Yan-Qiu Che

An improved robust ISS-satisficing control for synchronization of neurons

In the conventional robust ISS (input to state stable)-satisficing control strategy, all parameters of the system must be known and deterministic. This confines its application. So in this paper, the robust ISS-satisficing control strategy and the variable universe indirect fuzzy control strategy are combined to offset this weakness. The proposed control method has the inverse optimality of the robust ISS-satisficing control and the robust and predictive performance of the fuzzy control. Based on Lyapunov control method, the overall closed-loop system is shown to be stable. The new control strategy is used to realize the synchronization of Hodgkin-Huxley (HH) neurons. The simulation results confirm the feasibility and effectiveness of control algorithm.

Semi-global robust output regulation of minimum-phase nonlinear systems based on high-gain nonlinear internal model

We consider the assumption of existence of the general nonlinear internal model that is introduced in the design of robust output regulators for a class of minimum-phase nonlinear systems with rth degree (r ≥ 2). The robust output regulation problem can be converted into a robust stabilisation problem of an augmented system consisting of the given plant and a high-gain nonlinear internal model, perfectly reproducing the bounded including not only periodic but also nonperiodic exogenous signal from a nonlinear system, which satisfies some general immersion assumption. The state feedback controller is designed to guarantee the asymptotic convergence of system errors to zero manifold. Furthermore, the proposed scheme makes use of output feedback dynamic controller that only processes information from the regulated output error by using high-gain observer to robustly estimate the derivatives of the regulated output error. The stabilisation analysis of the resulting closed-loop systems leads to regional as well as semi-global robust output regulation achieved for some appointed initial condition in the state space, for all possible values of the uncertain parameter vector and the exogenous signal, ranging over an arbitrary compact set.

Introducing conditional integrator to sliding mode control of DC/DC buck converter

A sliding mode control introducing a conditional integrator is proposed for the robust output regulation of DC/DC buck converter. Based on the input-output linearization from the state-space averaged model of DC/DC Buck converter, the robust output regulation problem of the converter can be converted into a robust stabilization problem of an augmented system consisting of the given buck converter and the internal model by introducing a proper conditional integrator. The procedures include the design of an internal model with conditional integrator and a sliding mode robust controller. Also the closed-loop robust stability is theoretically analyzed. Finally, the effectiveness of the proposed converter is verified in comparing the proposed controller performance with open-loop control, single-loop proportional plus integral control and double loop proportional plus integral control under load disturbances and supply voltage variations.

Sensitivity of Neurons Exposed to AC Induction Electric Field

Neuronal coding is one of the characteristics that exhibit the response of the neuron to the external stimulus. Based on a simplified Hodgkin–Huxley model, this paper firstly establishes a new neuronal model under the effect of alternating current (AC) induction electric field, and investigates the mechanism of neuronal encoding and the sensitivity of firing rate to noises. According to the model established, this paper obtains the bifurcation point of the model with the membrane voltage–time curves Then by the data analysis of the mean firing rate-electric field frequency or the mean firing rate-amplitude curves, it obtains the relationship between the neuronal firing rate and noises as well as the electric field intensity, proving that the fluctuations of electric field frequency or amplitude can affect the sensitivity of neurons.

Topology Estimation of Uncertain General Complex Dynamical Networks from Noisy Time Series

This paper addresses the problem of simultaneous estimation of the topological structure and unknown parameters of uncertain general complex networks from noisy time series. Usually the complex networks consist of known node models with some unknown parameters and uncertain topological structure. At the same time, only partial states with heavy noise can be observed in real-world complex networks. By means of the unscented Kalman filter (UKF), we estimate the unknown states, parameters as well as topological structure with high accuracy only from partial heavily noise-corrupted states of the nodes. The simulation results verify the effectiveness of the proposed approach.

Chaos Synchronization of Coupled FitzHugh-Nagumo Neurons Via Adaptive Sliding Mode Control

In this paper, an adaptive neural network (NN) sliding mode controller is proposed to realize the chaos synchronization of two gap junction coupled FitzHugh-Nagumo (FHN) neurons under external electrical stimulation. The controller consists of two simple radial basis function (RBF) NNs which are used to approximate the desired sliding mode controller and the uncertain nonlinear part of the error dynamical system, respectively. The weights of these NNs are tuned on-line based on the sliding mode reaching law. According to the Lyapunov stability theory, the stability of the closed error system is guaranteed. The control scheme is robust to the uncertainties such as approximate error, ionic channel noise and external disturbances. Chaos synchronization are obtained by proper choice of the control parameters. The simulation results demonstrate the effectiveness of the proposed control method.

Chaos synchronization of coupled map-based neurons under external electrical stimulation via decoupling feedback linearization control

In this paper, synchronization dynamics of two map-based neurons being electrically coupled with gap junction in external electrical stimulation is studied. In order to realize the synchronization of all state variables in the neural system, we regard the nonlinear feedback system as a multi-input multi-output (MIMO) system. The feedback linearization control schemes are introduced to synchronize two coupled chaotic neurons. The simulations given in the paper demonstrate the effectiveness of the developed control method.

Synchronization of Ghostburster neurons using high order sliding mode control

In this paper, high order sliding mode control is proposed to realize the synchronization of two Ghostburster neurons under external electrical stimulation. Being a motion on a discontinuity set of a dynamic system, the sliding mode is used to keep accurately a given constraint and features theoretically-infinite-frequency switching. As the high order sliding mode technique employed in this paper, it considers a fractional power of the absolute value of the tracking error coupled with the sign function. This structure provides several advantages such as the simplification of the control law, higher accuracy and chattering prevention. Firstly we analyze the periodic and chaotic dynamics of individual Ghostburster neuron under different external electrical stimulus, then high order sliding mode controller is designed to synchronize two Ghostburster neurons and drive the slave neuron to act as the master one. Asymptotic synchronization of the system can be obtained by proper choice of the control parameters. Simulation results demonstrate the effectiveness of the proposed control method.

Chaos synchronization of coupled map-based neurons under external electrical stimulation via robust adaptive control

In this paper, a robust adaptive controller is proposed to realize the synchronization of two gap junction coupled chaotic map-based neurons under external electrical stimulation. Considering the uncertain states in practical neural systems, we propose a robust control scheme comprising an adaptive controller and an uncertainty estimator. Chaos synchronization is obtained by proper choice of the control parameters. Gaussian Random Noise is introduced to evaluate the stability of the adaptive control induced synchronized state. The simulation results demonstrate the effectiveness and robustness of the developed control method.

Robust output regulation of single-switch quadratic buck converter using internal model

An internal model control with conditional integrator is proposed for the robust output regulation of single-switch quadratic buck converters. The output regulation problem of a quadratic buck converter is converted to the stabilization of an augmented system consisting of the buck converter and the internal model. The design procedures include the design of an internal model with conditional integrator and a robust state feedback stabilizer with saturation behavior. The closed-loop stability of the converter will be theoretically analyzed. Finally, the effectiveness of the proposed control scheme for large load and supply variations are also tested. The design of the controller does not require the exact circuit parameters and the stability proof demonstrates the robustness against parameter uncertainties.