Comparison of backstepping, sliding mode and PID regulators for a voltage inverter

<p><span>In the present paper, an efficient and performant nonlinear regulator is designed for the control of the pulse width modulation (PWM) voltage inverter that can be used in a standalone photovoltaic microgrid. The main objective of our control is to produce a sinusoidal voltage output signal with amplitude and frequency that are fixed by the reference signal for different loads including linear or nonlinear types. A comparative performance study of controllers based on linear and non-linear techniques such as backstepping, sliding mode, and proportional integral derivative (PID) is developed to ensure the best choice among these three types of controllers. The performance of the system is investigated and compared under various operating conditions by simulations in the MATLAB/Simulink environment to demonstrate the effectiveness of the control methods. Our investigation shows that the backstepping controller can give better performance than the sliding mode and PID controllers. The accuracy and efficiency of the proposed backstepping controller are verified experimentally in terms of tracking objectives.</span></p>

ДОСЛІДЖЕННЯ РОБОТИ СИСТЕМ КЕРУВАННЯ ДОЗУВАЛЬНИМ ОБЛАДНАННЯМ БЕЗПЕРЕРВНОЇ ДІЇ З «ПІ» ТА «ПІД» РЕГУЛЯТОРАМИ

Purpose. Investigation of transients in the control system of continuous dosing equipment for bulk materials using PI and PID regulators and evaluation of their impact on the quality of work elements.Method. Using the Mathlab: Simulink software environment to develop a mathematical model, conduct experimental research and assess the impact on the working bodies of the control system of continuous dosing equipment.Researchresults. Control of motor parameters (motor characteristics) by means of standard regulators of their comparison of advantages and disadvantages. described in many standards and scientific papers. However, modeling the engine operation process in dosing systems is significantly complicated due to the inertness and adhesion of bulk material dosed in the core of the hopper, it can significantly affect the final result of the finished mixture. continuous operation equipment using PI and PID controllers, an experiment was performed on a mathematical model using PI and PID controllers to assess their impact on the control system of the plate feeder continuous action. The results of the simulation can be used to decide on the type of controller control of transient characteristics of the engine, at development of control system of plate feeders in mixing complexes of continuous action.Scientificnovelty. Scientific novelty. The parameters influencing the frequency with which the motor of the plate feeder of continuous action rotates are determined. The time of the transient process of engine operation and the value of the maximum dynamic deviation are determined. The expediency of using regulators of one or another type for certain modes of operation of feeders is proved.Practical significance. The obtained results will reduce the transient time in the operation of the feeder motor and increase the operating time until the failure of the entire system. Design changes are proposed that will reduce the amount of ripple and improve the performance of continuous dosing equipment.

Metaheuristic Optimization of PD and PID Controllers for Robotic Manipulators

In this paper, the Particle Swarm Optimization algorithm (PSO) is combined with Proportional-Derivative (PD) and Proportional-Integral-Derivative (PID) to design more efficient PD and PID controllers for robotic manipulators. PSO is used to optimize the controller parameters Kp (proportional gain), Ki (integral gain) and Kd (derivative gain) to achieve better performances. The proposed algorithm is performed in two steps: (1) First, PD and PID parameters are offline optimized by the PSO algorithm. (2) Second, the obtained optimal parameters are fed in the online control loop. Stability of the proposed scheme is established using Lyapunov stability theorem, where we guarantee the global stability of the resulting closed-loop system, in the sense that all signals involved are uniformly bounded. Computer simulations of a two-link robotic manipulator have been performed to study the efficiency of the proposed method. Simulations and comparisons with genetic algorithms show that the results are very encouraging and achieve good performances.

Self-Optimizing Path Tracking Controller for Intelligent Vehicles Based on Reinforcement Learning

The path tracking control system is a crucial component for autonomous vehicles; it is challenging to realize accurate tracking control when approaching a wide range of uncertain situations and dynamic environments, particularly when such control must perform as well as, or better than, human drivers. While many methods provide state-of-the-art tracking performance, they tend to emphasize constant PID control parameters, calibrated by human experience, to improve tracking accuracy. A detailed analysis shows that PID controllers inefficiently reduce the lateral error under various conditions, such as complex trajectories and variable speed. In addition, intelligent driving vehicles are highly non-linear objects, and high-fidelity models are unavailable in most autonomous systems. As for the model-based controller (MPC or LQR), the complex modeling process may increase the computational burden. With that in mind, a self-optimizing, path tracking controller structure, based on reinforcement learning, is proposed. For the lateral control of the vehicle, a steering method based on the fusion of the reinforcement learning and traditional PID controllers is designed to adapt to various tracking scenarios. According to the pre-defined path geometry and the real-time status of the vehicle, the interactive learning mechanism, based on an RL framework (actor–critic—a symmetric network structure), can realize the online optimization of PID control parameters in order to better deal with the tracking error under complex trajectories and dynamic changes of vehicle model parameters. The adaptive performance of velocity changes was also considered in the tracking process. The proposed controlling approach was tested in different path tracking scenarios, both the driving simulator platforms and on-site vehicle experiments have verified the effects of our proposed self-optimizing controller. The results show that the approach can adaptively change the weights of PID to maintain a tracking error (simulation: within ±0.071 m; realistic vehicle: within ±0.272 m) and steering wheel vibration standard deviations (simulation: within ±0.04°; realistic vehicle: within ±80.69°); additionally, it can adapt to high-speed simulation scenarios (the maximum speed is above 100 km/h and the average speed through curves is 63–76 km/h).

Improving 3D Path Tracking of Unmanned Aerial Vehicles through Optimization of Compensated PD and PID Controllers

The development of quadrotor unmanned aerial vehicles (QUAVs) is a growing field due to their wide range of applications. QUAVs are complex nonlinear systems with a chaotic nature that require a controller with extended dynamics. PD and PID controllers can be successfully applied when the parameters are accurate. However, this parameterization process is complicated and time-consuming; most of the time, parameters are chosen by trial and error without guaranteeing good performance. The originality of this work is to present a novel nonlinear mathematical model with aerodynamic moments and forces in the Newton–Euler formulation, and identify metaheuristic algorithms applied to parameter optimization of compensated PD and PID controls for tracking the trajectories of a QUAV. Eight metaheuristic algorithms (PSO, GWO, HGS, LSHADE, LSPACMA, MPA, SMA and WOA) are reported, and RMSE is used to measure each dynamic performance of the simulations. For the PD control, the best performance is obtained with the HGS algorithm with an RMSE = 0.037247252379126. For the PID control, the best performance is obtained with the HGS algorithm with an RMSE = 0.032594309723623. Trajectory tracking was successful for the QUAV by minimizing the error between the desired and actual dynamics.

DESIGN OF ADAPTIVE FUZZY FRACTIONAL ORDER PID CONTROLLER FOR AUTONOMOUS UNDERWATER VEHICLE (AUV) IN HEADING AND DEPTH ATTITUDES

This paper deals with the design of new self-tuning Fuzzy Fractional Order PID (AFFOPID) controller based on nonlinear MIMO structure for an AUV in order to enhance the performance in both transient state and steady state of traditional PID controller. It is particularly advantageous when the effects of highly nonlinear processes, like high maneuver, parameters variation, have to be controlled in presence of sensor noises and wave disturbances. Aspects of AUV controlling are crucial because of Complexity and highly coupled dynamics, time variety and difficulty in hydrodynamic modeling. In this try, the comprehensive nonlinear model of AUV is derived through kinematics and dynamic equations. The scaling factor of the proposed AFFOPID Controller is adjusted online at different underwater conditions. Combination of adaptive fuzzy methods and PID controllers can enhance solving the uncertainty challenge in the PID parameters and AUV parameter uncertainty. The simulation results show that developed control system is stable, competent and efficient enough to control the AUV in path following with stabilized and controlled speed. Obtained results demonstrate that the proposed controller has good performance and significant robust stability in comparison to traditional tuned PID controllers.

A simulation approach for closed-loop control of coupled four tank system

The coupled tank system is the most widely used sub-component in chemical process industries. Fluid mixing is a major step in chemical processes that alters the material properties and cost. Fluid flow and its level regulation between several tanks are important control problems. As the first step, this paper addresses the level regulation problem using classical integer order proportional, derivative, integral (PID), fractional order PID controllers. As a second step, model-based robust fractional order controllers are derived using sliding mode approach in order to achieve the desired response, parameters of the proposed controllers are tuned using genetic algorithm. Finally, system performance under all variants of control schemes has been tested using numerical simulations.