Classical PID Algorithm Research Articles

A Novel Fuzzy-SAE Control Method for an Improved Test Wind Tunnel Simulating Sand/Dust Environment

The sand/dust environment is an important cause of aircraft failure. A sand/dust environment simulation experiment must be devised to meet the standard technical requirements. Therefore, this article designs the control system for a sand/dust environment test tunnel, including a wind speed control system and a pneumatic conveying and concentration control system. A fuzzy intelligent control method and a deep neural network are used to track and control experimental parameters. Compared to the classic PID algorithm, this method achieves smaller overshoot, faster response speed, no steady error and a better dynamic response curve, as demonstrated by both the test result in the wind tunnel and a simulation result. Both the classic PID control method and the high-precision fuzzy control method are fast, stable, and robust. The fuzzy-SAE intelligent control method not only has the high accuracy of the classic PID control method but also has the high speed, stability, and robustness of fuzzy control, which can meet the intelligent control requirements of the sand/dust environment test equipment.

Self-Adaptive PID Control Based on RBF Network for Trajectory Tracking of Dual-Mass Servo System

This paper presents a state-of-the-art algorithm of self-adaptive RBF network PID control and aims at achieving the precise and fast trajectory tracking for a dual-mass servo system. To increase the robustness of servo system parameter varying and external disturbances, a classical PID algorithm with enhanced structure based on RBF neural network is proposed. Extensive simulations show that it practically validates the superiority of the proposed RBF adaptive PID controller. The experiment was simulated respectively under the external disturbance, which indicates that accurate tracking performance of the servo system with dual-mass load has been achieved and also verifies the effectiveness of self-adaptive PID control strategy.

The Chassis Design of the Swerveomni Directional Wheel

For the design of the FRC race chassis, this project uses the official motor and parts as materials and Autodesk Inventor for modeling to design and produce a four-wheel drive, independently controlled motor chassis structure. The power system of the chassis structure contains two motors, one of which is the Falcon 500 as the drive motor of the power system, and the other is the 775 pro chosen as the drive motor of the steering system. The drive system consists of a gear train containing spur gears and bevel gears. The control system uses a classical PID algorithm to achieve accurate overall robot motion by outputting a specific amount of motor rotation. In the project, a single Swerve drive module was designed and built, and its performance was tested on a mobile platform equipped with multiple Omni-directional wheels.

An Approach to Industrial Automation Based on Low-Cost Embedded Platforms and Open Software

This paper presents a performance evaluation of the development of the instrumentation, communications and control systems of a two-tank process by using low-cost hardware and open source software. The hardware used for automating this process consists of embedded platforms (Arduino and Raspberry Pi) integrated into programmable logic controllers (PLCs), which are connected to a supervisory control and data acquisition (SCADA) system implemented with an open source Industrial Internet of Things (IIoT) platform. The main purpose of the proposed approach is to evaluate low-cost automation solutions (hardware and software) within the framework of modern industry requirements in order to determine whether these technologies could be enabling factors of IIoT. The proposed control strategy for regulating tank levels combines the classic PID algorithm and the fuzzy gain scheduling PID (FGS-PID) approach. Fault detection capabilities are also enabled for the system through a fault detection and diagnosis module (FDD) implemented with an extended Kalman filter (EKF). The distributed controller’s (DC) algorithms are embedded into the PLC’s processors in order to demonstrate the flexibility of the proposed system. Additionally, a remote human to machine interface (HMI) is deployed through a web client of the IIoT application. Experimental results show the proper operation of the overall system.

Research on thrust vector control of nonlinear solid rocket motor nozzle based on active disturbance rejection technology

In this paper, based on the auto disturbance rejection control technology, the electromechanical servo system is used as the control actuator, and the thrust vector control of the solid rocket motor nozzle with typical nonlinear friction characteristics is studied and analyzed. In this paper, the realization of the classical PID algorithm and the lack of dynamic performance are analyzed, and then the compensation algorithm based on the auto disturbance rejection control technology is added. The algorithm compensates for the phase lag of the system due to the nonlinear friction characteristics. As a result, the frequency characteristics of the system have been significantly improved.

Compare of Transient Quality in Automatic Control Systems with Classic PID Algorithm and Optimal Regulator

Currently, about 90–95% of generic controllers use the PID algorithm to generate control actions, while 64% of the PID controllers are used in single-circuit automatic control systems. Most of industries (power industry among them) use hundreds of automatic control systems. The quality of their work is the basis of economic efficiency of technical processes, ensuring safety, reliability, durability and environmental friendliness of both technological equipment and automation equipment. There are different modifications of PID-controller structure implementation. In practice the ideal PID controller with a filter and the classic PID regulator (serial connection of the ideal PI controller and the real PD regulator as the direct action elements) are widely used. The problem of choosing a rational structure and a method of parametric optimization of PID controllers, which provide the best direct indicatives of the quality in the development of the main effects in single-circuit automatic control systems, becomes urgent. However, only for the classical PID controllers, which are widely used at present, there are more than three hundred methods for adjusting the three parameters of the optimal dynamic adjustment, as well as the ballast time constant. This results in arising a problem of substantiation of the best structure and method of parametric optimization of classical PID regulators. As a basic option, one of the simplest and most obvious one, viz. the method of automated adjustment of the controller in the Simulink MatLab environment had been chosen, which was compared with the method of full compensation in general for objects with a transfer function in the form of an inertial link with a conditional delay. Two variants of control action realization on the basis of the structural scheme of the optimal regulator developed by the Belarusian national technical University were also offered. In contrast with the classic PID controller, the optimal controller has one parameter of dynamic adjustment setting. The results of simulation of transients at basic perturbations confirmed that the best direct indicatives of the quality are provided with an optimal regulator, which makes it possible to recommend it for wide implementation instead of the classic PID controllers.

A course control system of unmanned surface vehicle (USV) using back-propagation neural network (BPNN) and artificial bee colony (ABC) algorithm

Course control is the basis of Unmanned Surface Vehicle (USV) control system, largely determines the performance of USV. Because of some uncontrollable factors such as wind, wave and other random disturbances, the course control of an Unmanned Surface Vehicle (USV) is always difficult. In recent years, there have been some studies of adaptive course control system for USV, but they are not precise enough for engineering practice. In this paper, we propose a novel adaptive course control method based on Back-propagation Neural Network (BPNN) and Artificial Bee Colony (ABC) algorithm. We use classic PID algorithm as the main course control algorithm, and back-propagation neural network (BPNN) is also utilized to achieve more effective self-adaptive PID control. At the same time, in order to improve the convergence speed and precision of BPNN, we bring in Artificial Bee Colony (ABC) algorithm to minimize the error of system and adjust the weight of BPNN. The system has been proven in simulation that it can accurately output the rudder angle according to the input course angle and can perform better than that without ABC algorithm optimization. The error of parameters obtained by this method is within the acceptable range, which can provide reference for engineering practice.

An intelligent control method for a large multi-parameter environmental simulation cabin

The structure and characteristics of a large multi-parameter environmental simulation cabin are introduced. Due to the difficulties of control methods and the easily damaged characteristics, control systems for the large multi-parameter environmental simulation cabin are difficult to be controlled quickly and accurately with a classical PID algorithm. Considering the dynamic state characteristics of the environmental simulation test chamber, a lumped parameter model of the control system is established to accurately control the multiple parameters of the environmental chamber and a fuzzy control algorithm combined with expert-PID decision is introduced into the temperature, pressure, and rotation speed control systems. Both simulations and experimental results have shown that compared with classical PID control, this fuzzy-expert control method can decrease overshoot as well as enhance the capacity of anti-dynamic disturbance with robustness. It can also resolve the contradiction between rapidity and small overshoot, and is suitable for application in a large multi-parameter environmental simulation cabin control system.

Design of Attitude Control System for Quadrotor

Quadrotor is a widely used aircraft now and be with the nonlinear, uncertain characteristics. Because of requirement on high attitude stability the attitude control system of quadrotor needs to be well designed. Firstly the movement pattern and working principle of the quadrotor are analyzed. Then the hardware schematic diagram of attitude control system is designed which includes several sensors, the master controller, power supply module and so on. Sensors communicate with the master controller mainly through the IC interfaces. Thirdly the application of Kalman filter is studied as the attitude solving algorithm for the quadrotor. Kalman filter is used to realize data fusion and the effectiveness of this algorithm is verified. Then Based on a dynamic model the classic PID algorithm is verified. At last outdoor flight tests are conducted several times. The results show the quadrotor can realize basic functions of hovering and vertical take-off and landing.

Direct Yaw-Moment Control through Optimal Control Allocation Method for Light Vehicle

This paper presents a Direct Yaw-moment Control (DYC) strategy to prevent light vehicles from entering the unsteady state and improve the handling stability. A novelty of this work is the ability to achieve superior performance through the lower workload of the actuators by using the optimal control allocation method to distribute the active yaw moment. In the main-loop, the DYC controller is designed based on the classical PID algorithm with the yaw rate and sideslip angle feedback. Simulation tests are carried out on the conditions of sine steering and single lane change steering. Results indicate that the working potential of each actuator can be fully utilized and a significant improvement in handling stability can be achieved from the DYC system.

Dynamics and Control on Double-drum Roller Maximum Brake Deceleration

Reducing start and stop time without destroying the pavement surface is important in enhancing productivity of double-drum roller and compact quality of asphalt pavement, but it is difficult in confirming the maximum brake deceleration or start acceleration of double-drum roller. Dynamic analysis is used in studying these questions, and a mathematic model of double-drum roller is established, based on the model, the factors which will affect the maximum brake deceleration of double-drum roller is studied, such as brake force from travel hydraulic travel system, the adhesive force from compacted ground, the inertia force of drums and double-drum roller itself. And the max brake deceleration equation is obtained which consider all these factors. It indicates that drum inertia affects the max brake deceleration of double-drum roller notable, and should be taken into account during design procedure and control program. At last a maximum deceleration control method based on slip control and classic PID algorithm is advised.

SOME THEORETICAL INSIGHTS INTO RECENT CONTROL DESIGN ALGORITHMS

Abstract Internal Model Control (IMC) design procedure is known to offer valuable advantages over classical control methods, especially for robust controller design. The Perfect Controller is defined using the well-known classical PID algorithm and its equivalence to IMC. After the definition of the Perfect Controller the IMC filter tuning parameter, which is used to maintain robustness, is obtained as a result of reparametrization of the classical PID control algorithm and factorization of the model. Through factorization, invertible part of the model is used directly in defining the perfect controller and the noninvertibility effects of the model are lumped into the new IMC tuning parameter. Closed loop IMC controller design is obtained with appropriate plant parameter uncertainty representation. Theoretical foundation for the transition from classical PID design to IMC design is analyzed using simple common chemical process models.

Análisis comparativo del desempeño de técnicas de control conmutado ejecutadas en dispositivos digitales

El presente artículo aborda el diseño y la implementación de estrategias de control híbrido (conmutado) para manipular la dinámica de rotación del eje un motor DC. Se presenta la implementación de técnicas no-convencionales de control por modos deslizantes y control óptimo conmutado, comparando su desempeño con el control PID convencional. Las implementaciones se realizan tanto en una arquitectura computacional configurada (microcontrolador Arduino) como en una arquitectura configurable (FPGA). De los resultados obtenidos se observa que para cualquiera de las dos plataformas de implementación, el desempeño de las técnicas no-convencionales de control es superior respecto al PID clásico, principalmente en cuanto respecta al error en estado estacionario ante la acción de perturbaciones. En términos de implementación, la técnica de control óptimo conmutado resulta ser más simple que el SMC. Análisis complementarios de robustez y optimalidad forman parte del trabajo futuro.