Adaptive Gain Tuning Rule for Nonlinear Sliding-mode Speed Control of Encoderless Three-phase Permanent Magnet Assisted Synchronous Motor

In this paper, an adaptive gain tuning rule is designed for the nonlinear sliding mode speed control(NSMSC) in order to enhance the dynamic performance and the robustness of the permanent magnet assisted synchronous reluctance motor(PMa-Syn RM) with considering the parameter uncertainties. A nonlinear sliding surface whose parameters are altering with time is designed at first. The proposed NSMSC can minimize the settling time without any overshoot via utilizing a low damping ratio at starting along with a high damping ratio as the output approaches the target set-point. In addition, it eliminates the problem of the singularity with the upper bound of an uncertain term that is hard to be measured practically as well as ensures a rapid convergence in finite time, through employing a simple adaptation law. Moreover, for enhancing the system efficiency throughout the constant torque region, the control system utilizes the maximum torque per ampere technique. The nonlinear sliding surface stability is assured via employing Lyapunov stability theory. Furthermore, a simple sliding mode estimator is employed for estimating the system uncertainties. The stability analysis and the experimental results indicate the effectiveness along with feasibility of the proposed speed estimation and the NSMSC approach for a 1.1-k W PMa-Syn RM under different speed references, electrical and mechanical parameters disparities, and load disturbance conditions.

ADAPTIVE VARIABLE STRUCTURE CONTROLLERS AND APPLICATION TO ENGINE IDLE SPEED CONTROL SIMULATION

A neural-network-based adaptive variable structure control methodology isproposed for the tracking problem of nonlinear discrete-time input-output systems. The unknowndynamics of the system are approximated via radial basis function neural networks. The control lawis based on sliding modes and simple to implement. The discrete-time adaptive law for tuning theweight of neural networks is presented using the adaptive filtering algorithm with residueupper-bound compensation. The application of the proposed controller to engine idle speed controldesign is discussed. The results indicate the validation and effectiveness of this approach.

Design of speed controller for electronic fuel injection gasoline generator based on feed-forward PID control

As for the application of electronic fuel injection （EFI） system to small gasoline generator set, mechanical speed controller cannot be coupled with EFI system and has the shortcomings of lagged regulation and poor accuracy, a feed-forward control strategy based on load combined with proportional-integral-differential （PID） control strategy was proposed, and a digital speed controller applied to the electrical control system was designed. The detailed control strategy of the controller was intro- duced. The hardware design for the controller and the key circuits of motor driving, current sampling and angular signal captu- ring were given, and software architecture was discussed. Combined with a gasoline generator set mounted with EFI system, the controller parameters were tuned and optimized empirically by hardware in loop and bench test methods. Test results show that the speed deviation of generator set is low and the control system is stable in steady state; In transient state the control system responses quickly, has high stability under mutation loads especially when suddenly apply and remove 100% load, the speed deviation is within 8% of reference speed and the transient time is less than 5 s, satisfying the ISO standard.

Vector control of induction motor based on fractional-order intelligent-integral speed controller

To improve dynamic and static performances and robustness of the induction motor speed control system based on vector control,an improved fractional-order intelligent proportional integral(IPIλ)controller was applied to the speed controller of the vector control system,which combined the intelligent fractional integral with the proportion according to the variation of deviation.Compared with proportional integral(PI)and fractional-order proportional integral(FOPI)controllers,the IPIλcontroller achieved better control performance.The stimulation results indicate that the IPIλcontroller can not only track the given speed quickly and accurately,but also have better anti-interference and robustness for load and parameters variations.

A Sensor-less Surface Mounted PMSM for Electronic Speed Control in Multilevel Inverter

Recent advancements in power electronics technology evolves inverter fed electric motors.Speed signals and rotor position are essential for controlling an electric motor accurately.In this paper,the sensorless speed control of surface-mounted permanent magnet synchronous motor(SPMSM)has been attempted.SPMSM wants a digital inverter for its precise working.Hence,this study incor-poratesfifteen level inverter to the SPMSM.A sliding mode observer(SMO)based sensorless speed control scheme is projected to determine rotor spot and speed of the multilevel inverter(MLI)fed SPMSM.MLI has been operated using a multi carrier pulse width modulation(MCPWM)strategy for generation offif-teen level voltages.The simulation works are executed with MATLAB/SIMU-LINK software.The steadiness and the heftiness of the projected model have been investigated under no loaded and loaded situations of SPMSM.Furthermore,the projected method can be adapted for electric vehicles.

Comparative Study of Conventional, Fuzzy Logic and Neural PID Speed Controllers with Torque Ripple Minimization for an Axial Magnetic Flux Switched Reluctance Motor

Three speed controllers for an axial magnetic flux switched reluctance motor with only one stator, are described and experimentally tested. As it is known, when current pulses are imposed in their windings, high ripple torque is obtained. In order to reduce this ripple, a control strategy with modified current shapes is proposed. A workbench consisting of a machine prototype and the control system based on a microcontroller was built. These controllers were: a conventional PID, a fuzzy logic PID and a neural PID type. From experimental results, the effective reduction of the torque ripple was confirmed and the performance of the controllers was compared.

PID-Type Fuzzy Controller for Grid-Supporting Inverter of Battery in Embedded Small Variable Speed Wind Turbine

Frequency and voltage of embedded variable speed wind turbine (VSWT) driving a permanent magnet synchronous generator (PMSG) is strongly affected by wind speed fluctuations. In practice, power imbalance between supply and demand is also common, especially when VSWT-PMSG is connected to a weak micro grid (MG). If load demand fluctuations become high, isolated MG may be unable to stabilize the frequency and voltage so that battery storage needs to be installed into the MG to adjust energy supply and demand. To allow flexible control of active and reactive power flow from/to battery storage, grid-supporting inverters are used. For a system that contains highly nonlinear components, the use of conventional linear proportional-integral-derivative (PID) controllers may cause system performance deterioration. Additionally, these controllers show slow, oscillating responses, and complex equations are required to obtain optimum responses in other controllers. To cope with these limitations, this paper proposes PID-type fuzzy controller (PIDfc) design to control grid-supporting inverter of battery. To ensure safe battery operating limits, we also propose a new controller scheme called intelligent battery protection (IBP). This IBP is integrated into PIDfc. Several simulation tests are performed to verify the scheme’s effectiveness. The results show that the proposed PIDfc controller exhibits improved performance and acceptable responses, and can be used instead of conventional controllers.

A Genetic Based Fuzzy Q-Learning Flow Controller for High-Speed Networks

For the congestion problems in high-speed networks, a genetic based fuzzy Q-learning flow controller is proposed. Because of the uncertainties and highly time-varying, it is not easy to accurately obtain the complete information for high-speed networks. In this case, the Q-learning, which is independent of mathematic model, and prior-knowledge, has good performance. The fuzzy inference is introduced in order to facilitate generalization in large state space, and the genetic operators are used to obtain the consequent parts of fuzzy rules. Simulation results show that the proposed controller can learn to take the best action to regulate source flow with the features of high throughput and low packet loss ratio, and can avoid the occurrence of congestion effectively.

Temperature-Based Fan Speed Optimization Strategy

As energy efficiency and indoor comfort increasingly become key standards in modern residential and office environments,research on intelligent fan speed control systems has become particularly important.This study aims to develop a temperature-feedback-based fan speed optimization strategy to achieve higher energy efficiency and user comfort.Firstly,by analyzing existing fan speed control technologies,their main limitations are identified,such as the inability to achieve smooth speed transitions.To address this issue,a BP-PID speed control algorithm is designed,which dynamically adjusts fan speed based on indoor temperature changes.Experimental validation demonstrates that the designed system can achieve smooth speed transitions compared to traditional fan systems while maintaining stable indoor temperatures.Furthermore,the real-time responsiveness of the system is crucial for enhancing user comfort.Our research not only demonstrates the feasibility of temperature-based fan speed optimization strategies in both theory and practice but also provides valuable insights for energy management in future smart home environments.Ultimately,this research outcome will facilitate the development of smart home systems and have a positive impact on environmental sustainability.

Predictive Control Model Based on the MPC Algorithm on Three Different Road Sections

Predictive control is an advanced control algorithm,which is widely used in industrial process control.Among them,model predictive control(MPC)is an important branch of predictive control.Its basic principle is to use the system model to predict future behavior and determine the current control action by optimizing the objective function.This paper discusses the application of MPC in the prediction and control of the speed of vehicles to optimize traffic flow.It is a valuable reference for alleviating traffic congestion and improving travel efficiency and smoothness and provides scientific basis and technical support for future highway traffic management.

HYDRAULIC ACTIVE GUIDE ROLLER SYSTEM FOR HIGH-SPEED ELEVATOR BASED ON FUZZY CONTROLLER

Increase of elevator speed brings about amplified vibrations of high-speed elevator. In order to reduce the horizontal vibrations of high-speed elevator, a new type of hydraulic active guide roller system based on fuzzy logic controller is developed. First the working principle of the hydraulic guide system is introduced, then the dynamic model of the horizontal vibrations for elevator cage with active guide roller system and the mathematical model of the hydraulic system are given. A fuzzy logic controller for the hydraulic system is designed to control the hydraulic actuator. To improve the control performance, preview compensation for the controller is provided. Finally, simulation and experiments are executed to verify the hydraulic active guide roller system and the control strategy. Both the simulation and experimental results indicate that the hydraulic active guide roller system can reduce the horizontal vibrations of the elevator effectively and has better effects than the passive one, and the fuzzy logic controller with preview compensation can give superior control performance.

Single Phase Induction Motor Drive with Restrained Speed and Torque Ripples Using Neural Network Predictive Controller

In industrial drives, electric motors are extensively utilized to impart motion control and induction motors are the most familiar drive at present due to its extensive performance characteristic similar with that of DC drives. Precise control of drives is the main attribute in industries to optimize the performance and to increase its production rate. In motion control, the major considerations are the torque and speed ripples. Design of controllers has become increasingly complex to such systems for better management of energy and raw materials to attain optimal performance. Meager parameter appraisal results are unsuitable, leading to unstable operation. The rapid intensification of digital computer revolutionizes to practice precise control and allows implementation of advanced control strategy to extremely multifaceted systems. To solve complex control problems, model predictive control is an authoritative scheme, which exploits an explicit model of the process to be controlled. This paper presents a predictive control strategy by a neural network predictive controller based single phase induction motor drive to minimize the speed and torque ripples. The proposed method exhibits better performance than the conventional controller and validity of the proposed method is verified by the simulation results using MATLAB software.

LOW COST IMPLEMENTATION OF SPEED SENSORLESS INDUCTION MOTOR CONTROL USING STATOR FLUX ORIENTATION

The control platform of the induction motor （IM） with low costs is developed by using DSP MC56F8013 with a good performance/price rtaio. The control algorithm for the speed sensorless IM is studied based on the stator flux orientation （SFO）. The algorithm structure is simple to be implemented and cannot be influenced by motor parameters, The improved stator flux estimation is used to compensate errors caused by the low pass filter （LPF）. A new speed regulator is designed to ensure the system working with the maximal torque in the transient state. The system simulation and the prototype experiment are made. Results show that the con- trol system has good dynamic and static performance.

INDUCTION MOTOR SPEED CONTROL SYSTEM BASED ON FUZZY NEURAL NETWORK

A fuzzy neural network controller with the teaching controller guidance and parameter regulations for vector-controlled induction motor is proposed. The design procedures of the fuzzy neural controller and the teaching controller are described. The parameters of the membership function are regulated by an on-line learning algorithm. The speed responses of the system under the condition, where the target functions are chosen as I qs and ω, are analyzed. The system responses with the variant of parameter moment of inertial J, viscous coefficients B and torque constant K tare also analyzed. Simulation results show that the control scheme and the controller have the advantages of rapid speed response and good robustness.

Speed Sensorless Vector Control of Induction Motor Based on Reduced Order Extended Kalman Filter

A speed sensorless vector control system of induction motor with estimated rotor speed and rotor flux using a new reduced order extended Kalman filter is proposed. With this method, two rotor flux components are selected as the state variables, and the rotor speed as an estimated parameter is regarded as an augmented state variable. The algorithm with reduced order decreases the computational complexity and makes the proposed estimator feasible to be implemented in real time. The simulation results show high accuracy of the estimation algorithm and good performance of speed control, and verify the usefulness of the proposed algorithm.

Design of hydraulic motor speed control system based on co-simulation of AMESim and Matlab_Simulink

In order to design an effective hydraulic motor speed control system, Matlab_Simiulink and AMESim co-simulation technology is adopted to establish more accurate model and reflect the actual system. The neural network proportion-integration-differentiation （PID） control parameters on-line adjustment is utilized to improve system accuracy, celerity and stability. Simulation results indicate that with the control system proposed in this paper, the system deviation is reduced, therefore accuracy is improved; response speed for step signal and sinusoidal signal gets faster, thus acceleration is rapidly improved; and the system can be restored to the control value in case of interfering, so stability is improved.

Speed Disturbance in the Diesel Engine Electronic Control System

In order to sample the speed signal of electronic diesel engine in real time and make the engine work reliable, the diesel engine control system's speed acquisition was studied and the problem of speed disturbance was solved. The control system was based on the 8?bit electronic control unit(ECU) system and the assembly language was used to design the software for controlling the engine fuel quantity and the turbocharger of the variable geometry turbine for the heavy duty diesel engine. By changing the timing method for speed acquisition, the problem of speed disturbance was solved and the reliability of the ECU was improved.

Speed Control of Travelling Wave Type Ultrasonic Motors Using Artificial Neural Network

Ultrasonic motor (USM) is a newly developed motor, and it has some excellent performances and useful features, therefore, it has been expected to be of practical use. However, the driving principle of USM is different from that of other electromagnetic type motors, and the mathematical model is complex to apply to motor control. Furthermore, the speed characteristics of the motor have heavy nonlinearity and vary with driving conditions. Hence, the precise speed control of USM is generally difficult. This paper proposes a new speed control scheme for USM using an artificial neural network. An accurate tracking response can be obtained by random initialization of the weights of the network owing to the powerful on line learning capability. Two prototype ultrasonic motors of travelling wave type were fabricated, both having 100 mm outer diameters of stator and piezoelectric ceramic. The usefulness and validity of the proposed control scheme are examined in experiments.

Application of High-speed Solenoid Valve to the Semi-active Control of Landing Gear

To select or develop an appropriate actuator is one of the key and difficult issues in the study of semi-active controlled landing gear. Performance of the actuator may directly affect the effectiveness of semi-active control. In this article, parallel high-speed solenoid valves are chosen to be the actuators for the semi-active controlled landing gear and being studied. A nonlinear high-speed solenoid valve model is developed with the consideration of magnetic saturation characteristics and verified by test. According to the design rule of keeping the peak load as small as possible while absorbing the specified shock energy, a fuzzy PD control rule is designed. By the rule controller parameters can be self-regulated. The simulation results indicate that the semi-active control based on high-speed solenoid valve can effectively improve the control performance and reduce impact load during landing.

Intelligent control for large-scale variable speed variable pitch wind turbines

Large-scale wind turbine generator systems have strong nonlinear multivariable characteristics with many uncertain factors and disturbances. Automatic control is crucial for the efficiency and reliability of wind turbines. On the basis of simplified and proper model of variable speed variable pitch wind turbines, the effective wind speed is estimated using extended Kaiman filter. Intelligent control schemes proposed in the paper include two loops which operate in synchronism with each other. At below-rated wind speed, the inner loop adopts adaptive fuzzy control based on variable universe for generator torque regulation to realize maximum wind energy capture. At above-rated wind speed, a controller based on least square support vector machine is proposed to adjust pitch angle and keep rated output power. The simulation shows the effectiveness of the intelligent control.