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).

DISOPE distributed model predictive control of cascade systems with network communication

A novel distributed model predictive control scheme based on dynamic integrated system optimization and parameter estimation （DISOPE） was proposed for nonlinear cascade systems under network environment. Under the distributed control structure, online optimization of the cascade system was composed of several cascaded agents that can cooperate and exchange information via network communication. By iterating on modified distributed linear optimal control problems on the basis of estimating parameters at every iteration the correct optimal control action of the nonlinear model predictive control problem of the cascade system could be obtained, assuming that the algorithm was convergent. This approach avoids solving the complex nonlinear optimization problem and significantly reduces the computational burden. The simulation results of the fossil fuel power unit are illustrated to verify the effectiveness and practicability of the proposed algorithm.

Constrained Nonlinear Model Predictive Control of a Polymerization Process via Evolutionary Optimization

In this work, a nonlinear model predictive controller is developed for a batch polymerization process. The physical model of the process is parameterized along a desired trajectory resulting in a trajectory linearized piecewise model (a multiple linear model bank) and the parameters are identified for an experimental polymerization reactor. Then, a multiple model adaptive predictive controller is designed for thermal trajectory tracking of the MMA polymerization. The input control signal to the process is constrained by the maximum thermal power provided by the heaters. The constrained optimization in the model predictive controller is solved via genetic algorithms to minimize a DMC cost function in each sampling interval.

Nonlinear Model Predictive Controller for Compensations of Single Line-to-ground Fault in Resonant Grounded Power Distribution Networks

An nonlinear model predictive controller(NMPC)is proposed in this paper for compensations of single line-to-ground(SLG)faults in resonant grounded power distribution networks(RGPDNs),which reduces the likelihood of power line bushfire due to electric faults.Residual current compensation(RCC)inverters with arc suppression coils(ASCs)in RGPDNs are controlled using the proposed NMPC to provide appropriate compensations during SLG faults.The proposed NMPC is incorporated with the estimation of ASC inductance,where the estimation is carried out based on voltage and current measurements from the neutral point of the power distribution network.The compensation scheme is developed in the discrete time using the equivalent circuit of RGPDNs.The proposed NMPC for RCC inverters ensures that the desired current is injected into the neutral point during SLG faults,which is verified through both simulations and control hardware-in-the-loop(CHIL)validations.Comparative results are also presented against an integral sliding mode controller(ISMC)by demon-strating the capability of power line bushfire mitigation.

A Novel Tuning Method for Predictive Control of VAV Air Conditioning System Based on Machine Learning and Improved PSO

The variable air volume(VAV)air conditioning system is with strong coupling and large time delay,for which model predictive control(MPC)is normally used to pursue performance improvement.Aiming at the difficulty of the parameter selection of VAV MPC controller which is difficult to make the system have a desired response,a novel tuning method based on machine learning and improved particle swarm optimization(PSO)is proposed.In this method,the relationship between MPC controller parameters and time domain performance indices is established via machine learning.Then the PSO is used to optimize MPC controller parameters to get better performance in terms of time domain indices.In addition,the PSO algorithm is further modified under the principle of population attenuation and event triggering to tune parameters of MPC and reduce the computation time of tuning method.Finally,the effectiveness of the proposed method is validated via a hardware-in-the-loop VAV system.

Experimental Evaluation of Parameterized Nonlinear MPC Applied to PEM Fuel Cell

This paper proposes a parameterized nonlinear model-based predictive control (NMPC) strategy to tackle the oxygen excess ratio regulation challenge of a proton exchange membrane fuel cell. In practice, the most challenging part regarding NMPC strategies remains the on-line implementation. In fact, NMPC strategies, at least in their basic form, involve heavy computation to solve the optimization problem. In this work, a specific parameterization of control actions has been designed to address this limitation and achieve on-line implementation. To assess the effectiveness and relevance of the proposed strategy, the controller has been implemented on-line, experimentally validated on a real fuel cell and compared to the built-in controller. Performance of the parameterized NMPC controller in terms of setpoint tracking accuracy, disturbances rejection and computational cost, have tested under several control scenarios. Experimental results have shown the excellent tracking capability, disturbances rejection ability and low computational cost of the NMPC controller, regardless of the operating conditions. Moreover, compared to the built-in controller the proposed strategy has demonstrated better disturbances rejection capability. Overall, the proposed parameterized NMPC controller appears as an excellent candidate to address the oxygen excess ratio regulation issue.

A weighted voltage model predictive control method for a virtual synchronous generator with enhanced parameter robustness

To address the problem of insufficient system inertia and improve the power quality of grid-connected inverters,and to enhance the stability of the power system,a method to control a virtual synchronous generator(VSG)output voltage based on model predictive control(MPC)is proposed.Parameters of the inductors,capacitors and other components of the VSG can vary as the temperature and current changes.Consequently the VSG output voltage and power control accuracy using the conventional MPC method may be reduced.In this paper,to improve the parameter robustness of the MPC method,a new weighted predictive capacitor voltage control method is proposed.Through detailed theoretical analysis,the principle of the proposed method to reduce the influence of parameter errors on voltage tracking accuracy is analyzed.Finally,the effectiveness and feasibility of the proposed method are verified by experimental tests using the Typhoon control hardware-in-the-loop experimental platform.

基于最小二乘法的欠驱动水面船舶模型预测控制

针对欠驱动水面船舶轨迹跟踪控制问题,根据模型预测控制(Model Predictive Control, MPC)原理,提出一种基于参数化模型的非线性模型预测控制(Parameterized Model-Nonlinear Model Predictive Control, PM-NMPC)方法。采用最小二乘法对船舶的参数化模型进行辩识,设计PM-NMPC控制器。对环境干扰下的某集装箱船艏向角控制和轨迹跟踪进行试验,验证控制算法的有效性,并将该控制器与比例积分微分控制器(Proportional plus Integral plus Derivative cotroller, PID cotroller)控制器进行对比。仿真结果表明,PM-NMPC控制器轨迹跟踪效果更好,对未知干扰具有更强的稳健性。

Survey on nonlinear reconfigurable flight control

An overview on nonlinear reconfigurable flight control approaches that have been demonstrated in flight-test or highfidelity simulation is presented. Various approaches for reconfigurable flight control systems are considered, including nonlinear dynamic inversion, parameter identification and neural network technologies, backstepping and model predictive control approaches. The recent research work, flight tests, and potential strength and weakness of each approach are discussed objectively in order to give readers and researchers some reference. Finally, possible future directions and open problems in this area are addressed.

A Survey of Output Feedback Robust MPC for Linear Parameter Varying Systems

For constrained linear parameter varying(LPV)systems,this survey comprehensively reviews the literatures on output feedback robust model predictive control(OFRMPC)over the past two decades from the aspects on motivations,main contributions,and the related techniques.According to the types of state observer systems and scheduling parameters of LPV systems,different kinds of OFRMPC approaches are summarized and compared.The extensions of OFRMPC for LPV systems to other related uncertain systems are also investigated.The methods of dealing with system uncertainties and constraints in different kinds of OFRMPC optimizations are given.Key issues on OFRMPC optimizations for LPV systems are discussed.Furthermore,the future research directions on OFRMPC for LPV systems are suggested.

Modeling,Parameter Measurement,and Control of PMSG-based Grid-connected Wind Energy Conversion System

The design of reliable controllers for wind energy conversion systems(WECSs)requires a dynamic model and accurate parameters of the wind generator.In this paper,a dynamic model and the parameter measurement and control of a direct-drive variable-speed WECS with a permanent magnet synchronous generator(PMSG)are presented.An experimental method is developed for measuring the key parameters of the PMSG.The measured parameters are used in the design of the controllers.The generator-side converter is controlled using a vector control scheme that maximizes the power extraction under varying wind speeds.A model predictive controller(MPC)is designed for the grid-side voltage source converter(VSC)to regulate the active and reactive power flows to the power grid by controlling the d-and q-axis currents in the synchronous reference frame.The MPC predicts the future values of the control variables and takes control actions based on the minimum value of the cost functions.To comply with the grid code requirement,a modified design approach for an LCL filter is presented and incorporated into the system.The design process is simple and incorporates significant filter parameters while avoiding iterative calculations.The comparative analysis of the designed filter with conventional L,LC,and iterative LCL filters demonstrates the effectiveness of the modified design approach.The proposed wind energy system with MPC and LCL filter is simulated in MATLAB/Simulink and experimentally implemented in the laboratory using the dSpace digital signal processor(DSP)system.The simulation and experimental results validate the efficacy of the designed controllers using the measured parameters and show dynamic and steady-state performance under varying wind speeds.

非线性约束系统H_∞鲁棒预测控制

针对一类具有状态约束和控制约束的离散非线性系统,本文采用有限维参数化方法提出了一种基于闭环优化的H∞鲁棒预测控制算法.这种算法把预测控制的滚动优化机制同微分对策理论和非线性H∞控制理论做了有机结合;在闭环优化中通过有限维参数化方法把控制变量参数化为多项式控制变量,并且引入被控系统的过渡平衡点.这样算法不但可以处理不确定系统,而且降低了在线闭环优化的计算复杂度.进一步,证明了算法的可行性和对有界不确定性系统的鲁棒稳定性.最后,用数值仿真验证了算法的有效性.

传感器受限的车辆队列燃油经济性模型预测控制

车辆队列保持较小的车间距可以有效地降低车受风阻力的面积,减少燃油的消耗。将车间距稳定在较小数值上对减小燃油消耗具有很大的作用,由于车载传感器的量程受限,会影响车间距测量值,所以提出一种车辆队列参数化模型预测控制策略,在保证车辆队列跟踪性能的前提下,让车间距稳定在较小的范围内,提升车辆队列的燃油经济性。采用经典PD策略构造参数化时滞队列切换控制器并结合非线性性能指标函数,通过求解有限时域最优控制问题得到控制器中的自由参数。在此基础上,利用频域方法,建立参数化时滞队列预测控制器队列稳定的条件。最后对加减速典型场景仿真验证参数化模型预测控制方法的有效性。

基于滑模磁链观测器的感应电机模型预测控制

感应电机模型预测控制通过系统状态预测的方法,利用设计好的目标函数直接控制磁链和转矩。该方案易于实现多变量控制和处理非线性约束,但因为预测过程需要使用电机的真实磁链,所以对磁链观测的精度有很高的要求,以滑模磁链观测器替代传统的电流模型和电压模型磁链观测器可大大提高该方案的控制性能。常规的滑模磁链观测器依据电流观测误差设计滑模控制函数,在实现对转子电阻变化的高鲁棒性的同时也引入了定子电阻变化的影响。文章中提出一种基于磁链观测误差的滑模控制函数设计方法,对滑模观测器的性能进行改进。因为磁链的真实值无法直接获取,磁链误差值以近似计算的方式得到。通过仿真和物理实验验证了该方案对电机参数变化的高鲁棒性。

Robust Cooperative Control of Multiple Autonomous Vehicles for Platoon Formation Considering Parameter Uncertainties

This paper proposes a robust cooperative control strategy for multiple autonomous vehicles to achieve safe and efficient platoon formation,and it analyzes the effects of vehicle stability boundaries and parameter uncertainties.The cooperative vehicle control framework is composed of the upper planning level and lower tracking control level.In the planning level,the trajectory of each vehicle is generated by using the multi-objective flocking algorithm to form the platoon.The parameters of the flocking algorithm are optimized to prevent the vehicle speed and yaw rate from going beyond their limits.In the lower level,to realize the stable platoon formation,a lumped disturbance observer is designed to gain the stable-state reference,and a distributed robust model predictive controller is proposed to achieve the offset-free trajectory tracking while downsizing the effects of parameter uncertainties.The simulation results show the proposed cooperative control strategy can achieve safe and efficient platoon formation.

Hierarchical Control Strategy of Trajectory Tracking for Intelligent Vehicle

In order to track the desired trajectory for intelligent vehicle, a new hierarchical control strategy is presented. The control structure consists of two layers. The high-level controller adopts the model predictive control (MPC) to calculate the steering angle tracking the desired yaw angle and the lateral position. The low-level controller is designed as a gain-scheduling controller based on linear matrix inequalities. The desired longitudinal velocity and the yaw rate are tracked by the adjustment of each wheel torque. The simulation results via the high-fidelity vehicle dynamics simulation software veDYNA show that the proposed strategy has a good tracking performance and can guarantee the yaw stability of intelligent vehicle. © 2017, Shanghai Jiaotong University and Springer-Verlag Berlin Heidelberg.