Nonlinear system PID-type multi-step predictive control

A compound neural network was constructed during the process of identification and multi-step prediction. Under the PID-type long-range predictive cost function, the control signal was calculated based on gradient algorithm. The nonlinear controller’s structure was similar to the conventional PID controller. The parameters of this controller were tuned by using a local recurrent neural network on-line. The controller has a better effect than the conventional PID controller. Simulation study shows the effectiveness and good performance.

Model Predictive Control-Based Direct Torque Control for Matrix Converter-Fed Induction Motor with Reduced Torque Ripple

To reduce the torque ripple in motors resulting from the use of conventional direct torque control(DTC),a model predictive control(MPC)-based DTC strategy for a direct matrix converter-fed induction motor is proposed in this paper.Two new look-up tables are proposed,these are derived on the basis of the control of the electromagnetic torque and stator flux using all the feasible voltage vectors and their associated switching states.Finite control set model predictive control(FCS-MPC)has then been adopted to select the optimal switching state that minimizes the cost function related to the electromagnetic torque.Finally,the experimental results are shown to verify the reduced torque ripple performance of the proposed MPC-based DTC method.

Direct Model Predictive Control of Noninverting Buck-boost DC-DC Converter

In this paper, direct model predictive control(DMPC) of the noninverting buck-boost DC-DC converter with magnetic coupling between input and output is proposed. Unlike most of the other converters, the subject converter has the advantage of exhibiting minimum phase behavior in the boost mode. However, a major issue that arises in the classical control of the converter is the dead zone near the transition of the buck and boost mode. The reason for the dead zone is practically unrealizable duty cycles, which are close to zero or unity, of pulse width modulation(PWM) near the transition region. To overcome this issue, we propose to use DMPC. In DMPC, the switches are manipulated directly by the controller without the need of PWM.Thereby, avoiding the dead zone altogether. DMPC also offers several other advantages over classical techniques that include optimality and explicit current constraints. Simulations of the proposed DMPC technique on the converter show that the dead zone has been successfully avoided. Moreover, simulations show that the DMPC technique results in a significantly improved performance as compared to the classical control techniques in terms of response time, reference tracking, and overshoot.

Predictive direct power control of three-phase PWM rectifier based on TOGI grid voltage sensor free algorithm

In predictive direct power control(PDPC)system of three-phase pulse width modulation(PWM)rectifier,grid voltage sensor makes the whole system more complex and costly.Therefore,third-order generalized integrator(TOGI)is used to generate orthogonal signals with the same frequency to estimate the grid voltage.In addition,in view of the deviation between actual and reference power in the three-phase PWM rectifier traditional PDPC strategy,a power correction link is designed to correct the power reference value.The grid voltage sensor free algorithm based on TOGI and the corrected PDPC strategy are applied to three-phase PWM rectifier and simulated on the simulation platform.Simulation results show that the proposed method can effectively eliminate the power tracking deviation and the grid voltage.The effectiveness of the proposed method is verified by comparing the simulation results.

Model Predictive Direct Torque Control of Permanent Magnet Synchronous Motor (PMSM) with Online Parameter Estimation Based on Extended Kalman Filter

Aiming at the torque and flux ripples in the direct torque control and the time-varying parameters for permanent magnet synchronous motor (PMSM), a model predictive direct torque control with online parameter estimation based on the extended Kalman filter for PMSM is designed. By predicting the errors of torque and flux based on the model and the current states of the system, the optimal voltage vector is selected to minimize the error of torque and flux. The stator resistance and inductance are estimated online via EKF to reduce the effect of model error and the current estimation can reduce the error caused by measurement noise. The stability of the EKF is proved in theory. The simulation experiment results show the method can estimate the motor parameters, reduce the torque, and flux ripples and improve the performance of direct torque control for permanent magnet synchronous motor (PMSM).

A Long-Range Generalized Predictive Control Algorithm for a DFIG Based Wind Energy System

This paper presents a new Long-range generalized predictive controller in the synchronous reference frame for a wind energy system doubly-fed induction generator based. This controller uses the state space equations that consider the rotor current and voltage as state and control variables, to execute the predictive control action. Therefore, the model of the plant must be transformed into two discrete transference functions, by means of an auto-regressive moving average model, in order to attain a discrete and decoupled controller, which makes it possible to treat it as two independent single-input single-output systems instead of a magnetic coupled multiple-input multiple-output system. For achieving that, a direct power control strategy is used, based on the past and future rotor currents and voltages estimation. The algorithm evaluates the rotor current predictors for a defined prediction horizon and computes the new rotor voltages that must be injected to controlling the stator active and reactive powers. To evaluate the controller performance, some simulations were made using Matlab/Simulink. Experimental tests were carried out with a small-scale prototype assuming normal operating conditions with constant and variable wind speed profiles. Finally, some conclusions respect to the dynamic performance of this new controller are summarized.

A Content-Aware Bitrate Selection Method Using Multi-Step Prediction for 360-Degree Video Streaming

A content-aware multi-step prediction control(CAMPC)algorithm is proposed to determine the bitrate of 360-degree videos,aim⁃ing to enhance the quality of experience(QoE)of users and reduce the cost of video content providers(VCP).The CAMPC algorithm first em⁃ploys a neural network to generate the content richness and combines it with the current field of view(FOV)to accurately predict the probability distribution of tiles being viewed.Then,for the tiles in the predicted viewport which directly affect QoE,the CAMPC algorithm utilizes a multi-step prediction for future system states,and accordingly selects the bitrates of multiple subsequent steps,instead of an instantaneous state.Meanwhile,it controls the buffer occupancy to eliminate the impact of prediction errors.We implement CAMPC on players by building a 360-degree video streaming platform and evaluating other advanced adaptive bitrate(ABR)rules through the real network.Experimental results show that CAMPC can save 83.5%of bandwidth resources compared with the scheme that completely transmits the tiles outside the viewport with the Dynamic Adaptive Streaming over HTTP(DASH)protocol.Besides,the proposed method can improve the system utility by 62.7%and 27.6%compared with the DASH official and viewport-based rules,respectively.

Trajectory Tracking of Autonomous Vehicle with the Fusion of DYC and Longitudinal–Lateral Control

The current research of autonomous vehicle motion control mainly focuses on trajectory tracking and velocity tracking. However, numerous studies deal with trajectory tracking and velocity tracking separately, and the yaw stability is seldom considered during trajectory tracking. In this research, a combination of the longitudinal–lateral control method with the yaw stability in the trajectory tracking for autonomous vehicles is studied. Based on the vehicle dynamics, considering the longitudinal and lateral motion of the vehicle, the velocity tracking and trajectory tracking problems can be attributed to the longitudinal and lateral control. A sliding mode variable structure control method is used in the longitudinal control. The total driving force is obtained from the velocity error in order to carry out velocity tracking. A linear time-varying model predictive control method is used in the lateral control to predict the required front wheel angle for trajectory tracking. Furthermore, a combined control framework is established to control the longitudinal and lateral motions and improve the reliability of the longitudinal and lateral direction control. On this basis, the driving force of a tire is allocated reasonably by using the direct yaw moment control, which ensures good yaw stability of the vehicle when tracking the trajectory. Simulation results indicate that the proposed control strategy is good in tracking the reference velocity and trajectory and improves the performance of the stability of the vehicle.

Neural network-based model predictive control with fuzzy-SQP optimization for direct thrust control of turbofan engine

A nonlinear model predictive control method based on fuzzy-Sequential Quadratic Programming(SQP)for direct thrust control is proposed in this paper for the sake of improving the accuracy of thrust control.The designed control system includes four parts,namely a predictive model,rolling optimization,online correction,and feedback correction.Considering the strong nonlinearity of engine,a predictive model is established by Back Propagation(BP)neural network for the entire flight envelope,whose input and output are determined with random forest algorithm and actual situation analysis.Rolling optimization typically uses SQP as the optimization algorithm,but SQP algorithm is easy to trap into local optimization.Therefore,the fuzzy-SQP algorithm is proposed to prevent this disadvantage using fuzzy algorithm to determine the initial value of SQP.In addition to the traditional three parts of model predictive control,an online correction module is added to improve the predictive accuracy of the predictive model in the predictive time domain.Simulation results show that the BP predictive model can reach a certain degree of predictive accuracy,and the proposed control system can achieve good tracking performance with the limited parameters within the safe range。

Model predictive control of indoor thermal environment conditioned by a direct expansion air conditioning system

Temperature and humidity are two important factors that influence both indoor thermal comfort and air quality.Through varying compressor and supply fan speeds of a direct expansion(DX)air conditioning(A/C)unit,the air temperature and humidity in the conditioned space can be regulated simultaneously.However,most existing controllers are designed to minimize the tracking errors between the system outputs with their corresponding settings as quickly as possible.The energy consumption,which is directly influenced by the compressor and supply fan speeds,is not considered in the relevant controller formulations,and thus the system may not operate with the highest possible energy efficiency.To effectively control temperature and humidity while minimizing the system energy consumption,a model predictive control(MPC)strategy was developed for a DX A/C system,and the development results are presented in this paper.A physically-based dynamic model for the DX A/C system with both sensible and latent heat transfers being considered was established and validated by experiments.To facilitate the design of MPC,the physical model was further linearized.The MPC scheme was then developed by formulating the objective function which sought to minimize the tracking errors of temperature and moisture content while saving energy consumption.Based on the results of command following and disturbance rejection tests,the proposed MPC scheme was capable of controlling temperature and humidity with adequate control accuracy and sensitivity.In comparison to linear-quadratic-Gaussian(LQG)controller,better control accuracy and lower energy consumption could be realized when using the proposed MPC strategy to simultaneously control temperature and humidity.

Predictive control of a direct internal reforming SOFC using a self recurrent wavelet network model

In this paper,an application of a nonlinear predictive controller based on a self recurrent wavelet network (SRWN) model for a direct internal reforming solid oxide fuel cell (DIR-SOFC) is presented. As operating temperature and fuel utilization are two important parameters,the SOFC is identified using an SRWN with inlet fuel flow rate,inlet air flow rate and current as inputs,and temperature and fuel utilization as outputs. To improve the operating performance of the DIR-SOFC and guarantee proper operating conditions,the nonlinear predictive control is implemented using the off-line trained and on-line modified SRWN model,to manipulate the inlet flow rates to keep the temperature and the fuel utilization at desired levels. Simulation results show satisfactory predictive accuracy of the SRWN model,and demonstrate the excellence of the SRWN-based predictive controller for the DIR-SOFC.

Improved three-vector based dead-beat model predictive direct power control strategy for grid-connected inverters

Since only one inverter voltage vector is applied during each duty cycle, traditional model predictive direct power control(MPDPC) for grid-connected inverters(GCIs) results in serious harmonics in current and power. Moreover, a high sampling frequency is needed to ensure satisfactory steady-state performance, which is contradictory to its long execution time due to the iterative prediction calculations. To solve these problems, a novel dead-beat MPDPC strategy is proposed, using two active inverter voltage vectors and one zero inverter voltage vector during each duty cycle. Adoption of three inverter vectors ensures a constant switching frequency. Thus, smooth steady-state performance of both current and power can be obtained. Unlike the traditional three-vector based MPDPC strategy, the proposed three vectors are selected based on the power errors rather than the sector where the grid voltage vector is located, which ensures that the duration times of the selected vectors are positive all the time. Iterative calculations of the cost function in traditional predictive control are also removed, which makes the proposed strategy easy to implement on digital signal processors(DSPs) for industrial applications. Results of experiments based on a 1 kW inverter setup validate the feasibility of the proposed three-vector based dead-beat MPDPC strategy.

Distributed MPC for Reconfigurable Architecture Systems via Alternating Direction Method of Multipliers

This paper investigates the distributed model predictive control(MPC)problem of linear systems where the network topology is changeable by the way of inserting new subsystems,disconnecting existing subsystems,or merely modifying the couplings between different subsystems.To equip live systems with a quick response ability when modifying network topology,while keeping a satisfactory dynamic performance,a novel reconfiguration control scheme based on the alternating direction method of multipliers(ADMM)is presented.In this scheme,the local controllers directly influenced by the structure realignment are redesigned in the reconfiguration control.Meanwhile,by employing the powerful ADMM algorithm,the iterative formulas for solving the reconfigured optimization problem are obtained,which significantly accelerate the computation speed and ensure a timely output of the reconfigured optimal control response.Ultimately,the presented reconfiguration scheme is applied to the level control of a benchmark four-tank plant to illustrate its effectiveness and main characteristics.

Experimental Verification of Fault Predictions in High Pressure Hydraulic Systems

In this paper a model of a high pressure hydraulic system was developed to simulate the effect of increased internal leakages inside the hydraulic cylinder and the 4/2 way directional control valve and to calculate the main parameters of the hydraulic system under various loads through the use of leakage-simulating throttle valves. After the completion of modeling, the throttle valves that simulate the internal leakages were calibrated and a number of test runs were performed for the cases of normal operation and the operation with increased internal leakages. The theoretical predictions were compared against the experimental results from an actual hydraulic test platform installed in the laboratory. In all cases, modeling and experimental data curves correlate very well in form, magnitude and response times for all the system’s main parameters. This proves that the present modeling can be used to accurately predict various faults in hydraulic systems, and can thus be used for proactive fault finding in many cases, especially when the defective component is not easily detected and obvious at first sight.

Anti-Windup Control Strategy of Drive System for Humanoid Robot Arm Joint

To address the problems of torque limit and controller saturation in the control of robot arm joint,an anti-windup control strategy is proposed for a humanoid robot arm,which is based on the integral state prediction under the direct torque control system of brushless DC motor. First,the arm joint of the humanoid robot is modelled. Then the speed controller model and the influence of the initial value of the integral element on the system are analyzed. On the basis of the traditional antiwindup controller,an integral state estimator is set up. Under the condition of different load torques and the given speed,the integral steady-state value is estimated. Therefore the accumulation of the speed error terminates when the integrator reaches saturation. Then the predicted integral steady-state value is used as the initial value of the regulator to enter the linear region to make the system achieve the purpose of anti-windup. The simulation results demonstrate that the control strategy for the humanoid robot arm joint based on integral state prediction can play the role of anti-windup and suppress the overshoot of the system effectively. The system has a good dynamic performance.

基于模型预测的直驱式波浪发电机机侧最优功率控制技术

直驱式波浪发电系统具有结构简单、成本低、效率高等优点,得到了国内外学者的广泛关注。该文以直驱式波浪发电系统为研究对象,分析并建立了直驱式波浪发电装置的水动力数学模型和永磁直线电机的动态数学模型。推导了基于模型预测的功率控制策略用于直驱式波浪发电系统。在功率控制策略中从功率捕获策略和功率跟踪控制策略展开研究以实现将波浪能转换为电能并汇入直流母线。重点围绕功率捕获策略开展基于模型预测控制技术研究,分别从控制策略、预测区间和成本函数三个角度分析了基于模型预测的最优功率捕获策略。建立了仿真模型并进行仿真分析,结果表明,采用基于模型预测的最优功率捕获策略可有效提高不规则波下发电机从波浪发电系统捕获能量。此外,采用以直流母线功率汇入最大为目标的成本函数可以有效地提高发电机输入直流侧能量,具有重要的实用价值。最后通过实验室模拟平台进行了实验测试。

基于分数阶滑模控制的光伏并网模型预测直接功率控制

针对外界扰动情况下的光伏并网模型预测直接功率控制(model predictive direct power control,MPDPC)系统中存在系统抖振、功率跟踪速度慢、并网电流总谐波失真率较高等问题,提出一种改进分数阶滑模电压控制器,该策略在直流侧母线电压外环采用了分数阶微积分理论.首先,构造分数阶非奇异快速终端滑模面函数,削弱系统抖振,提高系统动态性能;然后,构造分数阶双幂次指数趋近律,引入加权积分型增益和饱和函数,有效避免系统在非滑动模态阶段时切换增益的增大,提高系统控制精度;最后,设计新型分数阶电压环控制器并运用于光伏并网系统中.研究结果表明,改进后的分数阶滑模电压控制器能够满足光伏并网MPDPC系统的各项基本需求,抑制系统抖振,提高功率跟踪性能,降低并网电流总谐波失真率,有效解决可再生能源和公共电网电能转化的关键难题,对光伏并网系统高性能控制的理论研究具有重要意义.

计及弱磁效应的直驱式波浪发电系统变系数模型预测控制策略

为提高波浪发电系统(wave energy converter,WEC)能量捕获效率,并保证其在运行过程中满足系统的各种约束条件,该文以平均捕获功率最优为目标,提出计及弱磁效应的直驱式波浪发电系统变系数模型预测控制(model predictive control,MPC)策略。针对波浪激励力过大导致的变流器输出电压过调制问题,将弱磁控制引入优化算法,在满足发电机弱磁极限的前提下增大发电系统的运行范围,提高WEC的能量捕获效率。此外,基于仿真分析,发现了规则波下MPC目标函数的正则系数与平均捕获功率的对应关系。在此基础上,引入快速傅里叶变换(fast Fourier transformation,FFT)与叠加定理,构建了适用于不规则波输入的直驱式WEC变系数MPC控制策略。仿真结果表明,在不规则波浪下,所提方案能在满足系统约束条件的同时,有效提高能量捕获效率。

磁链分区的磁场增强型永磁电机全速域模型预测直接转矩控制

磁场增强型内置式永磁(flux intensifying interior permanent magnet,FI-IPM)电机的反凸极(L_(q)<L_(d))特性,使得正向d轴电流增强磁场,提高输出转矩的同时,也使得弱磁升速时d轴电流正负交替变化,电流角度变化大,磁链波动大,进而造成转矩脉动大、电流畸变振荡、响应速度慢等问题。针对这些问题,该文提出一种基于磁链分区的磁场增强型内置式永磁电机全速域模型预测直接转矩控制控制策略。根据FI-IPM电机速度运行区域与磁链状态映射关系,构建磁链和转矩预测模型。在此基础上,提出磁链分区控制,引入磁链差值判断增磁和弱磁运行区,并进行磁链补偿,解决不同速度运行域之间切换的震荡和不稳定问题。最后,通过实验验证所提出控制策略的有效性,为该类电机在电驱动系统中的应用提供技术方案。

基于MPC的耦合电感飞跨电容双向DC/DC变换器功率均衡解耦控制策略

耦合电感功率变换器因耦合电感阻抗、电感值等参数差异易导致功率不均衡。针对耦合电感飞跨电容双向DC/DC变换器,提出一种基于模型预测控制(MPC)的功率均衡解耦控制策略。通过对变换器原理进行分析,建立基于电感电流解耦的数学模型,得到包括电感电流和飞跨电容电压等6个控制变量的解耦控制方法。在此基础上,提出基于MPC的功率均衡解耦控制策略。同时,为降低MPC算法的运算负荷,根据解耦控制模型重构模型预测价值函数,实现各控制变量独立动态寻优,使系统能在稳定控制输出电压及飞跨电容电压的同时,实现两相耦合电感的功率均衡控制。最后,通过理论分析及实验对所提策略进行有效验证。