Ahmed Khamis

Control System Design Based on Modern Embedded Systems

The functionality and complexity of real-world engineering control systems is increasing significantly due to continuous growth in requirements and their details. Since this trend is predicted to grow even stronger, the old control solutions will be becoming less and less efficient. There are several approaches to designing modern control systems that meet the current and future needs. In this paper, we focus on one of the promising ways to control engineering: Embedded Systems. We describe categories of embedded systems and an engineering approach to control systems design based on the embedded systems. All related challenges are presented considering weaknesses of traditional systems. For the described embedded control system, a design methodology is given as well. Our discussion focuses on approach based on Field-Programmable Gate Array (FPGA) as a solution with huge potential. Finally, we share our thoughts on further trends in modern embedded control systems.

Nonlinear Finite-Horizon Regulation and Tracking for Systems with Incomplete State Information Using Differential State Dependent Riccati Equation

This paper presents an efficient online technique used for finite-horizon, nonlinear, stochastic, regulator, and tracking problems. This can be accomplished by the integration of the differential SDRE filter algorithm and the finite-horizon state dependent Riccati equation (SDRE) technique. Unlike the previous methods which deal with the linearized system, this technique provides finite-horizon estimation and control of the nonlinear stochastic systems. Further, the proposed technique is effective for a wide range of operating points. Simulation results of a missile guidance system are presented to illustrate the effectiveness of the proposed technique.

Nonlinear Optimal Control Using Finite-Horizon State Dependent Riccati Equation Combined with Genetic Algorithm

Precise model of DC motor is a nonlinear model. Accurate nonlinear control of DC motors is required. A novel online technique for finite-horizon nonlinear tracking problem is offered in this paper. The idea of the proposed technique is the change of variables that converts the nonlinear differential Riccati equation to a linear Lyapunov differential equation. Genetic algorithm is used as a method to calculate the optimal weighting matrices. Unlike the linear techniques that are used for linearized systems, the proposed technique is effective for wide range of operating points. Simulation results are given to demonstrate the effectiveness of the offered technique.

Nonlinear Position Control of DC Motor Using Finite-Horizon State Dependent Riccati Equation

DC motors are often used for accurate positioning in industrial machines. Precise equations describing DC motors are nonlinear. Accurate nonlinear control of the motion of the DC motors is required. In this paper, an online technique for finite-horizon nonlinear tracking problems is presented. The idea of the proposed technique is the change of variables that converts the nonlinear differential Riccati equation to a linear Lyapunov differential equation. The proposed technique is effective for wide range of operating points. Simulation results for a realistic DC motor are given to illustrate the effectiveness of the proposed technique.

Nonlinear optimal tracking with incomplete state information using finite-horizon State Dependent Riccati Equation (SDRE)

In this paper, an online technique for finite-horizon nonlinear stochastic tracking problems is presented. The idea of the proposed technique is to integrate the Kalman filter algorithm and the State Dependent Riccati Equation (SDRE) technique. Unlike the ordinary methods which deal with the linearized system, this technique will estimate the unmeasured states of the nonlinear system directly, and this will make the proposed technique effective for wide range of operating points. Numerical example is given to illustrate the effectiveness of the proposed technique.

Real-time algorithm for nonlinear systems with incomplete state information using finite-horizon optimal control technique

This paper discusses a novel efficient real-time technique used for finite-horizon nonlinear regulator problems with incomplete state information. This technique based on integrating the Kalman filter algorithm and the finite-horizon differential State Dependent Riccati Equation (SDRE) technique. In this technique, the optimal control problem of the nonlinear system is solved by using finite-horizon differential SDRE algorithm, which makes this technique effective for a wide range of operating points. A nonlinear mechanical crane is given to show the effectiveness of the proposed technique.

Finite-Horizon Optimal Nonlinear Regulation and Tracking Using Differential State Dependent Riccati Equation

A number of computational techniques have been proposed for synthesizing nonlinear control based on state dependent Riccati equation (SDRE). Most of these techniques focusing on infinite-horizon regulator problems. This paper presents a novel and computationally efficient online technique used for finite-horizon nonlinear regulation and tracking problems. In the finite-horizon SDRE problems, the solution is time dependent and a differential equation, rather than an algebraic equation in infinite-horizon SDRE. This technique is based on change of variables that converts the differential Riccati equation to a linear Lyapunov differential equation. Simulation results are provided to illustrate the effectiveness of the proposed technique.

Nonlinear Optimal Tracking With Incomplete State Information Using State Dependent Riccati Equation

A number of computational techniques have been offered for estimation of unmeasured states in nonlinear systems. Most of these techniques rely on applying the linear estimation techniques to the linearized systems, which can be effective only in the neighborhood of the operating point. This paper presents an online technique for nonlinear stochastic tracking problems. The idea of the proposed technique is to integrate the Kalman filter algorithm and the State Dependent Riccati Equation (SDRE) technique. Unlike the ordinary methods which deal with the linearized system, this technique will estimate the unmeasured states of the nonlinear system directly, and this will make the proposed technique effective for wide range of operating points. Numerical example is given to illustrate the effectiveness of the proposed technique.

Experimental validation for real time control of DC motor using novel finite-horizon optimal technique

DC motors are widely used in industrial applications. Controlling of DC motor is a great challenge for control engineers. Precise equations describing DC motors are nonlinear. Accurate nonlinear control of the motion of the DC motors is required. In this paper, a novel online technique for finite-horizon nonlinear tracking problems is presented. The idea of the proposed technique is the change of variables, that converts the nonlinear differential Riccati equation to a linear Lyapunov differential equation. The proposed technique is effective for a wide range of operating points. Simulation results and experimental implementation of a DC motor are given to illustrate the effectiveness of the proposed technique.

Improved Genetic Algorithm for Finite-Horizon Optimal Control of Nonlinear Systems

Optimal control has been a very attractive and desirable feature for many dynamic and static systems, An effective online technique for finite-horizon nonlinear control problem is offered in this paper. The idea of the proposed technique is to combine the differential State Dependent Riccati equation filter algorithm and the finite-horizon SDRE technique. Genetic algorithm is used to calculate the optimal weighting matrices. Unlike the linear techniques that are used for linearized systems, the proposed technique is effective for wide range of operating points. Simulation results are given to demonstrate the effectiveness of the offered technique.