Predictive direct torque control with reduced ripples for induction motor drive based on T‐S fuzzy speed controller

Finite‐state model predictive control (FS‐MPC) has been widely used for controlling power converters and electric drives. Predictive torque control strategy (PTC) evaluates flux and torque in a cost function to generate an optimal inverter switching state in a sampling period. However, the existing PTC method relies on a traditional proportional‐integral (PI) controller in the external loop for speed regulation. Consequently, the torque reference may not be generated properly, especially when a sudden variation of load or inertia takes place. This paper proposes an enhanced predictive torque control scheme. A Takagi‐Sugeno fuzzy logic controller replaces PI in the external loop for speed regulation. Besides, the proposed controller generates a proper torque reference since it plays an important role in cost function design. This improvement ensures accurate tracking and robust control against different uncertainties. The effectiveness of the presented algorithms is investigated by simulation and experimental validation using MATLAB/Simulink with dSpace 1104 real‐time interface.     

基于切换系统理论的异步电机最优直接转矩控制

提出一种系统建模和控制策略来实现两电平异步电机最优直接转矩控制来改善传统直接转矩控制存在的磁链和转矩脉动较大的问题。首先,基于切换系统理论建立两电平异步电机传动系统模型,然后在切换模型基础上,建立一个约束条件的有限时间最优控制目标函数,并利用滚动优化策略求解目标函数来实现最优直接转矩控制。仿真结果表明,该方法能够有效减少磁链和转矩脉动,降低逆变器开关频率,具有良好的动、静态性能。     

Quality guaranteed aggregation based model predictive control and stability analysis

The input aggregation strategy can reduce the online computational burden of the model predictive controller. But generally aggregation based MPC controller may lead to poor control quality. Therefore, a new concept, equivalent aggregation, is proposed to guarantee the control quality of aggregation based MPC. From the general framework of input linear aggregation, the design methods of equivalent aggregation are developed for unconstrained and terminal zero constrained MPC, which guarantee the actual control inputs exactly to be equal to that of the original MPC. For constrained MPC, quasi-equivalent aggregation strategies are also discussed, aiming to make the difference between the control inputs of aggregation based MPC and original MPC as small as possible. The stability conditions are given for the quasi-equivalent aggregation based MPC as well. Supported by the National Natural Science Foundation of China (Grant No. 60674041), and the Specialized Research Fund for the Doctoral Program of Higher Education (Grant No. 20070248004)     

永磁容错游标轮缘推进电机改进模型预测转矩控制

为提高永磁容错游标轮缘推进电机(FTPMV-RDM)在正常状态和一相开路故障状态下的控制性能, 本文提出了一种基于电压矢量预选的改进模型预测转矩(MPTC)控制方法. 针对六相独立H桥逆变器提供的备选电压矢量数量多导致MPTC系统计算量大的问题, 首先, 采用直接转矩控制中利用转矩、磁链误差及定子磁链位置信息确定预选电压矢量, 减少MPTC系统中电压矢量的枚举次数. 然后, 利用价值函数进行二次筛选得到最优电压矢量. 为了实现开路故障下的容错控制, 提出了一种更换备选电压矢量表的开路故障容错控制策略. 实验结果表明, 基于电压矢量预选的FTPMV-RDM模型, 本文预测转矩控制算法能够在无故障和一相开路故障下抑制电流畸变, 进而有效降低转矩和磁链脉动.     

基于蚁群算法的永磁同步电机多步预测控制

为了提高永磁同步电机驱动系统的效率与动态性能,结合传统矢量控制的特点,研究了一种永磁同步电机多步长预测直接电流控制策略,减少了逆变器开关损耗;为了解决多步长在线预测过程中的计算量过大的问题,提出了一种基于蚁群算法的模型预测控制方法,将连续时刻逆变器开关状态视为蚁群运动轨迹,并根据优化目标在较优路径上留下较强信息素作为正反馈以减少计算量。通过仿真对比,相较于矢量控制与直接转矩控制,上述算法可以明显降低逆变器开关损耗,提高动态响应速度;同时保持较小的总谐波失真。结果表明基于蚁群算法的多步预测能够在较少迭代次数内收敛,保证了在较低逆变器开关损耗下系统的性能。     

Two-stage on-board optimization of merging velocity planning with energy management for HEVs

This paper proposes a two-stage hierarchy control system with model predictive control (MPC) for connected parallel HEVs with available traffic information. In the first stage, a coordination of on-ramp merging problem using MPC is presented to optimize the merging point and trajectory for cooperative merging. After formulating the merging problem into a nonlinear optimization problem, a continuous/GMRES method is used to generate the real-time vehicle acceleration for two considered HEVs running on main road and merging road, respectively. The real-time acceleration action is used to calculate the torque demand for the dynamic system of the second stage. In the second stage, an energy management strategy (EMS) for powertrain control that optimizes the torque-split and gear ratio simultaneously is composed to improve fuel efficiency. The formulated nonlinear optimization problem is solved by sequential quadratic programming (SQP) method under the same receding horizon. The simulation results demonstrate that the vehicles can merge cooperatively and smoothly with a reasonable torque distribution and gear shift schedule.     

Handling uncertainty in economic nonlinear model predictive control: A comparative case study

In the last years, the use of an economic cost function for model predictive control (MPC) has been widely discussed in the literature. The main motivation for this choice is that often the real goal of control is to maximize the profit or the efficiency of a certain system, rather than tracking a predefined set-point as done in the typical MPC approaches, which can be even counter-productive. Since the economic optimal operation of a system resulting from the application of an economic model predictive control approach drives the system to the constraints, the explicit consideration of the uncertainties becomes crucial in order to avoid constraint violations. Although robust MPC has been studied during the past years, little attention has yet been devoted to this topic in the context of economic nonlinear model predictive control, especially when analyzing the performance of the different MPC approaches. In this work, we present the use of multi-stage scenario-based nonlinear model predictive control as a promising strategy to deal with uncertainties in the context of economic NMPC. We make a comparison based on simulations of the advantages of the proposed approach with an open-loop NMPC controller in which no feedback is introduced in the prediction and with an NMPC controller which optimizes over affine control policies. The approach is efficiently implemented using CasADi, which makes it possible to achieve real-time computations for an industrial batch polymerization reactor model provided by BASF SE. Finally, a novel algorithm inspired by tube-based MPC is proposed in order to achieve a trade-off between the variability of the controlled system and the economic performance under uncertainty. Simulations results show that a closed-loop approach for robust NMPC increases the performance and that enforcing low variability under uncertainty of the controlled system might result in a big performance loss.     

Price-driven coordination method for solving plant-wide MPC problems

In large-scale model predictive control (MPC) applications, such as plant-wide control, two possible approaches to MPC implementation are centralized and decentralized MPC schemes. These represent the two extremes in the “trade-off” among the desired characteristics of an industrial MPC system, namely accuracy, reliability and maintainability. To achieve optimal plant operations, coordination of decentralized MPC controllers has been identified as both an opportunity and a challenge. Typically, plant-wide MPC problem can be formulated as a large-scale quadratic program (QP) with linking equality constraints. Such problems can be decomposed and solved with the price-driven coordination method and on-line solutions of these structured large-scale optimization problems require an efficient price-adjustment strategy to find an “equilibrium price”. This work develops an efficient price-adjustment algorithm based on Newton’s method, in which sensitivity analysis and active set change identification techniques are employed. With the off-diagonal element abstraction technique and the enhanced priced driven coordination algorithm, a coordinated, decentralized MPC framework is proposed. Several case studies show that the proposed coordination-based decentralized MPC scheme is an effective approach to plant-wide MPC applications, which provides a high degree of reliability and accuracy at a reasonable computational load.     

基于扩张状态观测器的无速度传感器容错逆变器驱动永磁同步电机系统自抗扰模型预测转矩控制

针对三相四开关逆变器驱动永磁同步电机(PMSM)系统,基于扩张状态观测器(ESO)技术,提出了无速度传感器的自抗扰模型预测转矩控制(ADRMPTC)策略.建立了三相四开关逆变器驱动PMSM系统的数学模型;采用ESO技术构造了PMSM系统转速观测器,以实现对转速快速准确地实时估计;用自抗扰控制器(ADRC)作为系统的转速调节器,以提高系统的鲁棒性;利用模型预测转矩控制(MPTC)方法,以达到减小转矩和磁链脉动的目的.所设计基于ESO的无速度传感器ADRMPTC策略能够使三相四开关逆变器驱动的PMSM系统可靠稳定运行,达到满意的转矩和转速控制效果.与基于PI的MPTC策略相比,本文控制策略使PMSM系统不仅具有良好的动态性能,而且具有较强的抗负载干扰能力.仿真结果验证了所提方法的正确性和有效性.     

工业大系统双层结构预测控制的集中优化与分散控制策略

为降低工业大系统模型预测控制(Model predictive control,MPC)在线计算复杂度,同时保证系统的全局优化性能,提出一种集中优化、分散控制的双层结构预测控制策略.在稳态目标计算层(Steady-state target calculation, SSTC),基于全局过程模型对系统进行集中优化,将优化结果作为设定值传递给动态控制层;在动态控制层,将大系统划分为若干个子系统,每个子系统分别由基于各自子过程模型的模型预测控制进行控制,为减少各子系统之间的相互干扰,在各个子系统之间添加前馈控制器对扰动进行补偿,提高系统的总体动态控制性能.该策略的优点在于能确保系统全局最优性的同时降低了在线计算量,提高了工业大系统双层结构预测控制方法的实时性.仿真实例验证该方法的有效性.     

无刷直流伺服电机换向最优控制

为改善无刷直流伺服电机的换向转矩性能,本文利用逆变器电压控制矢量优化调制的思 想,提出了基于非换向相电流的恒频采样PWM控制的最优换向方案,该方案能有效地抑制换 向转矩脉动,缩短换向时间.实验结果表明,上述方案是可行的.     

Humanoid walking pattern generation based on model predictive control approximated with basis functions

This paper proposes a real-time walking pattern generator (WPG) based on model predictive control (MPC). Since reducing the calculation time is a crucial problem in real-time WPG, we consider introducing basis functions to reduce the number of control input. The control inputs in the MPC are described by a series of basis functions. Compared with the standard discrete-time MPC formulation, the approach with basis functions requires fewer optimization variables at the cost of decreasing precision. In order to find an appropriate trade-off, two basis functions named Laguerre functions and Haar functions, are tested in this paper. MPC with Laguerre functions decreases more computational load while MPC with Haar functions offers a more accurate solution. The approach is not restricted to Laguerre functions or Haar functions, users can select their own basis functions for different applications and preferences.     

Real-time energy-efficient anticipative driving control of connected and automated hybrid electric vehicles

In this paper, we propose a real-time energy-efficient anticipative driving control strategy for connected and automated hybrid electric vehicles (HEVs). Considering the inherent complexities brought about by the velocity profile optimization and energy management control, a hierarchical control architecture in the model predictive control (MPC) framework is developed for real-time implementation. In the higher level controller, a novel velocity optimization problem is proposed to realize safe and energy-efficient anticipative driving. The real-time control actions are derived through a computationally efficient algorithm. In the lower level controller, an explicit solution of the optimal torque split ratio and gear shift schedule is introduced for following the optimal velocity profile obtained from the higher level controller. The comparative simulation results demonstrate that the proposed strategy can achieve approximately 13% fuel consumption saving compared with a benchmark strategy.     

Robust event‐triggered model predictive control for constrained linear continuous system

Model predictive control (MPC) is capable to deal with multiconstraint systems in real control processes; however, the heavy computation makes it difficult to implement. In this paper, a dual‐mode control strategy based on event‐triggered MPC (ETMPC) and state‐feedback control for continuous linear time‐invariant systems including control input constraints and bounded disturbances is developed. First, the deviation between the actual state trajectory and the optimal state trajectory is computed to set an event‐triggered mechanism and reduce the computational load of MPC. Next, the dual‐mode control strategy is designed to stabilize the system. Both recursive feasibility and stability of the strategy are guaranteed by constructing a feasible control sequence and deducing the relationship of parameters, especially the inter‐event time and the upper bound of the disturbances. Finally, the theoretical results are supported by numerical simulation. In addition, the effects of the parameters are discussed by simulation, which gives guidance to balance computational load and control performance.     

A general framework to design stabilizing nonlinear model predictive controllers

We propose a new model predictive control (MPC) framework to generate feedback controls for time-varying nonlinear systems with input constraints. We provide a set of conditions on the design parameters that permits to verify a priori the stabilizing properties of the control strategies considered. The supplied sufficient conditions for stability can also be used to analyse the stability of most previous MPC schemes. The class of nonlinear systems addressed is significantly enlarged by removing the traditional assumptions on the continuity of the optimal controls and on the stabilizability of the linearized system. Some important classes of nonlinear systems, including some nonholonomic systems, can now be stabilized by MPC. In addition, we can exploit increased flexibility in the choice of design parameters to reduce the constraints of the optimal control problem, and thereby reduce the computational effort in the optimization algorithms used to implement MPC.     

An improved robust model predictive control approach to systems with linear fractional transformation perturbations

In this paper, a robust model predictive control approach is proposed for a class of uncertain systems with time-varying, linear fractional transformation perturbations. By adopting a sequence of feedback control laws instead of a single one, the control performance can be improved and the region of attraction can be enlarged compared with the existing model predictive control (MPC) approaches. Moreover, a synthesis approach of MPC is developed to achieve high performance with lower on-line computational burden. The effectiveness of the proposed approach is verified by simulation examples.     

Nonlinear model predictive control of a magnetic levitation system

The paper presents a fast nonlinear model predictive control (MPC) scheme for a magnetic levitation system. A nonlinear dynamical model of the levitation system is derived that additionally captures the inductor current dynamics of the electromagnet in order to achieve a high MPC performance both for stabilization and fast setpoint changes of the levitating mass. The optimization algorithm underlying the MPC scheme accounts for control constraints and allows for a time and memory efficient computation of the single iteration. The overall control performance of the levitation system as well as the low computational costs of the MPC scheme is shown both in simulations and experiments with a sampling frequency of 700 Hz on a standard dSPACE hardware.     

Energy Control of Plug-In Hybrid Electric Vehicles Using Model Predictive Control With Route Preview

The paper proposes an adoption of slope, elevation, speed and route distance preview to achieve optimal energy management of plug-in hybrid electric vehicles (PHEVs). The approach is to identify route features from historical and real-time traffic data, in which information fusion model and traffic prediction model are used to improve the information accuracy. Then, dynamic programming combined with equivalent consumption minimization strategy is used to compute an optimal solution for real-time energy management. The solution is the reference for PHEV energy management control along the route. To improve the system's ability of handling changing situation, the study further explores predictive control model in the real-time control of the energy. A simulation is performed to model PHEV under above energy control strategy with route preview. The results show that the average fuel consumption of PHEV along the previewed route with model predictive control (MPC) strategy can be reduced compared with optimal strategy and base control strategy.      

Theoretical analysis of unconstrained nonlinear model predictive control

Model predictive control (MPC) is a well-established controller design strategy for linear process models. Because many chemical and biological processes exhibit significant nonlinear behaviour, several MPC techniques based on nonlinear process models have recently been proposed. The most significant difference between these techniques is the computational approach used to solve the nonlinear model predictive control (NMPC) optimization problem. Consequently, analysis of NMPC techniques is often connected to the computational approach employed. In this paper, a theoretical analysis of unconstrained NMPC is presented that is independent of the computational approach. A nonlinear discrete-time, state-space model is used to predict the effects of future inputs on future process outputs. It is shown that model inverse, pole-placement, and steady-state controllers can be obtained by suitable selection of the control and prediction horizons. Moreover, the NMPC optimization problem can be modified to yield nonlinear internal model control (NIMC). The computational requirements of NIMC are considerably less than NMPC, but the NIMC approach is currently restricted to nonlinear models with well-defined and stable inverses. The NIMC controller is shown to provide superior servo and regulatory performance to a linear IMC controller for a continuous stirred tank reactor.     

On real-time robust model predictive control

High-speed applications impose a hard real-time constraint on the solution of a model predictive control (MPC) problem, which generally prevents the computation of the optimal control input. As a result, in most MPC implementations guarantees on feasibility and stability are sacrificed in order to achieve a real-time setting. In this paper we develop a real-time MPC approach for linear systems that provides these guarantees for arbitrary time constraints, allowing one to trade off computation time vs. performance. Stability is guaranteed by means of a constraint, enforcing that the resulting suboptimal MPC cost is a Lyapunov function. The key is then to guarantee feasibility in real-time, which is achieved by the proposed algorithm through a warm-starting technique in combination with robust MPC design. We address both regulation and tracking of piecewise constant references. As a main contribution of this paper, a new warm-start procedure together with a Lyapunov function for real-time tracking is presented. In addition to providing strong theoretical guarantees, the proposed method can be implemented at high sampling rates. Simulation examples demonstrate the effectiveness of the real-time scheme and show that computation times in the millisecond range can be achieved.