This paper proposes an Adaptive Neuro Linear Quadratic Regulator (ANLQR) controller for Buck converter operating under harmful disturbances. Considering the real-time condition of a with regular variations, Neural Network is adopted to improve and tune gain LQR strategy adaptive mechanism. assumes system as Gray-box process without need exact mathematical model which can result in lower computa...

BACKGROUND We investigated the applicability of linear quadratic Gaussian (LQG) methodology to the subcutaneous blood glucose regulation problem. We designed an LQG-based feedback control algorithm using linearization of a previously published metabolic model of type 1 diabetes. A key feature of the controller is a Kalman filter used to estimate metabolic states of the patient based on continuo...

An AFS (active front steering) on the basic of a C-EPS (column electric power steering) system was developed. Based on the mathematical model of essential components, the AFS controller was designed covering two units: an EPS actuator unit was used to reduce the steering torque requirement to the driver; an AFS actuator unit was used to compensate the steering angle for steering characteristics...

AbstractThis paper considered the problem of stabilization of longitudinal and lateral dynamics of an unmanned autonomous helicopter (UAH) for hovering flight mode in a gusty environment. The controllers design is based on generic linear model which successfully describe the behavior of most smallscale helicopters. A backstepping design procedure is used to design the adaptive controller for lo...

Linear quadratic regulator (LQR) with trajectory sensitivity minimization and structurally constrained controller was formulated in [2] and [4]. In these works, a gradient search method, which is a particular type of nonlinear programming, was used to solve the problem. In this paper, we propose a linear matrix inequality (LMI)-based method for the problem. This LMI is formulated with block-dia...

A hybrid model is designed by combining the genetic algorithm (GA), radial basis function neural network (RBF-NN) and Sugeno fuzzy logic to determine the optimal parameters of a proportional-integralderivative (PID) controller. Our approach used the rule base of the Sugeno fuzzy system and fuzzy PID controller of the automatic voltage regulator (AVR) to improve the system sensitive response. Th...

Design of a robust controller via single objective constrained optimization using differential evolution (DE) is presented in this paper. A set robust feedback controller gain is optimized based on plant’s linear model having structured parametric uncertainty such that the closed-loop system would have the maximum stability radius. A wedge region is assigned as the optimization constraint to sp...

Abstract—The design and implementation of the hybrid control method for a three-pole active magnetic bearing (AMB) is proposed in this paper. The system is inherently nonlinear and conventional nonlinear controllers are a little complicated, while the proposed hybrid controller has a piecewise linear form, i.e. linear in each subregion. A state-feedback hybrid controller is designed in this stu...

This paper presents the design of optimal controller for nonlinear Rotary Inverted Pendulum (RIP) dynamic system using Linear Quadratic Regulator (LQR). LQR, an optimal control technique is generally used for control of the linear dynamical systems, have been used in this paper to control the non linear dynamical system. The non linear system states are fed to LQR which is designed using linear...