Comparison of Model Predictive Control (MPC) and Linear-Quadratic Gaussian (LQG) Algorithm for Electrohydraulic Steering Control System

Predictive Inverse Optimal Control for Linear-Quadratic-Gaussian Systems

Proof of Lemma 1. The previously developed theory of maximum causal entropy [28] shows the causally conditioned probability distribution defined according to affine constraint (15),(16) and (18) are equivalent to it defined by the decomposition into a product of conditional probabilities (35),(36). Then, we show partial observability constraint (17) implies (37). ∀�u1:T ∈ � U1:T , �x1:T ,x1:T ∈...

Linear-Quadratic Fractional Gaussian Control

A control problem for a linear stochastic system with an arbitrary fractional Brownian motion and a cost functional that is quadratic in the system state and the control is solved. An optimal control is given explicitly as the sum of a prediction of the future fractional Brownian motion and the well known linear feedback control for the associated deterministic linear-quadratic control problem....

A comparison between LQG and MPC autopilots for inclusion in a navigation, guidance and control system

Linear - Quadratic - Gaussian ( LQG ) Controller for Two link - robotic Manipulator Control

This paper presents a new method for the control of Two linkrobotic manipulator systems using Linear-QuadraticGaussian (LQG), in This method Linear-Quadratic-Gaussian (LQG) that is a modern state-space technique for designing optimal dynamic regulators by kalman used for the control of Two linkrobotic manipulator systems. The simulation result are very promising.

Model Predictive Control with Integral Action: A simple MPC algorithm

A simple Model Predictive Control (MPC) algorithm of velocity (incremental) form is presented. The proposed MPC controller is insensitive to slowly varying system and measurement trends and therefore has integral action. The presented algorithm is illustrated by both simulations and practical experiments on a quadruple tank MIMO process.