A Design and Implementation of a New Control Based on Petri Nets for Three Phase PWM-Rectifier

This article introduces a novel and effective diagram based on direct instantaneous power control (DPC) of a PWM-controlled rectifier connected to the grid without a switching table. An optimum control vector of the PWM rectifier's input voltage, which depends on the switching states determined by a Petri nets controller, is adopted. This approach limits the instantaneous detection errors of reactive and active powers, maintains the DC bus voltage at a reference level, and ensures current close to a sinusoidal wave, guaranteeing operation at a unit power factor. The instantaneous tracking errors of active and reactive powers and the angular position of the voltage are used as input variables for the proposed controller, which then selects the best control vector for the converter based on the transition of a Petri net. The significant advantages of DPC based on Petri nets compared to traditional switching tables are that hysteresis comparators are not required, and the classical regulation of active and reactive powers is achieved in all sectors. Simulation and testing findings demonstrated excellent performance, supporting the viability of the suggested control approach using Petri nets

Sliding Mode Control of the PUMA 560 Robot

The purpose of this article is to present the application of the sliding mode control and investigate its effectiveness when applied to a three-dimensional robotic manipulator model. The analysis is based on the application of the sliding mode control law for the PUMA 560 model, three degrees of freedom, through the development of a dynamic simulation model. The simulation results show the effectiveness of this proposed method for the automation of industrial applications, such as assembly, machining (deburring, trimming), and surface tracking (polishing). This technique provides a useful insight into the advantages of using sliding mode control laws in robotics applications

Modeling and Simulation of Quadcopter Using Self-tuning Fuzzy-PI Controller

Helicopters, commonly known as quadrotors (UAVs), are popular unmanned aerial vehicles. Despite their small size and high stability, they are used in a variety of applications. This chapter presents the fundamental principles for modeling and controlling quadcopters that will form the basis for future research and development in the field of drones. The problem is addressed on two fronts; first, the mathematical dynamic models are developed, and second, the trajectory of the quadcopter is stabilized and controlled. IMUs (Inertial Measurement Units) consist of accelerometers and gyroscopes and constitute the core of the system. In order to fly the quadcopter in six directions, it is necessary to determine the orientation of the system and control the speed of four BLDC motors. A Matlab/Simulink analysis of the quadcopter is performed. A self-tuning fuzzy-PI regulator is used to control the quadcopter’s pitch, roll, and yaw. It was evaluated whether the quadcopter controller was effective and efficient, and the desired outputs were discussed

Design and Implementation of a Robust 6-DOF Quadrotor Controller Based on Kalman Filter for Position Control

The objective of this chapter is to develop quadcopter flight control algorithms using a PID controller enhanced by a Kalman Filter (KF) using an experimental approach to extract the physical and aerodynamic settings of the quadcopter. It is first necessary to present the current state of the quadcopter analytical dynamics model in order to achieve an effective design. A second step involves the development of the quadcopter’s hardware and software, as well as the development of a full thrust test rig to extract the parameters of the propulsion system and the linearisation approximations between the different variables. Using the quadcopter’s 6-DOF analytical dynamic model, the controller’s control parameters are determined using a PID design enhanced with KF. Test results were assessed using dynamic response curves and 3D Matlab visualisations. In order to evaluate the performance of the PID controllers, we measured the time response, overshoot, and settling time with and without the KF. After the SIMULINK model’s results for the drone were accepted, a C++ code was produced. Uploading the generated code into the Pixhawk autopilot was accomplished through a Simulink application in the autopilot firmware. Based on the Pixhawk autopilot, we present a quick and real-time test solution for drone controllers. Further enhancements are provided by near-real-time tuning of the control settings. This research uses the Embedded Coder Tool to develop SIMULINK-generated code for the Pixhawk autopilot board