Particle swarm optimization (PSO) is an algorithm that simple and reliable to complete optimization. The balance between exploration exploitation of PSO searching characteristics maintained by inertia weight. Since this parameter has been introduced, there have several different strategies determine the weight during a train run. This paper describes method adjusting weights using fuzzy signatu...

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 presents an adaptive particle swarm optimization (APSO) based LQR controller for optimal tuning of state feedback controller gains for a class of under actuated system (Inverted pendulum). Normally, the weights of LQR controller are chosen based on trial and error approach to obtain the optimum controller gains, but it is often cumbersome and tedious to tune the controller gains via ...

Optimal control is one of the most important branches in modern control theory, and linear quadratic regulator (LQR) has been well used and developed in linear control systems. However, there would be several problems in employing LQR to uncertain nonlinear systems. The optimal LQR problem for nonlinear systems often leads to solving a nonlinear two-point boundary-value (TPBV) problem (Tang et ...

Unmanned aerial vehicle experience different atmospheric uncertainties during their flight. These uncertainties caused the problem in the stability and the desired performance of the system. This research paper structured to nullify the influence of atmospheric turbulence and ground effect on the UAV. The models of Dryden turbulence is used as it is one of the major disturbances affecting the U...

This work presents the BDU technique (Bounded Data Uncertainties) and the tuning of the linear quadratic regulator (LQR) via this technique, which considers models with bounded uncertainties. The BDU method is based on constrained game-type formulations, and allows the designer to explicitly incorporate a priori information about bounds on the sizes of the uncertainties into the problem stateme...

The tuning of Linear Quadratic regulator (LQR) controllers is a challenge for researchers and plant operators. This paper presents a optimization and comparison of time response specification between Traditional ZN Tuning & Modified ZN Tuning controllers with Linear Quadratic Regulator (LQR) for a speed control of a separately excited DC motor. The goal is to determine which control strategy de...

طلتخملا رطيسملل ميمصت مدقي ثحبلا اذه نا LQR/H-infinity .ةدكؤملا ريغو تاريغتملا ةددعتم ةمظنلأل مت ةفلك ةلاد ىلع ادامتعا ةعجارلا ةيذغتلا ةلاح تباوثل ىلثملا ميقلا داجيا ضرغل يئيزجلا دشحلا ةيلثما ةقيرط مادختسا تاددحم نيب طيلخ نع ةرابع يه ةحرتقملا ةفلكلا ةلاد نا.ةحرتقم LQR و H-infinity حرتقملا رطيسملا نا. ريغتملا ةددعتم ةمظنلأا ضوعي نا ةياع ةءافكبو عيطتسي رطيسملا ةوق تابثا ضرغل .ةيدكؤملا مدع دوجوب ...

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 ...