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. A DOFC is designed to achieve the synchronization of the NNs. Stability criteria of the synchronisation error system are derived based on the Lyapunov-Krasovskii functional method, and the co-design of the DOFC and DETS parameters are accomplished using the Cone-complementarity linearization (CCL) approach. The effectiveness and advantages of the proposed method are illustrated considering an example of the chaotic system.

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. Second, a generalized bumpless transfer anti-disturbance specification is given to describe the switching quality at the switching points of switching Markovian LPV systems. Third, a solvability condition is established for the bumpless transfer H∞ anti-disturbance control problem, and a parameter-dependent multiple piecewise disturbance observer-based bumpless transfer controller is designed. Finally, an application example has been supplied to demonstrate the availability of the developed method.

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. Furthermore, a distributed online learning framework, namely, reinforce-critic-actor neural networks, is established in the implementation of the proposed approach, which is aimed at estimating the IRR function, the Q-function, and the optimal control scheme, respectively. The implemented procedure is designed in a data-driven way without needing knowledge of the system dynamics. Finally, simulations and comparison results with the classical method are given to demonstrate the effectiveness of the proposed tracking control method.

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. Based on a constructed Lyapunov-Krasovskii functional (LKF) with the distributed delay kernel and a generalized integral inequality, new sufficient conditions are formed by linear matrix inequalities (LMIs) for designing an event-triggered H∞ controller. Finally, experiments based on a computer and a real wireless network are executed to confirm the validity of the developed method.

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. With the aid of graph theory and neural network technique, a decentralized adaptive neuro-output feedback saturated controller is designed in a nonrecursive manner. A sufficient criterion is established to achieve the uniform ultimate boundedness (UUB) of the closed-loop system. An application example of autonomous underwater vehicle (AUV) is provided to verify the effectiveness of the developed algorithm.

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. As special cases, the asymptotical synchronization of the considered model via sampled-data control and saturating impulsive control are also studied, respectively. Numerical simulations are presented to claim the effectiveness of theoretical analysis. A new image encryption algorithm is proposed to utilize the synchronization theory of hybrid control. The validity of image encryption algorithm can be obtained by experiments.

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. It is noted that the novel LKF and delay-dependent reciprocally convex inequality plays an important role in dealing with global asymptotical stability and strict (Q, S,R) - α -dissipativity of the T-S fuzzy delayed model. Through the constructed LKF, a new set of less conservative linear matrix inequality (LMI) conditions is presented to obtain an observer-based controller for the T-S fuzzy delayed model. This proposed observer-based controller ensures that the state of the closed-loop system is globally asymptotically stable and strictly (Q, S,R) - α -dissipative. Finally, the effectiveness of the proposed results is shown in numerical simulations.

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. Based on sector nonlinearity model approach, impulsive controls with actuator saturation and with partial actuator saturation are studied, respectively, and some effective sufficient conditions are obtained. Numerical simulation is presented to verify the validity of the theoretical analysis results. Finally, the impulsive synchronization is applied to image encryption. The experimental results show that the proposed image encryption system has high security properties.

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. Using the Bessel-Legendre inequalities, sufficient conditions for the existence of a controller that ensures asymptotic synchronization are provided in the form of linear matrix inequalities (LMIs). Illustrative examples are used to demonstrate the advantages of the proposed IETS method over other event-triggered scheme (ETS) methods. Moreover, this IETS method is applied to the image encryption and decryption. A novel encryption algorithm is proposed to enhance the quality of the encryption process.

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. Based on these inequalities, we design the Lyapunov-Krasovskii functional and derive hierarchical linear matrix inequality stability conditions. Finally, three numerical examples are provided to demonstrate that the proposed method is less conservative with a reasonable numerical burden than the existing results.

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. Then, by feat of the random analysis method and matrix technique, sufficient conditions are given to guarantee the noise-to-state stability in mean and globally asymptotic stability in probability, respectively. At last, two simulation examples are exploited in order to verify the validity of the proposed theory.

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. In order to get rid of the rigorous constraint that the derivatives of time-varying delays must be less than one, a new approach is introduced by improving Lyapunov-Krasovskii functional rather than using the traditional free-weighting matrices. Finally, numerical examples are presented to demonstrate the effectiveness of the theoretical results.

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. The membership functions of the plant and the controller are approximated as polynomial functions, then incorporated into the controller design. Sufficient conditions for the existence of the controller are derived in terms of sum of square inequalities, which are then solved by YALMIP. Finally, a numerical example is used to demonstrate the validity of the proposed methodology.

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. A desired filter is constructed by solving a convex optimization problem, which can be efficiently solved by standard numerical algorithms. Finally, simulation examples are provided to illustrate the effectiveness of the proposed approaches.

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). It was suggested from the optimization results that the body's weight has an important influence on the vaulting performance beside the vaulter's height and approach speed; the less skilled vaulter should gradually adopt a longer pole to improve the performance.

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. This single-bit representation offers significant advantages over multibit representations since they mitigate the fan-in and fan-out issues which are typical to distributed systems. To process these bit streams using ANNs concepts, functional elements which perform summing, scaling, and squashing have been implemented. These elements are modular and have been designed such that they can be easily interconnected. Two new architectures which act as monotonically increasing differentiable nonlinear squashing functions have also been presented. Using these functional elements, a multilayer perceptron (MLP) can be easily constructed. Two examples successfully demonstrate the use of bit streams in the implementation of ANNs. Since every functional element is individually instantiated, the implementation is genuinely parallel. The results clearly show that this bit-stream technique is viable for the hardware implementation of a variety of distributed systems and for ANNs in particular.

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. 2422, Dec. 2004. Note that Theorems 3.1 and 3.2 are correct. The errors in the proofs have been fixed.

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. An iterative algorithm based on the linear matrix inequality is developed to compute robust H infinity static output feedback gains. To reduce the conservatism of the design, the structural information of membership function characteristics is incorporated. A numerical example is used to illustrate the validity of the design methodologies.

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. The proposed approach does not involve the separation of states into slow and fast ones and it can be applied not only to standard, but also to nonstandard singularly perturbed non-linear systems. A numerical example is provided to illustrate the design developed in this paper.