Exponential synchronization control of delayed memristive neural network based on canonical Bessel-Legendre inequality

<abstract><p>In this paper, we study the exponential synchronization problem of a class of delayed memristive neural networks(MNNs). Firstly, a intermittent control scheme is designed to solve the parameter mismatch problem of MNNs. A discontinuous controller with two tunable scalars is designed, and the upper limit of control gain can be adjusted flexibly. Secondly, an augmented Lyaponov-Krasovskii functional(LKF) is proposed, and vector information of N-order canonical Bessel-Legendre(B-L) inequalities is introduced. LKF method is used to obtain the stability criterion to ensure exponential synchronization of the system. The conservatism of the result decreases with the increase of the order of the B-L inequality. Finally, the effectiveness of the main results is verified by two simulation examples.</p></abstract>

Two-Phase Short-Term Frequency Response Scheme of a DFIG-Based Wind Farm

The kinetic energy stored in the doubly-fed induction generators (DFIG)-based wind farm can be utilized to sustain the dynamic system frequency. However, difficulties arise in determining the control gain to effectively improve the frequency nadir and smoothly return to the maximum power point tracking (MPPT) operation. This paper addresses a two-phase short-term frequency response (STFR) scheme to boost the frequency nadir and minimize the second drop in the system frequency based on a piecewise control gain. To achieve the first goal, a constant control coefficient, which is determined according to the initial operating conditions of the DFIG-based wind farm, is employed until the frequency nadir produces. To achieve the second goal, the control coefficient, which changes with time, facilitates to smoothly return to the MPPT operation. The effectiveness of the proposed two-phase STFR scheme is verified under various wind power penetration levels, wind speeds, and disturbances. The results reveal that the frequency nadir is improved, and simultaneously, it smoothly returns to the MPPT operation and minimizes the second drop in the system frequency.

Synchronization of chaotic delayed systems via intermittent control and its adaptive strategy

In this paper the problem of synchronization for delayed chaotic systems is considered based on aperiodic intermittent control. First, delayed chaotic systems are proposed via aperiodic adaptive intermittent control. Next, to cut down the control gain, a new generalized intermittent control and its adaptive strategy is introduced. Then, by constructing a piecewise Lyapunov auxiliary function and making use of piecewise analysis technique, some effective and novel criteria are obtained to ensure the global synchronization of delayed chaotic systems by means of the designed control protocols. At the end, two examples with numerical simulations are provided to verify the effectiveness of the theoretical results proposed scheme.

Robust Stabilization of T-S Fuzzy Systems via Improved Integral Inequality

This paper focuses on the state feedback control for uncertain nonlinear model, which can be denoted by Takagi - Sugeno (T-S) fuzzy model. We derive an improved integral inequality as a rearrangement of quadratic matrix-vector form combined with Jensen's inequality. By using this improved inequality, the sufficient conditions guaranteeing the stability of the resulting T-S fuzzy model have been proposed in terms of linear matrix inequalities. With respect to these stability conditions, the stabilization criterion is given for the T-S fuzzy systems with the prescribed control gain matrices. Finally, to check the feasibility and less conservatism of the derived results, numerical examples are given including the physical model such as continuous stirred tank reactor ( CSTR ) model supported by numerical simulations.

Accommodation of Multi-Shaft Gas Turbine Switching Control Gain Tuning Problem to IGV Position

This article aims to discuss the influence of compressor Inlet Guide Vane (IGV) position on gas turbine switching control system gain tuning problem. The distinction between IGV and normally reckoned working conditions is differentiated, and an improved double-layer LPV model is proposed to estimate the protected parameters under various IGV positions. Controller gain tuning is conducted with single and multi-objective intellectual optimization algorithms. Simulation results reveal that normally used multi-objective optimization procedure is unnecessary and time-consuming. While with the comprehensive indicator introduced in this paper, the calculation burden can be greatly eased. This improvement is especially advantageous when tuning work is carried out under multiple IGV positions.

An Improved Robust Adaptive Controller for a Fed-Batch Bioreactor with Input Saturation and Unknown Varying Control Gain via Dead-Zone Quadratic Forms

In this work, a new adaptive controller is designed for substrate control of a fed-batch bioreactor in the presence of input saturation and unknown varying control gain with unknown upper and lower bounds. The output measurement noise and the unknown varying nature of reaction rate and biomass concentration and water volume are also handled. The design is based on dead zone quadratic forms. The designed controller ensures the convergence of the modified tracking error and the boundedness of the updated parameters. As the first distinctive feature, a new robust adaptive auxiliary system is proposed in order to tackle input saturation and control gain uncertainty. As the second distinctive feature, the modified tracking error converges to a compact region whose bound is user-defined, in contrast to related studies where the convergence region depends on upper bounds of either external disturbances, system states, model parameters or terms and model parameter values. Simulations confirm the properties of the closed loop behavior.

Control of a Variable Blade Pitch Wind Turbine Subject to Gust Wind and Actuators Saturation

This paper examines the dynamics and control of a variable blade pitch wind turbine during extreme gust wind and subject to actuators saturation. The mathematical model of the wind turbine is derived using the Lagrange dynamics. The controller is formulated using the Takagi–Sugeno fuzzy model and utilizes the parallel distributor compensator to obtain the feedback control gain. The controller’s objective is to obtain the generator electromagnetic torque and the blade pitch angle to attenuate the external disturbances. The (T–S) fuzzy controller with disturbances rejection properties is developed using the linear matrix inequalities technic and solved as an optimization problem. The efficacy of the proposed (T–S) fuzzy controller is illustrated via numerical simulations.

Prescribed Performance Synchronization Control of Chaotic Systems with Unknown Control Gain Signs

For a class of uncertain nonlinear chaotic systems with unknown control gain signs and saturated input, by means of Nussbaum function, a scheme of finite-time prescribed performance synchronization control is proposed. Here, Nussbaum function is used to eliminate the influence of unknown control gain signs, and fuzzy logic systems are used to estimate unknown functions. Lyapunov theory is used to prove that all synchronization errors converge to a predefined small performance range under the designed control method. Finally, simulation results are provided to illustrate the feasibility of the proposed method.