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Go to Editorial ManagerAt recent days, the robot performs many tasks on behalf of humans or in support of humans. Among the most prominent benefits of robots for humans are removing the risk factor from humans, completing routine tasks for humans, saving a lot of time and effort, and mastering work. This paper presents a model of an eight-legged robot equipped with an intelligent controller that was simulated using MATLAB. The designed structure contains 24 controllers, three for each leg, to provide flexibility in movement and rotation. Proportional Integral Derivative (PID) controller has been used in this work , each leg contains three PIDs. A particle swarm optimization algorithm (PSO) was used to adjust the parameters of the PID controller (Kp , Ki and Kd). The structure of eight legs robot with controller is implemented using Simscape Multibody in the MATLAB program, where the movement of the eight-legged robot is visualized and analyzed without the need for complex analysis associated with a mathematical model. The simulation results were conducted in a three-dimensional environment and were presented in two scenarios . The first was implementing and simulating the robot without using a controller, which leads to the robot falling at the starting point. The second was when a PID controllers are used with the system, where better movement was obtained. Finally, the robustness of the controller was verified by changing the load that the robot bears.
Engineers are searching for alternatives to conventional energy sources to address the energy crisis as a result of the sharp increase in energy usage. This work describes developing, simulating, and evaluating a three-phase, 13.25 kW solar power system. PV analysis is also performed. An inverter featuring a dual Electricity flow is connected to a solar system consisting of six consecutive strings of four solar power cells connected in series. The output of the phase lock loop (PLL) feedback in the linearization system is used to generate a signal, and the power conversion voltage is synchronized with the signal by using its output as a voltage reference. This hybrid technology, which has two phases that are optimal for the rechargeable recharging process of the batteries, is used to replenish a battery bank in capacity or float arrangement for eight sequences of 12V - 200-Ah rechargeable batteries. Ultimately, a MATLAB computational model has been created for a grid-connected photovoltaic system that uses sinusoidal modulation of pulse width and an inverter as voltage sources.
The reluctance of industry to allow wireless paths to be incorporated in process control loops has limited the potential applications and benefits of wireless systems. The challenge is to maintain the performance of a control loop, which is degraded by slow data rates and delays in a wireless path. To overcome these challenges, this paper presents an application–level design for a wireless sensor/actuator network (WSAN) based on the “automated architecture”. The resulting WSAN system is used in the developing of a wireless distributed control system (WDCS). The implementation of our wireless system involves the building of a wireless sensor network (WSN) for data acquisition and controller area network (CAN) protocol fieldbus system for plant actuation. The sensor/actuator system is controlled by an intelligent digital control algorithm that involves a controller developed with velocity PID- like Fuzzy Neural Petri Net (FNPN) system. This control system satisfies two important real-time requirements: bumpless transfer and anti-windup, which are needed when manual/auto operating aspect is adopted in the system. The intelligent controller is learned by a learning algorithm based on back-propagation. The concept of petri net is used in the development of FNN to get a correlation between the error at the input of the controller and the number of rules of the fuzzy-neural controller leading to a reduction in the number of active rules. The resultant controller is called robust fuzzy neural petri net (RFNPN) controller which is created as a software model developed with MATLAB. The developed concepts were evaluated through simulations as well validated by real-time experiments that used a plant system with a water bath to satisfy a temperature control. The effect of disturbance is also studied to prove the system's robustness.
The main objective of designed the controller for a vehicle suspension system is to reduce the discomfort sensed by passengers which arises from road roughness and to increase the ride handling associated with the pitching and rolling movements. This necessitates a very fast and accurate controller to meet as much control objectives, as possible. Therefore, this paper deals with an artificial intelligence Neuro-Fuzzy (NF) technique to design a robust controller to meet the control objectives. The advantage of this controller is that it can handle the nonlinearities faster than other conventional controllers. The approach of the proposed controller is to minimize the vibrations on each corner of vehicle by supplying control forces to suspension system when travelling on rough road. The other purpose for using the NF controller for vehicle model is to reduce the body inclinations that are made during intensive manoeuvres including braking and cornering. A full vehicle nonlinear active suspension system is introduced and tested. The robustness of the proposed controller is being assessed by comparing with an optimal Fractional Order PI λ D μ (FOPID) controller. The results show that the intelligent NF controller has improved the dynamic response measured by decreasing the cost function.