A weighted summation of Integral of Time Multiplied Absolute Error (ITAE) and Integral of Squared Controller Output (ISCO) minimization based time domain optimal tuning of fractional-order (FO) PID or PIλDμ controller is proposed in this paper with a Linear Quadratic Regulator (LQR) based technique that minimizes the change in trajectories of the state variables and the control signal. A class ...

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...

The linear quadratic optimal regulator problem on a wave equation in a bounded subset of R n for n = 2; 3 with boundary damping and pointwise control input is formulated as a linear quadratic control problem with unbounded input/output operators using a variational approach. A dual control system is deened which allows us to investigate the regularity of the solution of the generalized wave equ...

The linear quadratic optimal regulator problem on a wave equation in a bounded subset of R for n = 2; 3 with boundary damping and pointwise control input is formulated as a linear quadratic control problem with unbounded input/output operators using a variational approach. A dual control system is de ned which allows us to investigate the regularity of the solution of the generalized wave equat...

This article presents a neural algorithm based on Reinforcement Learning for optimising Linear Quadratic Regulator (LQR) creation. The proposed method allows designing such target function that automatically leads to changes in the quality and resource matrix so LQR regulator achieves desired performance. solution’s stability optimality are controller’s responsibility. However, mechanism obtain...

Linear Quadratic Gaussian (LQG) control has a known analytical solution [1] but non-linear problems do not [2]. The state of the art method used to find approximate solutions to non-linear control problems (iterative LQG) [3] carries a large computational cost associated with iterative calculations [4]. We propose a novel approach for solving nonlinear Optimal Control (OC) problems which combin...

We propose a distributed data-based predictive control scheme to stabilize network system described by linear dynamics. Agents cooperate predict the future evolution without knowledge of dynamics, relying instead on learning representation from single sample trajectory. employ this reformulate finite-horizon Linear Quadratic Regulator problem as optimization with separable objective functions a...

Nowadays, interceptors are often used to decrease total resistance and enhance comfort by reducing dynamic trim for high-speed planing vessels. They can be controlled manually as well automatically using a suitable closed-loop control system. Thus, in the present study, an controllable system is presented minimize calm water. To reach this aim, mathematical model which represent 2 degree of fre...

In this paper we solve the basic fractional analogue of the classical linear-quadratic Gaussian regulator problem in continuous-time with partial observation. For a controlled linear system where both the state and observation processes are driven by fractional Brownian motions, we describe explicitly the optimal control policy which minimizes a quadratic performance criterion. Actually, we sho...

In this paper a generalization of the indirect pseudo-spectral method, presented in [17], for numerical solution budget-constrained infinite horizon optimal control problems is presented. Consideration problem statement framework weighted functional spaces allows to arrive at good approximation initial value adjoint variable, which inevitable obtaining solutions. The method illustrated by apply...