Adaptive Fuzzy Neural Network PID Algorithm for BLDCM Speed Control System

Because of its simple structure, high efficiency, low noise, and high reliability, the brushless direct current motor (BLDCM) has an irreplaceable role compared with other types of motors in many aspects. The traditional proportional integral derivative (PID) control algorithm has been widely used in practical engineering because of its simple structure and convenient adjustment, but it has many shortcomings in control accuracy and other aspects. Therefore, in this paper, a fuzzy single neuron neural network (FSNNN) PID algorithm based on an automatic speed regulator (ASR) is designed and applied to a BLDCM control system. This paper introduces a BLDCM mathematical model and its control system and designs an FSNNN PID algorithm that takes speed deviation e at different sampling times as inputs of a neural network to adjust the PID parameters, and then it uses a fuzzy system to adjust gain K of the neural network. In addition, the frequency domain stability of a double closed loop PID control system is analyzed, and the control effect of traditional PID, fuzzy PID, and FSNNN PID algorithms are compared by setting different reference speeds, as well as the change rules of three-phase current, back electromotive force (EMF), electromagnetic torque, and rotor angle position. Finally, results show that a motor controlled by the FSNNN PID algorithm has certain superiority compared with traditional PID and fuzzy PID algorithms and also has better control effects.

Determine the parameters of PID algorithm in liquid level control controlled by PLC s7 1500 using 3d virtual reality model

It is very common to stabilize the preset value (Wanted value) of analog signals such as temperature, pressure, weight, flow, speed in automatic control. However, these control objects often have some problems such as overshooting, taking a long time to bring the system to a steady value, and large errors. One of the most used systems to overcome these problems is the PID, which is a preset stabilizing system with a quick function that returns the system to the set value in a short time without overshooting. error is close to zero. However, determining the scale parameters Ki, integral Kp, and differential Kd for the system to work optimally is a problem that needs to be studied. This paper presents how to accurately determine differential, integral, and scale coefficients according to 3D virtual reality model. Used a lot in simulation modeling for training and practical applications.

Efficient Reservoir Modelling for Flood Regulation in the Ebro River (Spain)

The vast majority of reservoirs, although built for irrigation and water supply purposes, are also used as regulation tools during floods in river basins. Thus, the selection of the most suitable model when facing the simulation of a flood wave in a combination of river reach and reservoir is not direct and frequently some analysis of the proper system of equations and the number of solved flow velocity components is needed. In this work, a stretch of the Ebro River (Spain), which is the biggest river in Spain, is simulated solving the Shallow Water Equations (SWE). The simulation model covers the area of river between the city of Zaragoza and the Mequinenza dam. The domain encompasses 721.92 km2 with 221 km of river bed, of which the last 75 km belong to the Mequinenza reservoir. The results obtained from a one-dimensional (1D) model are validated comparing with those provided by a two-dimensional (2D) model based on the same numerical scheme and with measurements. The 1D modelling loses the detail of the floodplain, but nevertheless the computational consumption is much lower compared to the 2D model with a permissible loss of accuracy. Additionally, the particular nature of this reservoir might turn the 1D model into a more suitable option. An alternative technique is applied in order to model the reservoir globally by means of a volume balance (0D) model, coupled to the 1D model of the river (1D-0D model). The results obtained are similar to those provided by the full 1D model with an improvement on computational time. Finally, an automatic regulation is implemented by means of a Proportional-Integral-Derivative (PID) algorithm and tested in both the full 1D model and the 1D-0D model. The results show that the coupled model behaves correctly even when controlled by the automatic algorithm.

Braking angle control of loom spindle based on neuron-adaptive PID algorithm

The intelligent control strategy of electromagnetic clutch actuator is analyzed in detail in this paper. The start - stop control of the loom is realized by an electromagnetic clutch. The existing control method of electromagnetic clutch of loom is high and low pressure control strategy. The operator sets the braking advance angle according to experience, to realize the accurate braking of the spindle, but it is difficult to realize the fast and accurate control. In order to achieve good performance, it is very important to develop a fast and accurate loom braking system. Aiming at the fabric defects caused by the elongation of the warp when the loom is stopped, a method of stabilizing the excitation current of the electromagnetic clutch by using the neural adaptive PID (proportional integral differential) controller is proposed to improve the control precision of the actuator. The experimental results show that the proposed control algorithm is feasible and can effectively realize the adaptive control of the spindle braking Angle within the allowable error range.

Universal single-board computer based control unit for biomedical test benches

Continuous adaptation of international standards for medical devices requires recurrent modification of test benches. A universal control unit using an open software environment is presented to simplify the maintenance of various test benches. Our developed control unit is equipped with a Raspberry Pi 4, with standard communication interfaces and application-specific electronic assemblies. The software is based on Node-RED, a browser-based editor. A measuring setup was adapted for a flow perfusion system. Our control unit simplifies the handling of the flow perfusion system by controlling a hydraulic pump and all required valves. A software programmed PID algorithm adapts the speed of the pump to adjust the pressure automatically. Actuators like proportional pinch valves are handled to control volumetric flow and pressure within the circulation. Consequently, the user directly observes changes inside the system. The measured data are stored and are available for documentation.

Smart aerosonde UAV longitudinal flight control system based on genetic algorithm

Synthesis of a flight control system for such an aircraft that achieves stable and acceptable performance across a specified flying envelope in the presence of uncertainties represents an attractive and challenging design problem. This study uses the genetic self-tuning PID algorithm to develop an intelligent flight control system for the aerosonde UAV model. To improve the system's transient responses, the gains of the PID controller are improved using a genetic algorithm (GA). Simulink/MATLAB software is used to model and simulate the proposed system. The proposed PID controller integrated with the GA is compared with the classical one. Three simulation scenarios are carried out. In the first scenario, and at normal conditions, the proposed controller performance is better than the classical one. While in the second scenario, identical results are achieved from both controllers. Finally, in the third scenario, the PID controller with GA shows the robustness and durability of the system compared with the classical PID in presence of external wind disturbance. The simulation results prove the system parameters optimization.

Application of embedded soft PLC in the control system of rapier loom

At present, the rapier loom has gradually become the mainstream equipment in the manufacturing industry. In order to make the rapier loom realize automated production and further improve the production efficiency of the rapier loom, improve the programmability of the system, and reduce the cost of system maintenance. The thesis developed a rapier loom control system based on embedded soft PLC, and carried out experiments and applications in the field. The contribution and innovation of this paper is to develop a complete low-cost control system, and through a genetic algorithm optimized PID algorithm to complete the more effective control of the loom tension system. The embedded soft PLC system proposed in this paper reduces the overall maintenance cost of the system and improves the programmability of the system. This text carries on the systematic scheme design to the embedded soft PLC from the hardware system and the software system respectively. First, according to the actual requirements, this article designs the overall scheme of the embedded software PLC hardware system with STM32F407ZGT6 as the core. Then this article is based on the embedded soft PLC hardware platform, according to the international standard of industrial control programming, writes the embedded soft PLC low-level driver software. Secondly, this article analyzes the factors that affect the warp tension during the operation of the rapier loom, and proposes the use of genetic algorithm to optimize the warp tension control method of the traditional PID algorithm. Finally, we conducted verification tests and on-site application debugging for the entire set of rapier loom embedded soft PLC control system. We controlled the warp tension as the main experimental object. The results show that this control system effectively improves the control accuracy of the warp tension of the rapier loom and meets the actual needs of industrial applications. The whole system has a good application prospect in the warp tension control of rapier looms.

Nonlinear energy-based control of soft continuum pneumatic manipulators

This paper investigates the model-based nonlinear control of a class of soft continuum pneumatic manipulators that bend due to pressurization of their internal chambers and that operate in the presence of disturbances. A port-Hamiltonian formulation is employed to describe the closed loop system dynamics, which includes the pressure dynamics of the pneumatic actuation, and new nonlinear control laws are constructed with an energy-based approach. In particular, a multi-step design procedure is outlined for soft continuum manipulators operating on a plane and in 3D space. The resulting nonlinear control laws are combined with adaptive observers to compensate the effect of unknown disturbances and model uncertainties. Stability conditions are investigated with a Lyapunov approach, and the effect of the tuning parameters is discussed. For comparison purposes, a different control law constructed with a backstepping procedure is also presented. The effectiveness of the control strategy is demonstrated with simulations and with experiments on a prototype. To this end, a needle valve operated by a servo motor is employed instead of more sophisticated digital pressure regulators. The proposed controllers effectively regulate the tip rotation of the prototype, while preventing vibrations and compensating the effects of disturbances, and demonstrate improved performance compared to the backstepping alternative and to a PID algorithm.