Speed Tracking Accuracy Research Articles

A Control Strategy for Two-stage PV Grid-connected Inverter Based on Voltage Oriented Control

For the design of three-phase two-stage grid-connected PV inverter, the topology and control strategy of two-stage grid-connected inverter are analyzed. For the DC/DC converter control strategy, the shortcoming of conventional perturbation and observation method are analyzed, which is difficult to obtain high response speed and steady-state tracking accuracy due to its fixed disturbance step size. A duty cycle self-optimizing MPPT method is adopted and effectively alleviates the contradiction between tracking speed and accuracy. For the DC/AC converter control strategy, the grid voltage oriented vector control is adopted based on the analysis of the mathematical model of rotating coordinate. The current decoupling and grid voltage feedforward control in dq coordinate system are carried out. The control system parameters are designed and verified to be feasible.

Trajectory tracking of 4-DOF assembly robot based on quantification factor and proportionality factor self-tuning fuzzy PID control

When the manipulator performs the assembling task, the performance of the system is easily affected by external interference and parameter changes, resulting in slow tracking response speed and low tracking accuracy. In order to improve the stability and robustness of the controller, a self-tuning fuzzy PID controller with quantification factor and proportionality factor is designed based on the fuzzy PID control. The parameters of the PID controller are dynamically fine-tuned by the fuzzy controller 1, and the quantification factor and proportionality factor are adjusted online by the modifying fuzzy controller 2 to fine-tune the parameters of PID controller. The control effect and robustness of the improved fuzzy PID controller to the manipulator system are simulated and verified on the MATLAB/Simulink platform. The simulation results show that the proposed method significantly improves the transient response speed, tracking accuracy and follower characteristics of the system, and has good dynamic performance.

Trajectory tracking of 4-DOF assembly robot based on quantification factor and proportionality factor self-tuning fuzzy PID control

When the manipulator performs the assembling task, the performance of the system is easily affected by external interference and parameter changes, resulting in slow tracking response speed and low tracking accuracy. In order to improve the stability and robustness of the controller, a self-tuning fuzzy PID controller with quantification factor and proportionality factor is designed based on the fuzzy PID control. The parameters of the PID controller are dynamically fine-tuned by the fuzzy controller 1, and the quantification factor and proportionality factor are adjusted online by the modifying fuzzy controller 2 to fine-tune the parameters of PID controller. The control effect and robustness of the improved fuzzy PID controller to the manipulator system are simulated and verified on the MATLAB/Simulink platform. The simulation results show that the proposed method significantly improves the transient response speed, tracking accuracy and follower characteristics of the system, and has good dynamic performance.

Soft‐computing techniques for cruise controller tuning for an off‐road electric vehicle

Speed controllers may be employed to provide safer along with more secure vehicles. They may also be used to minimise environmental pollution, e.g. speed controllers can be employed to track speed optimal velocity profile based on energy consumption minimisation. Consequently, accurate speed tracking is important. However, despite soft-computing techniques have been proved successful in controller tuning, there is a limited amount of research on these techniques applied to speed controller optimisation. Therefore, this study performs a comparison study on PI cruise controller tuning for an off-road electric vehicle. A cost function is designed to reach an accurate EV speed tracking while considering safety aspects, such as no reverse speed. The ACO-NM algorithm has been demonstrated to be the most efficient compared to GA, ALO, DE, and PSO. Indeed, ACO-NM reached high-quality solutions for lower computational cost for three driving cycles. Moreover, contrary to the majority of published work on the subject, experimental validations have been carried out with the optimised PI cruise controllers. The experimental results have validated the ACO-NM efficiency with a maximum overshoot average <10% for the hardest acceleration of the real driving cycle.

Distributed optimal control for multiple high-speed train movement: An alternating direction method of multipliers

This study systematically investigates the distributed optimal control of multiple high-speed train movement to overcome communication constraints and realize efficient speed control. A dynamic multiple train movement model is constructed to capture the dynamic evolution of trains in real-world operations. With the coupling constraints for the safe distance headway among neighboring trains, an optimal control problem is formulated to improve speed and distance tracking accuracy for each train and reduce energy consumption. Based on the dual decomposition technique, each train is regarded as a subsystem equipped with a local controller while a centralized entity manages the subsystem via the prices associated with the coupling constraints. Within the framework of an alternating direction method of multipliers and a model predictive control method, a novel distributed optimal control algorithm based on a distributed message passing mechanism is designed. The algorithm divides the original complex optimal control problem into many smaller optimal control problems that can be computed in parallel and satisfies the real-time control requirement. Numerical examples are provided to illustrate the effectiveness of the proposed train control model and methods.

Active Disturbance Rejection Control for a Class of Non-affine Nonlinear Systems via Neural Networks

An active disturbance rejection controller based on radial basis function (RBF) neural network is proposed for a class of non-affine nonlinear systems in this paper. In which, the active disturbance rejection control (ADRC) is utilized to estimate the full system state and RBF neural network is used to approximate the reference signal. It is proved that, the composite controller has faster response speed and higher tracking accuracy, which effectively improves the control performance of the system and alleviates the adverse effects caused by strong coupling in the nonlinear dynamic system. Effectiveness of the proposed method is verified by MATLAB simulation.

Synchronous Reluctance Motor Speed Tracking Using a Modified Second-Order Sliding Mode Control Method

A modified second-order sliding mode control (MSOSMC) combined with radial basis function (RBF) network estimator is developed and proposed to achieve accurate speed tracking performance for synchronous reluctance motor (SynRM). The dynamic model of SynRM system has the properties of parameter variations, external disturbance, and nonlinear friction force. The MSOSMC method that utilizes continuous control input is applied to reduce the chattering phenomenon. Also, this method utilizes two sliding surfaces to solve the problem of system uncertainty and reduce motor power consumption. The RBF network is developed in MSOSMC scheme to estimate the lumped uncertainty in an on-line fashion. The proposed MSOSMC method uses the system error and control input as the convergence criteria. The adaptation scheme adjusts the parameter vectors based on the Lyapunov theorem approach, so that the asymptotic stability of the developed motor system can be guaranteed. Experimental results show that the MSOSMC structure achieves the better tracking performances in terms of root-mean-square error compared with the traditional SOSMC method under different speed tracking conditions.

Modified P&amp;O MPPT algorithm for optimal power extraction of five-phase PMSG based wind generation system

In this study, the maximum power point tracking (MPPT) performance is investigated based on variable-speed wind energy conversion system below the rated wind speed. The proposed modified perturb and observe (MPO) MPPT enhances the initial speed-tracking accuracy and eliminates the oscillations level. The MPO employs four sectors operation using a variable-speed step sizes by comparing the new suggesting curve with the P–ω curve. The system is described as a large-scale five-phase permanent magnet synchronous generator (PMSG) which is grid connected through a back-to-back converter (BTBC) and Dc-link capacitor. The field-oriented control (FOC) is applied in the machine-side converter (MSC) to extract the optimal generated power using the proposed MPPT algorithm. Moreover, the voltage-oriented control (VOC) is applied in the grid-side converter (GSC) to regulate the Dc-link voltage and inject active power with unity power factor. The simulation results depict the superior performance of the MPO over the conventional P&O. The performance of the proposed control scheme is validated using MATLAB/SIMULINK program.

Nonlinear Direct Torque Control of Interior Permanent Magnet Synchronous Motor Drive

This paper presents a nonlinear direct torque control (NDTC) strategy of interior permanent magnet synchronous motors (IPMSMs) for electric vehicle (EV) propulsion. The proposed NDTC scheme applies a nonlinear model of IPMSM to dynamically determine the optimal switching states that optimize the EV drivers’ decision to reduce the workload. Moreover, the proposed NDTC method has a simple control structure and can explicitly handle system constraints and nonlinearities. The performance evaluation is conducted via a prototype IPMSM test-bed with a TMS320F28335 DSP. Comparative experimental results provide the evidence of improvements of the proposed NDTC strategy over the conventional DTC strategy by indicating a fast torque response and an accurate speed tracking even under rapid speed change conditions.

Controller Design for Different Electric Tail Rotor Operating Modes in Helicopters

The nonlinear aerodynamics and new kinds of operation associated with helicopter electric tail rotors (ETRs) make accurate speed tracking control under complex flight conditions a key challenge confronting designers. In this paper, we present an electric propulsion system for tail rotors that uses a high-power-density permanent magnet motor. The management of aerodynamic disturbance rejection and accurate speed control are aspects of ETR design that require particularly close attention. To this end, we have developed a speed controller that is based on an active disturbance rejection control (ADRC) technique that can handle fixed speed and adjustable pitch-angle modes. We have also applied a linear extended state observer (LESO) with a self-tuning bandwidth to estimate fluctuations in the drive system. For variable speeds, a simple controller combined with an adaptive radial basis function (RBF) observer and nonlinear state error feedback using ADRC was designed to replace LESO while avoiding any dependence on the system parameters. The stability of the controllers was analyzed and their effectiveness was verified using a simulation platform. Test results showed that the propulsion system is able to achieve fast dynamic response and aerodynamic disturbance rejection.

Iterative Data-Driven Fractional Model Reference Control of Industrial Robot for Repetitive Precise Speed Tracking

Nowadays, industrial robots have been widely used in manufacturing applications; however, their speed is hard to control precisely due to unknown system dynamics. Instead of struggling to get an accurate explicit model, this paper addresses this challenge by proposing a new iterative data-driven fractional model reference control (FMRC) method, which tunes an adaptive controller to ensure that each robot actuation system behaves closely to a reference model with desirable system behavior to be obtained. This method utilizes input–output measurements without requiring an identified model or accessing the plant through specific experiments. A multiple degrees-of-freedom FMRC method with self-learning ability is designed to iteratively reach the optimal control parameters such that an accurate speed tracking is attained for each actuator. Constraints on the input signal are also considered to enhance the system robustness against external disturbances. The convergence, asymptotic accuracy, and stability of the designed control system are analyzed theoretically. Experimental results indicate that the proposed FMRC method is able to achieve a higher tracking precision and better robustness for the industrial robot compared with conventional methods.

Research on Neural Network MPPT Algorithm Based on DE and Dichotomy

Aiming at the shortcomings of traditional MPPT method, such as slow tracking speed and oscillation at maximum power point, this paper combines neural network with dichotomy to propose a new maximum power point tracking method for photovoltaic power generation system. And the traditional neural network uses the gradient descent method to solve the problem that the parameters are easy to enter the local optimal solution. In this paper, the improved differential evolution method is used to solve the global optimal solution. The neural network is used to track the vicinity of the maximum power point, and then the dichotomy is used to further approach the maximum power point. The simulation results show that compared with the traditional MPPT method, BP neural network and dichotomy can track the maximum power point faster, avoid the oscillation phenomenon, and have faster tracking speed and higher tracking accuracy.

Optimization of direct torque controlled induction motor drive by golden section method

A search control based energy loss minimization technique is proposed for Direct Torque Control (DTC) of three phase Induction Motor (IM) with Fuzzy Logic Controller (FLC) used in Electric Vehicles (EV). The performance parameters of the EV considered for analysis are Dynamic performance and Efficiency. DTC is used to achieve the excellent dynamic performance of the IM drive. FLC is employed to achieve accurate speed tracking and enhance the ruggedness against disturbance and uncertainties. Loss minimization using Golden section method is proposed to search optimum level of flux value for DTC of induction motor drive. This will lead to minimum core loss and enhanced efficiency of the system. The proposed work is simulated in MATLAB/SIMULINK. The simulation and experimental results are presented to validate the performance of the proposed loss minimization algorithm.

Simplified Speed Control of Permanent Magnet Synchronous Motors Using Genetic Algorithms

In this paper, a simplified control scheme is introduced for permanent magnet synchronous motors (PMSMs). The control strategy consists of a direct voltage controller that capitalizes on the motor's model to achieve accurate speed tracking. As such, no explicit currents loop regulation is needed which simplifies the control structure and unlike other control strategies, no motor's parameter knowledge, voltage or current transducer is required. But, the absence of current regulation loops yields higher energy consumption, which limits the motor's range of operation. Therefore, a genetic algorithm is presented to determine control gains with optimal current consumption ensuring operation at the full range of the machine. Simulation and experimental results for different situations highlight the performance of the proposed controller in transient, steady-state, and standstill conditions. Furthermore, the simplicity of the control scheme makes it a good candidate for a low-cost implementation of real-time PMSM drives.

Application of fuzzy PID controller for valveless hydraulic system driven by PMSM

For the disadvantages of low efficiency, slow speed response and unsteady speed regulation performance in the application of traditional motor drive in valve less hydraulic system, A vector controlled permanent magnet synchronous motor (PMSM) is proposed to replace the conventional motor to drive the quantitative pump in the valveless system, and a mathematical model of the valveless hydraulic system is established. In view of the non-linear and time-varying problems in the control process of the valve free hydraulic system designed, the traditional PID is difficult to solve. So the fuzzy PID position controller based on the system designed is designed. The joint simulation of the hydraulic system is carried out by using AMESim and Matlab software, and the simulation results are compared with the simulation results with traditional PID. The joint simulation results show that the fuzzy PID control method has a good effect on response speed, anti-interference and position tracking accuracy.

Sensorless Vector Control of Permanent Magnet Synchronous Linear Motor Based on Self-Adaptive Super-Twisting Sliding Mode Controller

Due to its own structural characteristics, permanent magnet synchronous linear motors (PMSLMs) have unstable motor parameters during operation due to the end effect and the inherent magnetic circuit instability. At the same time, linear motors have the characteristics of reciprocating switching motion and high-speed positioning motion in industrial applications, thus placing high demands on the controller. In order to improve the performance of the PMSLM, this paper firstly studies the linear motor structure, the distribution characteristics of its air gap magnetic field are obtained, and the simulation analysis for the back electromotive force (EMF) of PMSLM of its running process is carried out. Then, a state observer based on the sliding mode state observer (SMO) is built to replace the traditional speed sensor and a super-twisting sliding mode controller is designed. The double-closed loop control of the speed and the use of high-order sliding mode control features effectively reduce the chattering of the system and reduce the impact of the observation error brought by the observer on the system. The adaptive sliding mode control strategy is used to effectively suppress the influence of the boundaryless uncertainty interference on the system and reduce the influence of the observation error brought by the observer on the system. This paper also combines the linear motor experimental platform to further verify the control scheme based on the super-twisting sliding mode non-sensing linear motor. The simulation and experimental results show that SMO has accurate speed and position tracking performance, the system's chattering is significantly reduced, the control precision is excellent, and the introduction of adaptive sliding mode controller enhances the robustness of the system.

Adaptive robust speed control for continuously variable transmission in wind turbine under grid faults

For the grid-connected wind turbine, the frequency converter is used to connect the generator and the grid, which makes the power quality weaker than the conventional synchronous generator. To solve such a problem, a continuously variable transmission (CVT) can be connected at the high speed end of the gearbox to obtain the constant speed output at varying wind speeds for the generator. The frequency converter can then be neglected. However, shafts of the CVT suffer the external disturbances caused by grid faults. The speed tracking accuracy of the CVT can be affected seriously. So, this paper presents the adaptive robust speed control (ARSC) for the CVT, which regulates the speed output through the permanent magnet synchronous motor (PMSM). Model uncertainties of the PMSM, such as the inertia load, friction force, and external disturbances, are considered in the control scheme. The designed controller guarantees high performance and speed tracking accuracy at different wind speeds and simulated grid faults. The experimental results demonstrate the proposed CVT controller at varying wind speeds simulated via the software of FAST and the simulation of grid faults, such as one phase short-circuit and sudden load fluctuation, at a built testbed, which is used to emulate the wind turbine with the CVT. For the comparison, the PID controller for the CVT is then applied in the testbed. The results show that the ARSC method owns better performance and speed tracking accuracy than the PID method. In order to verify the utility scale for the ARSC based CVT, we build a Simulink program for a 1.5 MW wind turbine with the CVT. Simulation results show that the ARSC based CVT still owns high speed tracking accuracy in the large scale wind turbine.

A Novel and Fast MPPT Method Suitable for Both Fast Changing and Partially Shaded Conditions

In this study, a novel and fast maximum power point tracking (MPPT) algorithm for a photovoltaic generation system is proposed. The main idea is to remove the random number in the voltage calculation equation of the conventional cuckoo search method. The advantages of the proposed method include 1) fast maximum power point (MPP) tracking speed and high MPP tracking accuracy under both uniform insolation and partially shaded conditions (PSCs), 2) simple MPPT structure, and 3) consistent solution can be achieved with only three particles and only one parameter is required to be tuned. In order to validate the effectiveness and correctness of the proposed method, both simulation and experiments are carried out on a 300 W prototyping circuit. According to the experimental results, the proposed MPPT method can improve the tracking time by 46.42% and 11.76% comparing to conventional perturb and observe (P&O) and variable-step P&O technique, respectively. In addition, the proposed method can successfully track the global MPP in 249 of 252 different PSC patterns. The tracking accuracies under both uniform irradiance and PSC conditions are all higher than 99.8%.

Longitudinal speed control of autonomous vehicle based on a self‐adaptive PID of radial basis function neural network

The tracking accuracy of speed plays a significant role in the autonomous vehicle's control and safety management. In this study, we presented a novel method called self-adaptive proportional integral derivative (PID) of radial basis function neural network (RBFNN-PID) which is shown with improved longitudinal speed tracking accuracy for autonomous vehicles. A forward simulation model of longitudinal speed control for autonomous vehicles is established based on the driver model of self-adaptive RBFNN-PID and the vehicle dynamics model. Based on that, we used the traditional PID and fuzzy control methods as benchmarks to demonstrate the edge of the self-adaptive RBFNN-PID control under the new European driving cycle. Simulation results show the RBFNN-PID method is significantly more precise than the comparing groups, with a reduced error in the range of [-0.369, 0.203] m/s. The vehicle performance gives better ride comfort as well. In all, self-adaptive RBFNN-PID is proven to be effective in longitudinal speed control of autonomous vehicles and significantly outperforms the other two methods.

Comprehensive evaluation method for performance of unmanned robot applied to automotive test using fuzzy logic and evidence theory and FNN

In order to obtain reliable and exact evaluation, a new comprehensive evaluation method for performance of an unmanned robot applied to automotive test (URAT) using fuzzy logic, evidence theory and fuzzy neural network (FNN) is presented in this paper. Throttle repeatability, speed tracking accuracy, speed repeatability, driving shock degree are used as the system evaluation index. The subjective evaluation results with various expressions are quantified using fuzzy logic. The group decision making with quantified subjective evaluation results from various drivers is combined through evidence theory. The objective evaluation indexes measured by instrumentation and the corresponding combined subjective evaluation are self-learned and trained with FNN. The comprehensive performance evaluation system of the URAT is established. Finally, real vehicle experiments are conducted. The effectiveness of the presented method for the URAT is experimentally verified.