Journal of Majlesi Journal of Mechatronic Systems
Volume:2 Issue: 4, Dec 2013

The purpose of this paper is to present the application of the state-dependent Riccati equation (SDRE) method for controlling a class of non-affine nonlinear systems. By mentioning a class of non-affine system, a system with nonlinear states and control inputs is meant. The non-affine in control systems have been secluded in contrast to affine in control systems, especially in the SDRE field. The SDRE method is one of the approaches for solving the Hamilton–Jacobi–Bellman (HJB) equation as a closed loop nonlinear optimal control method. In this work, the SDRE is solved with exact solution (ES) method and online control update (OCU) formulation. The proposed algorithms are verified by three examples which are selected from previous works. The effectiveness of the SDRE is demonstrated through numerical simulations and comparisons with previous works which the SDRE brilliantly outperforms the other techniques in presence of external disturbance.

In this article the electrical parameteres of the induction motor are estimated by measuring temperature of its components. This process consists of four sections: 1- Thermal resistance model 2- Electrical parameters model of the motor and the model gives us corresponding temperatures as output 3- Electrical model and 4- Neural network which presents motor parameters and operational status compatible with corresponding learning data. In thermal resistance model, we use physical and geometrical properties of motor components to formulate thermal resistance of each component of the motor. In electrical model, electrical losses are calculated by electrical variables. In neural network the temperature of the motor components is used as input data and stator current and maximum torque are used as target data. as input data in the electrical These temperatures were used for training network (temperatures as input data and stator current and maximum torque as target data) in the neural network. After measuring temperatures of the components and using them as inputs to neural network, the corresponding stator current and maximum torque (target data) are estimated. All the stator currents referred to this paper were validated by experimental measurements

In this approach (The linear parametric approximation), the nonlinear functions is approximated by a piecewise linear functions. The obtained solution has desirable accuracy and the error is completely controllable. With extension this approach, we propose a new two-step iterative method for solving nonlinear fuzzy equations and nonlinear non-smooth fuzzy equations. Finally some numerical examples are given to show the efficiency of the proposed approach to solve same equations in the other references.

In this paper, problem design a controller of H∞ static output feedback for a flying vehicle of above channel of elastic effect is investigated. Equations of motion of the flying vehicle, even in a channel in the presence of elastic effects, is the nonlinear equations. Design nonlinear Robust H∞ controller for nonlinear control systems are complex issues. Sufficient conditions for the existence of a nonlinear H∞ static output controller in terms of solvability of a polynomial matrix inequalities is given. The method used to design the controller guarantees that the closed-loop system is stable and gain-L2 mapping of the external input noise to the regulated output is less than or equal to a defined value.

The fuel system is one of the significant subsystems of governor which controls output power of power plants. Governing of gas turbine is the procedure of monitoring and controlling the flow rate of fuel into the turbine with the objective of maintaining its speed of rotation at 3000 rpm in V94. 2 gas turbines. The flow rate of fuel is controlled by interposing valves between the fuel tank and the turbine. In V94. 2 gas turbine these valves are servo electro hydraulic type. At once governor receives command from grid power to rise or decrease the output power, begins processing it in its PLC and then sends a current signal of 4-20mA to coils on servo valve. Subsequently this current can apply a torque on servo flapper which causes a movement in actuator and so it can change the opening of control valve [1]. Of course this response must be at a short time [2]. To measure the dynamic response parameters such as delay time, rise time, settling time there are two ways, 1-Fast sampling of output signal, 2-Using a dynamic model between input and output signals. In this article both of these methods will be used. As a result of first method we will directly acquire the dynamic response parameters and from second method be able to simulate and analysis the system response and then optimizing the controller parameters if it is needed such as Kp, Td, Ti, zeroes and poles positions. Fast sampling will be done by a data acquisition card and then collected data will be used in “system identification” toolbox of MATLAB to obtain a dynamic model through Black box method. This paper is going to describe the way to obtain the fuel system dynamic model through the practical tests based on the «Black Box system identification” method.

Engineering and Technology in the coming century instrumentations, will encounter the dimensions within the range of nanometers for nano particles. Knowing the size of nano particles has great impact on mechanical strength, density, optical and thermal properties for final product, the nano measurements are very necessary. From the current styles about the measurement of nano particles can name dynamic and static light scattering. Dynamic light scattering is one of the most applicable techniques for measuring the hydrodynamic radius of nano particles. In this method autocorrelation function computes in the certain angle by measuring intensity scattering of incident light. In this paper theoretically simultaneously measures the scattering and computes in three directions, and the results compared with measuring in a direction mode. The procedure showed that the measurement accuracy was improved. It also showed the need for a measurement system in high speed hardware.