On the other side, intensive research in the field of mathematical modeling of pneumatic servo drives has shown that their mathematical models are nonlinear in which a lot of important details cannot be included in the model. Also, it has been well known that the nonlinear model can be approximated by a linear model with time‐varying parameters. Due to the abovementioned reasons, it can be assumed that the pneumatic cylinder model is a linear stochastic model with variable parameters. The good practical values of the proposed robust joint algorithm to identification of the pneumatic cylinder are illustrated by experimental results. [ABSTRACT FROM AUTHOR]

In this paper, an online adaptive optimal control problem of a class of continuous-time Markov jump linear systems (MJLSs) is investigated by using a parallel reinforcement learning (RL) algorithm with completely unknown dynamics. Before collecting and learning the subsystems information of states and inputs, the exploration noise is firstly added to describe the actual control input. Then, a novel parallel RL algorithm is used to parallelly compute the corresponding N coupled algebraic Riccati equations by online learning. By this algorithm, we will not need to know the dynamic information of the MJLSs. The convergence of the proposed algorithm is also proved. Finally, the effectiveness and applicability of this novel algorithm is illustrated by two simulation examples. [ABSTRACT FROM AUTHOR]

This paper studies the adaptive fault estimation problems for stochastic Markovian jump systems (MJSs) with time delays. With the aid of the selected Lyapunov-Krasovskii functional, the adaptive fault estimation algorithm based on adaptive observer is proposed to enhance the rapidity and accuracy performance of fault estimation. A sufficient condition on the existence of adaptive observer is presented and proved by means of linear matrix inequalities techniques. The presented results are extended to multiple time-delayed MJSs. Simulation results illustrate that the validity of the proposed adaptive faults estimation algorithms. [ABSTRACT FROM AUTHOR]

Abstract: This paper studied the proportional-integral (PI) control problems of stochastic Markovian jump systems (MJSs) with uncertain parameters. Under complete access to the system states, the PI controller design procedure turns to static output feedback control problem that make the closed-loop dynamics of this class of uncertain MJSs be robustly stochastically stable. A sufficient condition on the existence of PI controller is presented and proved by means of linear matrix inequality techniques. The presented results are extended to the case when the system states are not accessible. In order to make the relative equations approximate with a satisfactory precision, we described the problem as a semidefinite programming one via disciplined convex optimization. Simulation results illustrate the validity of the proposed algorithms. [Copyright &y& Elsevier]

This paper aims to solve the sliding mode control issue for the discrete nonlinear two-dimensional (2D) Fornasini–Marchesini second (FMII) model under the influence of quantisation error and stochastic packet loss. Firstly, an innovative stochastic 2D FMII sliding mode control model is constructed by considering quantisation error and Bernoulli packet loss process. And then we study the stability issue and formulate the corresponding stability criterion by virtue of the 2D sliding mode surface and a 2D sliding mode control law with data compensation. Subsequently, the reachability for the 2D sliding surface is verified by a innovative and executable 2D sliding mode control law. Besides, the article also formulates the adaptive intelligent iterative algorithms for the sliding mode surface gain and 2D sliding mode control algorithm, respectively. To conclude the paper, an example is provided to analyse its SMC problem under quantisation and stochastic packet loss. This example also shows the effectiveness of the theorems and algorithms proposed in this paper. [ABSTRACT FROM AUTHOR]

An online adaptive optimal control problem for a class of nonlinear Markov jump systems (MJSs) is studied. It is worth noting that the dynamic information of MJSs is partially unknown. Applying the neural network linear differential inclusion techniques, the nonlinear terms in MJSs are approximately converted to linear forms. By using subsystem transformation schemes, we can transfer the nonlinear MJSs to N new coupled linear subsystems. Then a new online policy iteration algorithm is put forward to obtain the adaptive optimal controller. Some theorems are given afterward to ensure the convergence of the new algorithm. At last, a simulation example is provided to verify the applicability of the algorithm. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problem of finite-time H∞ resilient filtering for a class of switch systems. The filtering error dynamics is constructed based on the H∞ resilient filter. The objective is to design a filter such that the finite-time H∞ gain from the unknown input to an estimation error is minimized or guaranteed to be less than or equal to a prescribed value. By selecting the proper multiple Lyapunov function and using the average dwell-time approach, sufficient conditions are obtained for the existence of the desired H∞ resilient filter, which also guarantee the finite-time boundedness of the filtering error dynamic systems. The design criteria are proposed in the form of linear matrix inequalities and then described as an optimization algorithm. Finally, a numerical example is employed to illustrate the effectiveness of the developed techniques. [ABSTRACT FROM AUTHOR]

• A novel online mode-free integral reinforcement learning algorithm is proposed to solve the mutiplayer non-zero sum games. • The online learning is used to compute the corresponding N coupled algebraic Riccati equations. • The policy iterative algorithm is applied to solve the coupled algebraic Riccati equations corresponding to the multiplayer nonzero sum games. In this paper, a novel online mode-free integral reinforcement learning algorithm is proposed to solve the multiplayer non-zero sum games. We first collect and learn the subsystems information of states and inputs; then we use the online learning to compute the corresponding N coupled algebraic Riccati equations. The policy iterative algorithm proposed in this paper can solve the coupled algebraic Riccati equations corresponding to the multiplayer non-zero sum games. Finally, the effectiveness and feasibility of the design method of this paper is proved by simulation example with three players. [ABSTRACT FROM AUTHOR]