Active Queue Management (AQM) takes a trade-off between link utilization and delay experienced by data packets. From control point of view, it is rational to regard AQM as a typical regulation system. In this paper, two types of controllers, i.e. Sliding Mode Variable Structure (SMVS) controller and optimal Linear Quadratic Regulator (LQR) are designed for AQM. Simulation results conform the ro...

Nowadays, there are a lot of different control methodologies that could be used within industrial processes. Some of these methodologies have a complex design and also demands an extra engineering effort to design the controller with a superior performance. Some other controllers may not lead to a desirable performance although they are too easy to design. The most common controller used on che...

This study presents a Linear Quadratic Optimal (LQR) controller design for an inverted pendulum on a cart using the Artificial Bee Colony (ABC) algorithm. Main design parameters of the linear quadratic regulator are the weighting matrices. Generally, selecting weighting matrices is managed by trial and error since there exists no apparent connection between these weights and time domain require...

This paper deals with the Cross-Entropy method application in the control theory. The method is a the combinatorial optimization technique that is mostly used in the networks theory and could be used in deterministic optimization problems as well. The paper shows the possibiliy of the Cross-Entropy usage in the control parameter tuning. Similar to genetics algorithms, this method minimizes a gi...

Although it is well known that fuzzy learning controller is powerful for nonlinear systems, it is very difficult to apply a learning method if they are unstable. An unstable system diverges for impulse input. This divergence makes it difficult to learn the rules unless we can find the initial rules to make the system stable prior to learning. Therefore, we introduced LQR(Linear Quadratic Regula...

Linear Quadratic Regulator (LQR) is an optimal multivariable feedback control approach that minimizes the excursion in state trajectories of a system while requiring minimum controller effort. The behaviour of a LQR controller is determined by two parameters: state and control weighting matrices. These two matrices are main design parameters to be selected by designer and greatly influence the ...

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...

In the present work a new controller called Particle Swarm Optimization (PSO) based state feedback gain (K) controller has been proposed for frequency regulation of a two area system and then its performance is compared with earlier designed controllers such as Linear Quadratic Regulator– Proportional Integral (LQR-PI) controller and Integral controller. The performance comparison has been done...

In this paper, we provide the solution to the optimal Linear Quadratic Regulator (LQR) paradigm for Markovian Jump linear Systems, when the continuous state is available at the controller instantaneously, but the mode is available only after a delay of one time step. This paper is the first to investigate the LQR paradigm in the presence of such mismatch between the delay in observing the mode ...

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...