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This paper concerns the stability analysis for singular systems with time-varying delay and nonlinear perturbations. Two cases of time-varying delay, which is differentiable (Case 1) or not differentiable (Case 2), are considered. The considered nonlinear perturbations includes the norm-bounded uncertainties as a special case. Some delay-dependent stability criteria are derived by using a delay decomposition approach. In the delay decomposition approach, the entire delay interval is divided into multiple sub-intervals for which different energy functions are defined for building new Lyapunov–Krasovskii functional. Some numerical and practical examples are given to show the effectiveness and significant improvement of the proposed method. [ABSTRACT FROM PUBLISHER]

The problem of H∞ fault‐tolerant control for networked control systems (NCSs) with actuator failures is addressed in this study. The authors consider the network‐induced delays, including sensor‐to‐controller delay and controller‐to‐actuator delay, to have both an upper bound and a non‐zero lower bound. By using a sampled‐data approach, a new model is formulated based on the updating instants of the ZOH. They derive the criteria which ensure that the sample‐data NCS is stable with H∞ norm γ. The authors design the H∞ fault‐tolerant controller that guarantees the closed‐loop NCS stability with H∞ norm γ, when all control components are operational, as well as when some actuators fail to operate. Simulation examples are given to show the effectiveness and less conservative needs of the proposed method. [ABSTRACT FROM AUTHOR]

This paper is concerned with finite-time stability and dynamic output feedback stabilization for Itô type stochastic systems. First of all, it presents a sufficient pth moment finite-time stability criterion of nonlinear stochastic systems. When linear stochastic systems are considered, a finite-time stability condition in the sense of mean square is obtained by choosing a quadratic Lyapunov function. Then, a dynamic output feedback controller is designed to mean-square finite-time stabilize the linear stochastic systems under consideration. Furthermore, the proposed method is extended to finite-time dynamic output feedback control for a class of linear stochastic retarded systems. Numerical examples are finally provided to show the effectiveness of the approach. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a partially decoupled design of the state space predictive functional control for MIMO processes. The multivariable process is first treated into MISO process by a simple Cramer''s rule solution to linear equations which provides a balance between model complexity and control system design, and then the derived MISO process based extended state space predictive functional control is presented. The overall design of the controller enables the controller to consider both the process state dynamics and the output dynamics, thus improved control performance for tracking set-points and disturbance rejection is resulted. The proposed controller is tested on both model match and model mismatch cases to demonstrate its superiority. In addition, a closed-form of transfer function representation that facilitates frequency analysis of the control system is provided to give further insight into the proposed method. [Copyright &y& Elsevier]

In this paper, we consider the H filtering problem for linear systems using quantized measurements. The communication channel we consider consists of two cases: the ideal one and the unreliable one. For the ideal channel, we designed a filter to mitigate the quantization effects, which ensured not only the asymptotical stability but also a prescribed H filtering performance. For the unreliable channel, we introduced the stochastic variable satisfying Bernoulli random binary distribution to model the lossy measurements. We also designed a filter to cope with the losses and mitigate quantization effects simultaneously which ensured not only stochastic stability, but also a prescribed H filtering performance. Furthermore, we derive sufficient conditions for the existence of the above filters. Finally, a numerical example is given to illustrate that the proposed approach is effective and feasible. [ABSTRACT FROM AUTHOR]

Abstract: The level control of the fractionation tower in industrial coke unit is not very easy due to its complex characteristics and nonlinearity. Most are controlled by PID or linear predictive control methods without considering the complexity. This paper shows that a more comprehensive nonlinear-model-based predictive control method can further improve control performance. A verification of a nonlinear process model with plant data is first shown. Then the design of nonlinear predictive functional control is discussed and results are shown by way of simulations and application to demonstrate the effectiveness and feasibility of the proposed control strategy. [Copyright &y& Elsevier]

Abstract: This paper presents a new design method of model predictive control (MPC) based on extended non-minimal state space models, in which the measured input and output variables, their past values together with the defined output errors are chosen as the state variables. It shows that this approach does not need the design of an observer to access the state information any more and by augmenting the process model and its objective function to include the changes of the system state variables, the control performances are superior to those of the controller that does not bear this feature. Furthermore, closed-loop transfer function representation of the model predictive control system facilitates the use of frequency response analysis methods for the nominal control performances of the system. [Copyright &y& Elsevier]

Passivity analysis of stochastic neural networks with time-varying delays and parametric uncertainties is investigated in this paper. Passivity of stochastic neural networks is defined. Both delay-independent and delay-dependent stochastic passivity conditions are presented in terms of linear matrix inequalities (LMIs). The results are established by using the Lyapunov–Krasovskii functional method. In order to derive the delay-dependent passivity criterion, some free-weighting matrices are introduced. The effectiveness of the method is illustrated by numerical examples. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problem of robust H2 control for an uncertain continuous descriptor system based on a new bounded real lemma. Our purpose is to present sufficient and necessary conditions such that the uncertain descriptor system is regular, impulse free, and stable for all admissible uncertainties, while the H2 norm of the descriptor system is less than a given positive number γ. To this end, a new bounded real lemma for the robust H2 control problem is first derived in terms of a strict linear matrix inequality (LMI). Secondly, based on this bounded real lemma, a design method of state feedback control is developed to guarantee robust H2 performance. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: This paper deals with the problem of absolute stability for a class of time-delay singular systems with sector-bounded nonlinearity. Both delay-independent and delay-dependent criteria are presented and formulated in the form of linear matrix inequalities (LMIs). Neither model transformation nor a bounding technique for cross-terms, nor a slack variable method is involved in obtaining the stability criteria. Numerical examples are given to show the effectiveness and improvements over some existing results. [Copyright &y& Elsevier]

Abstract: The paper presents a new nonlinear predictive control design for a kind of nonlinear mechatronic drive systems, which leads to the improvement of regulatory capacity for both reference input tracking and load disturbance rejection. The nonlinear system is first treated into an equal linear time-variant system plus a nonlinear part using a neural network, then an iterative learning linear predictive controller is developed with a similar structure of PI optimal regulator and with setpoint feed forward control. Because the overall control law is a linear one, this design gives a direct and also effective multi-step prediction method and avoids the complicated nonlinear optimization. The control law is also an accurate one compared with traditional linearized method. Besides, changes of the system state variables are considered in the objective function with control performance superior to conventional state space predictive control designs which only consider the predicted output errors. The proposed method is compared with conventional state space predictive control method and classical PI optimal control method. Tracking performance, robustness and disturbance rejection are enlightened. [Copyright &y& Elsevier]

The DC microgrid is an important structure of microgrids. Aiming at the problem of the grid-connected DC microgrid modeling, a grid-connected DC microgrid equivalent modeling method based on the optimized Broad Learning System (BLS) is proposed. Taking the electrical parameter data of the grid-connected DC microgrid access point as the training data set of BLS, the equivalent model of the grid-connected equivalent model is constructed. In order to further improve the accuracy and generalization performance of the model, the shark smell optimization (SSO) algorithm is used to optimize the input weights and thresholds of the BLS. Furthermore, the shark smell optimization-Broad Learning System (SSO-BLS) algorithm is proposed. SSO-BLS is compared with RBF, BLS, BFO-ELM, and other algorithms. The results show that the grid-connected DC microgrid model based on SSO-BLS has good accuracy and generalization characteristics. [ABSTRACT FROM AUTHOR]

This brief is concerned with the robust D-stability problem for a class of complex singularly perturbed systems with state delay. According to singular system approach, sufficient conditions, which are tested easily, are obtained by introduce delay-dependent state feedback, to ensure that the two-time-scale systems are regular, impulse free and D-stable, that is, all its poles are located with a specified disk for all small positive parameter ε ∊ (0, ε*]. [ABSTRACT FROM AUTHOR]

This paper is concerned with controller design for uncertain stochastic systems with time-varying delays. The parametric uncertainties which appear in all system matrices are assumed to be norm bounded. Two cases of time-varying delays are investigated. Based on the slack matrix technique, delay-dependent stability criteria for the delayed stochastic systems are derived. By using the analysis results, linear matrix inequality-based controllers are designed. Three numerical examples are provided to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: For the bearings-only tracking (BOT) problem with signal propagation time delay, a recursive algorithm is proposed. Based on the analysis of signal time delay effect, an online parameter estimation (OPE) method is presented, which can be embedded in nonlinear filters to recursively estimate the target state with delayed measurements. The nonlinear filter used in this paper is a combination of the kernel-based filter (KBF) with an improved range parameterized (IRP) method, which manages subintervals according to their corresponding weights. The effectiveness and advantages of our proposed method compared with two existing methods are demonstrated by simulation examples. [Copyright &y& Elsevier]