Event-Triggered Output-Feedback Control for Synchronization of Delayed Neural Networks.

This article proposes a novel discrete event-triggered scheme (DETS) for the synchronization of delayed neural networks (NNs) using the dynamic output-feedback controller (DOFC). The proposed DETS uses both the current and past samples to determine the next trigger, unlike the traditional event-triggered scheme (ETS) that uses only the current sample. The proposed DETS is employed in a dual setup for two network channels to significantly reduce redundant data transmission.

Memory-Event-Triggered H∞ Output Control of Neural Networks With Mixed Delays.

This article investigates the problem of memory-event-triggered H∞ output feedback control for neural networks with mixed delays (discrete and distributed delays). The probability density of the communication delay among neurons is modeled as the kernel of the distributed delay. To reduce network communication burden, a novel memory-event-triggered scheme (METS) using the historical system output is introduced to choose which data should be sent to the controller.

Optimal Tracking Control of Nonlinear Multiagent Systems Using Internal Reinforce Q-Learning.

In this article, a novel reinforcement learning (RL) method is developed to solve the optimal tracking control problem of unknown nonlinear multiagent systems (MASs). Different from the representative RL-based optimal control algorithms, an internal reinforce Q-learning (IrQ-L) method is proposed, in which an internal reinforce reward (IRR) function is introduced for each agent to improve its capability of receiving more long-term information from the local environment. In the IrQL designs, a Q-function is defined on the basis of IRR function and an iterative IrQL algorithm is developed to learn optimally distributed control scheme, followed by the rigorous convergence and stability analysis.

Decentralized Adaptive Neuro-Output Feedback Saturated Control for INS and Its Application to AUV.

This article investigates the problem of the decentralized adaptive output feedback saturated control problem for interconnected nonlinear systems with strong interconnections. A decentralized linear observer is first established to estimate the unknown states. Then, an auxiliary system is constructed to offset the effect of input saturation.

Bumpless Transfer H∞ Anti-Disturbance Control of Switching Markovian LPV Systems Under the Hybrid Switching.

This article focuses on the bumpless transfer H anti-disturbance control problem for switching Markovian LPV systems under a hybrid switching law. A parameter-dependent multiple piecewise disturbance observer-based bumpless transfer control strategy is put forward to reject multiple disturbances and reduce switching bumps. First, a hybrid switching law making full use of determinacy and randomness is proposed to improve the bumpless transfer anti-disturbance level by introducing a fixed dwell time in random switching.

Impulsive synchronization of coupled delayed neural networks with actuator saturation and its application to image encryption.

The actuator of any physical control systems is constrained by amplitude and energy, which causes the control systems to be inevitably affected by actuator saturation. In this paper, impulsive synchronization of coupled delayed neural networks with actuator saturation is presented. A new controller is designed to introduce actuator saturation term into impulsive controller.

Impulsive Synchronization of Unbounded Delayed Inertial Neural Networks With Actuator Saturation and Sampled-Data Control and its Application to Image Encryption.

The article considers the impulsive synchronization for inertial neural networks with unbounded delay and actuator saturation via sampled-data control. Based on an impulsive differential inequality, the difficulties caused by unbounded delay and impulsive effect may be effectively avoid. By applying polytopic representation technique, the actuator saturation term is first considered into the design of impulsive controller, and less conservative linear matrix inequality (LMI) criteria that guarantee asymptotical synchronization for the considered model via hybrid control are given.

Observer-Based Dissipativity Control for T-S Fuzzy Neural Networks With Distributed Time-Varying Delays.

An observer-based dissipativity control for Takagi-Sugeno (T-S) fuzzy neural networks with distributed time-varying delays is studied in this article. First, the network channel delays are modeled as a distributed delay with its kernel. To make full use of kernels of the distributed delay, a Lyapunov-Krasovskii functional (LKF) is established with the kernel of the distributed delay.

Synchronization of Delayed Neural Networks via Integral-Based Event-Triggered Scheme.

This article investigates the event-triggered synchronization of delayed neural networks (NNs). A novel integral-based event-triggered scheme (IETS) is proposed where the integral of the system states, and past triggered data over a period of time are used. With the proposed IETS, the integral event-triggered synchronization problem becomes a distributed delay problem.

Stability Analysis of Genetic Regulatory Networks With General Random Disturbances.

This paper establishes the stability criteria for genetic regulatory networks with random disturbances. We assume the nonlinear feedback regulation function to satisfy the sector-like condition and the random perturbation to have a finite second-order moment. First, under the globally Lipschitz condition, the existence and uniqueness of solution to random genetic regulatory networks are considered by exploiting an iterative approximation method.

Hierarchical Stability Conditions for a Class of Generalized Neural Networks With Multiple Discrete and Distributed Delays.

This brief investigates the analysis issue for global asymptotic stability of a class of generalized neural networks with multiple discrete and distributed delays. To tackle delays arising in different neuron activation functions, we employ a generalized model with multiple discrete and distributed delays which covers various existing neural networks. We then generalize the Bessel-Legendre inequalities to deal with integral terms with any linearly independent functions and nonlinear function of states.

Stability of a Class of Multiagent Tracking Systems With Unstable Subsystems.

In this paper, we predeploy a large number of smart agents to monitor an area of interest. This area could be divided into many Voronoi cells by using the knowledge of Voronoi diagram and every Voronoi site agent is responsible for monitoring and tracking the target in its cell. Then, a cooperative relay tracking strategy is proposed such that during the tracking process, when a target enters a new Voronoi cell, this event triggers the switching of both tracking agents and communication topology.

Exponential convergence analysis of uncertain genetic regulatory networks with time-varying delays.

This study is concerned with the problem of exponential convergence of uncertain genetic regulatory networks with time-varying delays in the case of the unknown equilibrium point. The system׳s uncertainties are modeled as a structured linear fractional form. Novel stability criteria are obtained by using the lower bound lemma together with Jensen inequality lemma.

SOS based robust H(∞) fuzzy dynamic output feedback control of nonlinear networked control systems.

In this paper, a methodology for designing a fuzzy dynamic output feedback controller for discrete-time nonlinear networked control systems is presented where the nonlinear plant is modelled by a Takagi-Sugeno fuzzy model and the network-induced delays by a finite state Markov process. The transition probability matrix for the Markov process is allowed to be partially known, providing a more practical consideration of the real world. Furthermore, the fuzzy controller's membership functions and premise variables are not assumed to be the same as the plant's membership functions and premise variables, that is, the proposed approach can handle the case, when the premise of the plant are not measurable or delayed.

Induced l₂ filtering of fuzzy stochastic systems with time-varying delays.

This paper is concerned with the problem of induced l2 filter design for a class of discrete-time Takagi-Sugeno fuzzy Itô stochastic systems with time-varying delays. Attention is focused on the design of the desired filter to guarantee an induced l2 performance for the filtering error system. A new comparison model is proposed by employing a new approximation for the time-varying delay state, and then, sufficient conditions for the obtained filtering error system are derived by this comparison model.

Pole vault performance for anthropometric variability via a dynamical optimal control model.

Optimal performance of a dynamical pole vault process was modeled as a constrained nonlinear optimization problem. That is, given a vaulter's anthropomorphic data and approach speed, the vaulter chose a specific take-off angle, pole stiffness and gripping height in order to yield the greatest jumping height compromised by feasible bar-crossing velocities. The optimization problem was solved by nesting a technique of searching an input-to-output mapping arising from the vaulting trajectory and a method of nonlinear sequential quadratic programming (SQP).

Comments on "Fuzzy H(infinity) tracking control for nonlinear networked control systems in T-S fuzzy model".

The above paper gives a sufficient condition for the existence of a Takagi-Sugeno (T-S) fuzzy H (infinity) tracking controller for a class of nonlinear networked control systems. The aim of this paper is to show that if there exists a T-S fuzzy H (infinity) tracking controller, then there exists a linear H (infinity) tracking controller that guarantees the same prescribed H (infinity) tracking performance.

Neural network implementation using bit streams.

A new method for the parallel hardware implementation of artificial neural networks (ANNs) using digital techniques is presented. Signals are represented using uniformly weighted single-bit streams. Techniques for generating bit streams from analog or multibit inputs are also presented.

Response to comments on "Hinfinity fuzzy control design for nonlinear singularly perturbed systems with pole placement constraints: an LMI approach".

This note responds to the comments published by Ni Zhao and Fu-Chun Sun in IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS-PART B, vol. 34, no. 6, p.

Robust H infinity static output feedback control of fuzzy systems: an ILMI approach.

This paper examines the problem of robust H infinity static output feedback control of a Takagi-Sugeno fuzzy system. The proposed robust H infinity static output feedback controller guarantees the pounds 2 gain of the mapping from the exogenous disturbances to the regulated output to be less than or equal to a prescribed level. The existence of a robust H infinity static output feedback control is given in terms of the solvability of bilinear matrix inequalities.

Hinfinity fuzzy control design for nonlinear singularly perturbed systems with pole placement constraints: an LMI approach.

This paper considers the problem of designing an H infinity fuzzy controller with pole placement constraints for a class of nonlinear singularly perturbed systems. Based on a linear matrix inequality (LMI) approach, we develop an H infinity fuzzy controller that guarantees 1) the L2-gain of the mapping from the exogenous input noise to the regulated output to be less than some prescribed value, and 2) the closed-loop poles of each local system to be within a pre-specified LMI stability region. In order to alleviate the ill-conditioned LMIs resulting from the interaction of slow and fast dynamic modes, solutions to the problem are given in terms of linear matrix inequalities which are independent of the singular perturbation, epsilon.

Soft sensors for on-line biomass measurements.

One of the difficulties encountered in control and optimisation of bioprocesses is the lack of reliable on-line sensors for their key state variables. This paper investigates the suitability of using on-line recurrent neural networks to predict biomass concentrations. Input variables of the proposed recurrent neural network are feed rate, liquid volume and dissolved oxygen.

Effect of renal perfusion pressure on responses of intrarenal blood flow to renal nerve stimulation in rabbits.

1. We investigated how sympathetic nerve activity and renal perfusion pressure (RPP) interact in controlling renal haemodynamics in pentobarbitone-anaesthetized rabbits. 2.

On-line identification and optimization of feed rate profiles for high productivity fed-batch culture of hybridoma cells using genetic algorithms.

In this paper, an on-line identification and optimization method based on genetic algorithms (GAs) has been used to optimize the productivity of a seventh-order nonlinear model of fed-batch culture of hybridoma cells. The parameters of the seventh-order nonlinear model are assumed to be unknown. The intention of this paper is to use GAs for (1) identifying the parameters of a seventh-order nonlinear model of fed-batch culture of hybridoma cells, and (2) determining the best feed rate control profiles for glucose and glutamine.

Dynamic baroreflex control of blood pressure: influence of the heart vs. peripheral resistance.

The aim in the present experiments was to assess the dynamic baroreflex control of blood pressure, to develop an accurate mathematical model that represented this relationship, and to assess the role of dynamic changes in heart rate and stroke volume in giving rise to components of this response. Patterned electrical stimulation [pseudo-random binary sequence (PRBS)] was applied to the aortic depressor nerve (ADN) to produce changes in blood pressure under open-loop conditions in anesthetized rabbits. The stimulus provided constant power over the frequency range 0-0.