Multi-innovation Stochastic Gradient Algorithms for Input Nonlinear Time-Varying Systems Based on the Line Search Strategy

This paper develops a multi-innovation stochastic gradient (MISG) algorithm for multi-input, multi-output systems. The convergence analysis using the martingale convergence theorem shows that the parameter estimates by the MISG algorithm consistently converge to the true parameters under the persistent excitation condition. The MISG algorithm uses not only the current innovation but also the past innovation at each iteration and repeatedly utilizes the available input-output data, thus the parameter estimation accuracy can be improved. The simulation example confirms the theoretical results.

This paper discusses the parameter estimation problems of nonlinear output error autoregressive systems and presents a data filtering-based multi-innovation stochastic gradient algorithm for improving the parameter estimation accuracy of the stochastic gradient algorithm by combining the multi-innovation identification theory and the data filtering technique. The proposed algorithm is effective and can generate highly accurate parameter estimates compared with the multi-innovation stochastic gradient algorithm. The simulation results confirm this conclusion.

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Parameter identification algorithm for ship manoeuvrability and wave peak model based multi-innovation stochastic gradient algorithm use data filtering technique

SummaryIn this paper, by means of the adaptive filtering technique and the multi‐innovation identification theory, an adaptive filtering‐based multi‐innovation stochastic gradient identification algorithm is derived for Hammerstein nonlinear systems with colored noise. The new adaptive filtering configuration consists of a noise whitening filter and a parameter estimator. The simulation results show that the proposed algorithm has higher parameter estimation accuracies and faster convergence rates than the multi‐innovation stochastic gradient algorithm for the same innovation length. As the innovation length increases, the filtering‐based multi‐innovation stochastic gradient algorithm gives smaller parameter estimation errors than the recursive least squares algorithm.

MTN optimal control of MIMO non-affine nonlinear time-varying discrete systems for tracking only by output feedback

In this paper, we discuss the identification problem of multi-input output-error moving average systems. A filtering based multi-innovation stochastic gradient algorithm with forgetting factors is derived by using the filtering technique with the multi-innovation identification theory, and a multi-innovation extended stochastic gradient algorithm is given for comparison. The simulation results confirm that the proposed algorithms can generate highly accurate parameter estimates compared with the multi-innovation stochastic gradient algorithm.

This paper deals with the fault-tolerant control problem for a class of nonlinear stochastic time-varying system with actuator failures. Both ℋ 2 and ℋ ∞ performance requirements are taken into consideration. The proposed actuator failure model is quite general and cover several frequently seen actuator failure phenomena as special cases. The stochastic nonlinearities are quite general and could stand for several nonlinear systems. It is the purpose of this paper to find equilibrium strategies of a two-player Nash game, and meanwhile both ℋ 2 and ℋ ∞ performances are achieved via proposed state feedback control scheme which is characterized by the solution to a set of coupled matrix equations. The feedback gains can be solved recursively backward in k. A numerical computing algorithm is presented, and then a simulation example is given to illustrate the effectiveness and applicability of the proposed algorithm.

This paper aims to investigate the security problem of nonlinear cyber-physical systems (CPSs), which poses a challenge to handle compared with linear CPSs. A series of optimization problems for nonlinear CPSs under injection attack are constructed, which are based on a general model of the nonlinear systems with repetitive operation characteristics and a novel introduction of the key technical lemma. These optimization results are more general than the existing injection attack results and the requirements for attackers to obtain system information are relaxed. Also, the form of switching applied to the attack strategy possesses several advantages, including high stealthiness, lower cost, and more flexibility. Therefore, the new optimal injection attack strategies are expected to be more widespread and provide a basis for the design of defense strategies. The key to acquiring the designed optimal attack strategies is to adopt the linear input/output (I/O) data model for these systems via introducing an estimation term of the improved projection estimation method into the linear model. Finally, a networked GLUON-6L3 manipulator example validates the effectiveness of the proposed methods. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The main purpose of this paper is to study the cyber security of nonlinear cyber-physical systems from the perspective of attackers, which can help defenders fully understand the behavior of attackers. Most of the existing attack strategies aimed at linear systems or have a priori knowledge of the attacked systems. However, there are great limitations and difficulties in practical application. In this paper, the new attack strategies are proposed by combining system identification, iterative learning and control theory, which relaxes the requirement of the attacker’s ability. In detail, the attacker only needs to obtain the control input and output data of the attacked system and design the attack strategy according to it. Among them, the first-in, first-out queue storage method is applied to remove the requirements for the storage capability of the attacker. In practical applications, the attacker does not need to store the I/O data continuously, but only store the initial value and the data of two successive iterations. The mathematical analytic forms of two optimal attack strategies are given, and then their effectiveness are verified on a welding work of a networked six-axis manipulator. In the future, we will investigate the design of attack strategies for nonlinear systems with heterogeneous dynamics and multiple delays.

An identification approach for linear and nonlinear time-variant structural systems via harmonic wavelets

EP-based adaptive tracker with observer and fault estimator for nonlinear time-varying sampled-data systems against actuator failures

A relation between the output regulation and the observer design for nonlinear systems

Decentralized output feedback adaptive NN tracking control of interconnected nonlinear time-delay systems with prescribed performance

We study the problem of stability analysis for certain nonlinear systems. Our contributions are new tools to guarantee uniform global asymptotic stability (UGAS) of nonlinear time-varying (NLTV) systems. First, we provide new definitions of persistency of excitation (PE). In particular, we give a new definition of uniform /spl delta/-PE (uG-PE) which, though conceptually equivalent to the original one we introduced (1999), is mathematically less conservative. We also provide with some properties of /spl delta/-PE pairs and contribute with a result which establishes UGAS of NLTV systems under u/spl delta/-PE.

The optimal control of nonlinear time-varying systems, particularly when the mathematical model of the system is unavailable or inexact, is an interesting and difficult control problem. This paper outlines a methodology for the design of an intelligent controller to perform optimal control of a nonlinear system adaptively, using emerging technologies of fuzzy logic (FL) and artificial neural networks (ANN). FL is utilized to incorporate the available knowledge into the control system, and ANN technology is applied to adaptively provide an optimal control strategy based on some performance criteria. The technique is tested on a system that consists of a DC motor (a linear time-invariant (LTI) system) and a thermal system (a time-varying nonlinear system). Performance criteria such as tracking accuracy, cost, robustness, are considered, and the results are presented in this paper. >