 The ADRC tuning is essentially a pole-placement technique and the desired performance is indirectly achieved through the location of the closed-loop poles. However, the final choice of these poles becomes a trial-and-error strategy. In contrast with pole-placement, in the LQR method, the desired performance objectives are directly and globally addressed by minimizing a quadratic function of ...

In this paper an infinite-dimensional LQR control-based design for a system containing linear hyperbolic partial differential equations coupled with linear ordinary differential equations is presented. The design is based on an infinite-dimensional Hilbert state-space representation of the coupled system. The feedback control gain is obtained by solving algebraic and differential matrix Riccati...

We present a new walking controller based on 3LP, a 3D model of bipedal walking that is composed of three pendulums to simulate falling, swing and torso dynamics. Taking advantage of linear equations and closed-form solutions of 3LP, the proposed controller projects intermediate states of the biped back to the beginning of the phase for which a discrete LQR controller is designed. After the pro...

This paper describes the LMI synthesis of feedback controllers which minimise closed loop transient perturbation growth with limited control effort. Controllers are synthesized for the linearised Lorenz equations, and their performance is compared to that of LQR controllers. At low control effort the controllers behave similarly, but the LMI based controllers are able to produce an almost monot...

One of the challenging tasks concerning two wheeled inverted pendulum (TWIP) mobile robot is balancing its tilt to upright position, this is due to its inherently open loop instability. This paper presents an experimental comparison between model based controller and non-model based controllers in balancing the TWIP mobile robot. A Fuzzy Logic Controller (FLC) which is a non-model based control...

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...

In this report a number of algorithms for optimal control of a double inverted pendulum on a cart (DIPC) are investigated and compared. Modeling is based on Euler-Lagrange equations derived by specifying a Lagrangian, difference between kinetic and potential energy of the DIPC system. This results in a system of nonlinear differential equations consisting of three 2-nd order equations. This sys...

This paper presents an LQR-Based 6DOF control of an unmanned aerial vehicles (UAV), namely a small-scale quadrocopter. Due to its high nonlinearity and a high degree of coupling system, the control of an UAV is very challenging. quadrocopter trajectory tracking in a 3D space is greatly affected by the quadrocopter balancing around its roll-pitch-yaw frame. Lack of precise tracking control about...

State-of-the-art approaches in quadrotor control split the problem into multiple cascaded subproblems, exploiting the different time scales of the rotational and translational dynamics. They calculate a desired acceleration as input for a cascaded attitude controller but omit the attitude dynamics. These approaches use limits on the desired acceleration to maintain feasibility and robustness th...

1 John Watkins, Systems Engineering Department, U.S. Naval Academy, 105 Maryland Ave, Annapolis, MD 21402 [email protected] 2 Eugene Mitchell, Systems Engineering Department, U.S. Naval Academy, 105 Maryland Ave, Annapolis, MD 21402 [email protected] Abstract  The optimal Linear Quadratic Regulator (LQR) method is a powerful technique for designing controllers for comple...