An Integral Sliding Mode approach to hierarchical control of interconnected systems with uncertainties arising from unknown interconnection states and unknown parametric variation is proposed. The work is based on a recent study using constrained LQR showing that a two-level hierarchical control system with on-line learning coordination provides a suitable architecture for control of uncertain ...

This work attempts to achieve motion control along with vibration suppression of flexible systems by developing a sensorless closed loop LQR controller. Vibration suppression is used as a performance index that has to be minimized so that motion control is achieved with zero residual vibration. An estimation algorithm is combined with the regular LQR to develop sensorless motion and vibration c...

This paper studies the exponential stabilization problem for discrete-time switched linear systems based on a control-Lyapunov function approach. A number of versions of converse control-Lyapunov function theorems are proved and their connections to the switched LQR problem are derived. It is shown that the system is exponentially stabilizable if and only if there exists a finite integer N such...

This paper presents a perfect tracking control for discrete-time nonminimum phase of electro-hydraulic servo (EHS) system by adopting a combination of feedback and feedforward controller. A linear-quadratic-regulator (LQR) is firstly design as a feedback controller and a feedforward controller is then proposed to eliminate the phase error emerge by the LQR controller during the tracking control...

A maximum power point tracking (MPPT) technique based on linear quadratic regulator (LQR) approach for solar photo-voltaic system has been proposed in this paper. LQR based MPPT controller has been designed with online set-point adjustment approach using current, radiation and temperature sensors. Real time simulations have been carried out on MATLAB TM /dSPACE TM platform for solar photo-volta...

This paper proposes a systematic method to design hierarchical, decentralized, stabilizing controllers for homogeneous hierarchical dynamical networks. Based on LQR approach with a properly chosen performance index including global and local objectives with control input penalty, an obtained optimal LQR feedback gain gives the closed-loop system a prescribed desirable hierarchical structure. In...

Received Apr 15, 2014 Revised Jul 6, 2014 Accepted Jul 26, 2014 In this paper, a LQR (Linear Quadratic Regulation) optimal method is implemented to control position of an overhead carne. To do this, a tracking formulation of LQR is developed and applied to the system. Hence the dynamic model of the overhead crane is presented, the dynamic of the actuator motor of the trolley is considered. As t...

The standard LQR design technique is extended to systems with parametric uncertainty in the open-loop "A" matrix. This design, called the robust LQR (RLQR), guarantees the stability of the uncertain system, and the same level of performance robustness as standard LQR designs. To determine the properties of the RLQR design, simulations are performed on various mass-spring systems, and compared t...

Stochastic optimal control usually requires an explicit dynamical model with probability distributions, which are difficult to obtain in practice. In this work, we consider the linear quadratic regulator (LQR) problem of unknown systems and adopt a Wasserstein penalty address distribution uncertainty additive stochastic disturbances. By constructing equivalent deterministic game penalized LQR p...

BP Neural Network has a longer training time and a slow convergence. To deal with the defects of BP Neural Network a modified BP algorithm is proposed in the paper. The algorithm is applied for the control of Inverted Pendulum, a highly non linear system inherently being open loop unstable. Levenberg-Marquardt algorithm is used for the training purpose. The training samples are being collected ...