Many different PD controller modeling, configurations and control algorithms have been developed. These methods differ in their theoretical basis and performance under the changes of system conditions. In the present paper we review the methods used in the design of PD control systems. We highlight the main difficulties and summarize the more recent developments in their control techniques. Intelligent control systems like PD fuzzy control can be used to emulate the qualitative aspects of human knowledge with several advantages such as universal approximation theorem and rule-based algorithms.

This paper presents some considerations regarding design, modeling and control solutions for Photovoltaic Panel-Converter (PVPC) system. Different control approaches and corresponding models are derived, developed and tested, to control output characteristics and performance of both overall PVPC system and each subsystem to meet desired output characteristics, performance and both and/or either voltages and currents requirements. The proposed approaches and models allow designer have the maximum output numerical visual and graphical data to select, evaluate and control the PVPC system output characteristics for a given PVPC system parameters, under given working conditions of PV panel. The proposed models and approaches were implemented and tested in MATLAB/Simulink.

This paper proposes a new, simple and user–friendly MATLAB built-in function, mathematical and Simulink models, to be used to early identify system level problems, to ensure that all design requirements are met, and, generally, to simplify Mechatronics motion control design process including; performance analysis and verification of a given electric DC machine, proper controller selection and verification for desired output speed or angle.

This paper extends writer's previous work and proposes design, modeling and control issues of a simple robot arm design. Mathematical, Simulink models and MATLAB program are developed to return maximum numerical visual and graphical data to select, design, control and analyze arm system. Testing the proposed models and program for different input values, when different control strategies are applied, show the accuracy and applicability of derived models. The proposed are intended for education and research purposes.

This paper proposes a new MATLAB built-in function, mathematical and simulink models, all to be simultaneously simple, user–friendly and to be used to face the two top challenges in developing mechatronic motion control systems, particularly, the early identifying system level problems and ensuring that all design requirements are met, as well as, to simplify and accelerate Mechatronics motion control design process including; performance analysis and verification of a given electric DC system, proper controller selection and verification for desired output speed or angle. The proposed models and function are intended for research purposes, application in educational process, and to be used by the mechatronics students and engineers for selection, design and verification purposes.

The accurate control of motion is a fundamental concern in mechatronics applications, where placing an object in the exact desired location with the exact possible amount of force and torque at the correct exact time is essential for efficient system operation. An accurate modeling, simulation and dynamics analysis of actuators for mechatronics motion control applications is of big concern. The ultimate goal of this paper addresses different approaches used to derive mathematical models, building corresponding simulink models and dynamic analysis of the basic open loop electric DC motor system, used in mechatronics motion control applications, particularly, to design, construct and control of a mechatronics robot arm with single degree of freedom, and verification by MATLAB/Simulink. To simplify and accelerate the process of DC motors sizing, selection, dynamic analysis and evaluation for different motion applications, different mathematical models in terms of output position, speed, current, acceleration and torque, as well as corresponding simulink models, supporting MATLAB m.file and general function block models are to be introduced. The introduced models were verified using MATLAB/ Simulink. These models are intended for research purposes as well as for the application in educational process.
This paper is part I of writers' research about mechatronics motion control, the ultimate goal of this research addresses design, modeling, simulation, dynamics analysis and controller selection and design issues, of mechatronics single joint robot arm. where a electric DC motor is used and a control system is selected and designed to move a Robot arm to a desired output position, θ corresponding to applied input voltage, Vin and satisfying all required design specifications.

Mobile robot motion control is simplified to a DC motor motion control that may include gear system. The simplest and widespread approach to control the mobile robot motion is the differential drive style, it consists of two in-lines with each a DC motor. Both DC motors are independently powered so the desired movements will rely on how these two DC motors are commanded. Thedevelop design, model and control of Mechatronics mobile robotic system is presented in this paper. The developed robotic system is intended for research purposes as well as for educational process. The model of proposed mobile robot was created and verified using MATLAB-Simulink software.

The development of a fuzzy-logic controller for a class of industrial hydraulic manipulator is described. The main element of the controller is a PI-type fuzzy control technique which utilizes a simple set of membership functions and rules to meet the basic control requirements of such robots. Using the triangle shaped membership function, the position of the servocylinder was successfully controlled. When the system parameter is altered, the control algorithm is shown to be robust and more faster compared to the traditional PID controller. The robustness and tracking ability of the controller were demonstrated through simulations.