Adaptive coordinated control of engine speed and battery charging voltage

In this paper, the control problem of auxiliary power unit (APU) for hybrid electric vehicles is investigated.An adaptive controller is provided to achieve the coordinated control between the engine speed and the battery charging voltage. The proposed adaptive coordinated control laws for the throttle angle of the engine and the voltage of the powerconverter can guarantee not only the asymptotic tracking performance of the engine speed and the regulation of the battery charging voltage, but also the robust stability of the closed loop system under external load changes. Simulation results are given to verify the performance of the proposed adaptive controller.     

Adaptive cruise control of a HEV using sliding mode control

This paper presents adaptive cruise control of a hybrid electric vehicle. First, the mathematical model of the vehicle is formulated. Next, a classical controller is applied to the vehicle model. Swarm optimisation is implemented for self parameter tuning of the controller. The model is simulated and the result of the response to a variable speed is analysed. The results reveal that the controller is not a powerful means to manage the rapid transformation of the desire set point. Accordingly, a sliding mode controller is developed next. The performance of this controller is compared with the classical controller.     

Lyapunov-based feedback design and experimental verification of IC engine speed control

In this paper, the speed control problem of internal combustion engines is investigated based on mean-value engine models. The dynamics of internal combustion engines is a complicated nonlinear system, and usually, it is difficult to know the exact values of the physical parameters. First, a Lyapunov-based design method is shown without requiring the full information of the physical parameters. Then, to improve transient performance, the design method is extended to several cases under different operation conditions. Numerical simulation results are presented for comparing the proposed design methods. Finally, experiments are conducted on an engine test bench and the results demonstrate the validity of the proposed design methods. Recommended by Editorial Board member Myotaeg Lim under the direction of Editor Hyun Seok Yang. The authors are grateful to Kai Zheng for his assistance of the model identification experiments. Jiangyan Zhang received the B.E. and M.E. degrees in Electrical Engineering, Yanshan University, China, in 2005 and 2008, respectively. Now, she is a Ph.D. candidate with the Department of Engineering and Applied Sciences, Sophia University, Tokyo, Japan. Her current research interests include nonlinear system control theory and applications to powertrain system control. Tielong Shen received the Ph.D. degree in Mechanical Engineering from Sophia University, Tokyo, Japan, March, 1992. From April 1992, he has been a faculty member of the Chair of Control Engineering in Department of Mechanical Engineering, Sophia University, where he currently serves as professor of the Department of Engineering and Applied Science. His research interests include control theory and application in mechanical systems, power systems, and automotive powertrain. Currently, he is an Associate Editor for the IEEE Control System Society Conference Editorial Board, and is serving as Associate Editor of Journal of Control Theory and Applications, and the Regional Editor Asia-Pacific for International Journal of Modeling, Identification and Control etc. Junichi Kako received the B.E. degree from Nagoya Institute of Technology, Nagoya, Japan. He joined Toyota Motor Corporation, Tokyo, Japan in 1989. He worked on various aspects of automotive powertrain control. From 1989 to 1994, he took part in the team for the development of Laboratory Automation (LA) system, Engineering Office Automation (EOD) system, and embedded system of powertrain control. During 1995–2001, he focused on the engine control systems in Powertrain Management Engineering Division. In 2002, he was with Future Project Division in which he was responsible for the R&D of model-based engine control system. Currently, he is developing engine control systems in the Powertrain Management Engineering Division, Toyota Motor Corporation. Shozo Yoshida received the M.S. degree in Engineering from Kyoto University, Kyoto, Japan. He joined Toyota Motor Corporation, Tokyo, Japan in 2000. From 2000 to 2004, he was with Future Project Division and worked on physical combustion modeling for Model-based Control Development. Since 2005, he has been with the Powertrain Management Engineering Division Toyota Motor Corporation, and is a member of the R&D of Model-based Engine Calibration.     

Modeling and speed control design of an ethanol engine for variable speed gensets

This paper presents the development of a discrete dynamic mean value engine model (MVEM) suitable for the design of speed controllers of ethanol fueled internal combustion engines (ICE), to be used in variable speed gensets. Two MVEMs are developed for the ICE: the Time Based model and the Crank Based model. The speed controller design is held through the discretization and linearization of the Crank Based MVEM. This model is used due to the advantages over the time based MVEM especially with respect to the transport delay which becomes constant. Two approaches for the ICE speed control are investigated: (i) a single loop gain-scheduled proportional integral (PI) controller and (ii) a dual loop control based on an internal gain-scheduled Manifold Absolute Pressure (MAP) feedback loop and an outer loop composed of a gain-scheduled PI controller. The control design is developed in the frequency domain and its stability is ensured by the phase and gain margins. In addition, an integral anti-windup and a feed forward action are also proposed to improve the behavior during control law saturation, improve transient responses and disturbance rejection capability. Experimental results on a 50 kW generator set are provided to validate the controllers and to demonstrate the performance of the system.     

Distributed optimal energy consumption control of HEVs under MFG-based speed consensus

This paper investigates a distributed optimal energy consumption control strategy under mean-field game based speed consensus. Large scale vehicles in a traffic flow is targeted instead of individual vehicles, and it is assumed that the propulsion power of vehicles is hybrid electric powertrain. The control scheme is designed in the following two stages. In the first stage, in order to achieve speed consensus, the acceleration control law is designed by applying the MFG (mean-field game) theory. In the second stage, optimal powertrain control for minimizing energy consumption is obtained through coordinate the engine and the motor under the acceleration constraint. The simulation is conducted to demonstrate the effectiveness of the proposed control strategy.     

Application of adaptive control to the fluctuation of engine speed at idle

Idle speed control in a fuel-injection engine system has focused on controlling long-term averages of engine speed, but short-term fluctuations of engine speed have been neglected. The torque differences among cylinders influence the idle stability and cause vibration of the vehicle. In this paper, we introduce two intelligent control systems to reduce the fluctuations of engine speed at idle, an evolutionary computing control based on genetic algorithms and a stochastic control based on Alopex algorithm. We first estimate the torque differences among the cylinders by observing an engine cycle of crankshaft angular speed. Then the uniformity level over the engine speed is fedback into the control system. It manipulates spark ignition timings to suppress unbalanced combustions among the cylinders. We test the two adaptive approaches with simulation of a nonlinear engine model, and compare their performances.     

高速公路主线限速与匝道融合的协调控制

为缓解高速公路的交通拥挤,主线限速、匝道融合等常被应用,因主线限速和匝道融合经各自优化获得的控制策略可能存在矛盾,故二者协调是必须的,而如何建立和求解二者的协调控制模型还没有有效方法.本文基于宏观交通流理论和多agent技术研究了此协调控制问题.为此首先阐述了高速公路的一般宏观交通流模型;然后分析主线限速、匝道融合的交通特性,建立了主线限速-匝道融合交通流模型;并协调主线限速和匝道融合,建立了协调控制模型.最后,基于多agent技术和分层递阶结构提出了协调控制模型的求解算法,并给出了应用此方法控制仿真高速公路的一个实例.     

基于有效风速估计与预测的风电机组自适应最大风能跟踪控制

针对如何在有效风速未知情况下实现风电机组最大风能跟踪(MPPT)的问题,本文使用支持向量回归(SVR)和自适应控制原理,提出基于有效风速估计与预测的自适应MPPT控制方案.首先,使用机组的历史运行数据,训练得到基于SVR的风速估计与预测模型,为MPPT控制提供实时参考输入.其次,结合在线学习估计器(OLA)和减小转矩增益(DTG)控制原理,设计自适应MPPT控制器,该控制器能够较好应对系统未知动态特性和干扰,且能降低传动链载荷.最后,使用李雅普诺夫原理证明闭环系统所有信号都是有界的.仿真结果表明本文提出的方法能够获得良好的MPPT效果,进而提高机组产能.     

基于自适应观测器的无速度传感器感应电机控制

针对采用极点配置的自适应速度观测器存在不稳定区域的问题,建立了全阶自适应状态观测器并给出了观测器的速度辨识律.应用Lyapunov稳定性理论,观测器的增益借助于MATLAB LMI工具箱求解两个双线性矩阵不等式得到.在MATLAB 6.5/SIMULINK环境下,建立了无速度传感器感应电机直接转矩控制的仿真实验平台,给出了无速度传感器直接转矩控制的仿真结果.仿真结果表明本文给出的自适应观测器在全速范围内具有很好的稳态性能,并具有很好的鲁棒性.同时,在以TMS320F240为核心的感应电机直接转矩控制系统上进行了速度辨识实验,实验结果验证了方案的有效性.     

智能电动车辆横纵向协调与重构控制

针对具有高度非线性、强耦合和冗余特性的智能电动车辆运动控制问题,提出了一种由协调控制律和控制分配律组成的横纵向综合控制新方法.首先,建立准确表征智能电动车辆行为机理的动力学模型;其次,采用非奇异滑模控制技术,引入非线性滑动模态切换面,设计有效克服非线性及不确定特性的协调控制律,保证系统状态在有限时间内收敛至平衡点;在此基础上,考虑到轮胎存在冗余和耦合特性,提出基于内点法的控制分配算法来完成期望广义力/力矩的优化分配,实现轮胎横纵向力的协调与重构.仿真结果表明了该方法的有效性.     

Automatic control of casting speed in ingot casting

A microwave casting speed meter and the automatic casting speed control system that uses it have been developed for the bottom pouring process of steel ingots. The microwave meter is based on the Doppler radar technique, and a circuit of period measurement and reciprocal calculation has been developed to make its response time faster. Also it has a function to measure cast height. The automatic system can be regarded as a sampled-data system with an adaptive control function. Outlines of the microwave meter, the control circuit and the method of adaptive control are presented. The relationship of the crack appearance to the casting speed is studied, and programmed patterns of the casting speed have been prepared to reduce cracking. The programmed ‘pattern casting’ has been achieved by the automatic control system, and the surface quality of the ingot has been improved.     

自适应逆控制的异步电机变频调速系统研究

将自适应逆控制应用干异步电机变频调速控制,对给定信号、参数摄动和外部扰动分别施以控制,使二者同时达到最佳控制效果,无需在二者之间进行折衷．利用变论域变步长的LMS自适应滤波算法,使自适应逆控制的异步电机变频调速系统及其逆系统辨识的初始收敛速度、时变系统跟踪能力及稳态精度3个指标同时达到最优．仿真实验表明了该控制的先进性和有效性．     

Adaptive neural network control of tendon-driven mechanisms with elastic tendons

We propose an adaptive control and an adaptive neural network control (composed of two RBF neural components and one adaptive component) for tendon-driven robotic mechanisms with elastic tendons. These controllers can be applied to serial or parallel tendon-driven manipulators having linear or non-linear elastic tendons. We begin by proving the stability of the adaptive control system for our mechanism, and then we prove the stability of the adaptive neural network system and report on the results of numerical simulations and experimental results performed using a 2-DOF tendon-driven mechanism having six elastic tendons.     

Design, implementation, and experimental validation of optimal power split control for hybrid electric trucks

Hybrid electric vehicles require an algorithm that controls the power split between the internal combustion engine and electric machine(s), and the opening and closing of the clutch. Optimal control theory is applied to derive a methodology for a real-time optimal-control-based power split algorithm. The presented strategy is adaptive for vehicle mass and road elevation, and is implemented on a standard Electronic Control Unit of a parallel hybrid electric truck. The implemented strategy is experimentally validated on a chassis dynamo meter. The fuel consumption is measured on 12 different trajectories and compared with a heuristic and a non-hybrid strategy. The optimal control strategy has a fuel consumption lower (up to 3%) than the heuristic strategy on all trajectories that are evaluated, except one. Compared to the non-hybrid strategy the fuel consumption reduction ranged from 7% to 16%.     

Adaptive tracking control of uncertain MIMO nonlinear systems with input constraints

In this paper, adaptive tracking control is proposed for a class of uncertain multi-input and multi-output nonlinear systems with non-symmetric input constraints. The auxiliary design system is introduced to analyze the effect of input constraints, and its states are used to adaptive tracking control design. The spectral radius of the control coefficient matrix is used to relax the nonsingular assumption of the control coefficient matrix. Subsequently, the constrained adaptive control is presented, where command filters are adopted to implement the emulate of actuator physical constraints on the control law and virtual control laws and avoid the tedious analytic computations of time derivatives of virtual control laws in the backstepping procedure. Under the proposed control techniques, the closed-loop semi-global uniformly ultimate bounded stability is achieved via Lyapunov synthesis. Finally, simulation studies are presented to illustrate the effectiveness of the proposed adaptive tracking control.     

双闭环策略下非完整轮式机器人鲁棒自适应运动/力协调控制

非完整轮式移动机器人的路径跟踪,需要在保证机器人姿态跟踪精度的同时,增强其地面适应性能.为实现这种运动/力的协调控制目标,本文提出双闭环的控制系统结构:外环能够增加运动精度,内环则可以增强机器人对地面动态摩阻的适应性.同时,考虑到地面摩阻的慢时变性,本文通过构造观测器对其进行估计.在具体算法实现方面,采用反步法在外环构建运动控制器:而在内环,则是应用积分型的滑模技术设计力控制器与观测器.最后,对控制系统进行仿真,仿真结果证明所提出控制方法的有效性.     

Adaptive tracking control of nonlinear systems with dynamic uncertainties using neural network

In this paper, an adaptive neural tracking control approach is proposed for a class of nonlinear systems with dynamic uncertainties. The radial basis function neural networks (RBFNNs) are used to estimate the unknown nonlinear uncertainties, and then a novel adaptive neural scheme is developed, via backstepping technique. In the controller design, instead of using RBFNN to approximate each unknown function, we lump all unknown functions into a suitable unknown function that is approximated by only a RBFNN in each step of the backstepping. It is shown that the designed controller can guarantee that all signals in the closed-loop system are semi-globally bounded and the tracking error finally converges to a small domain around the origin. Two examples are given to demonstrate the effectiveness of the proposed control scheme.     

Supervisory control of hybrid powertrains: An experimental benchmark of offline optimization and online energy management

The paper describes a chain of tools aimed at the development and validation of energy management strategies (EMS) for hybrid powertrains. These tools comprise an offline optimizer based on Pontryagin minimum principle (PMP) and the online equivalent consumption minimization strategy (ECMS), both implemented in a dynamic simulation platform and as a real-time controller in a semi-physical testing equipment. The results presented are aimed at illustrating the continuity of the various approaches by comparing the offline-generated energy management laws with their online counterparts, both in terms of trajectories over time and in terms of global results (fuel consumption, state-of-charge deviations).     

Adaptive passivity-based control for maximum power extraction of stand-alone windmill systems

This paper addresses the high performance regulation of stand-alone windmill systems consisting of a wind turbine coupled to a generator and a battery charging system, which is a challenging problem for at least two reasons. First, the dynamics of the overall system are described by a highly coupled set of nonlinear differential equations. Since the range of operating points of the system is very wide, classical linear controllers yield below par performances. Second, in many applications it is desirable to extract from windmill systems their maximum power. This operating point is a nonlinear function of the wind speed, which is hard to measure. In this paper, a nonlinear passivity-based controller that ensures asymptotic convergence to the maximum power extraction point, which is rendered adaptive combining it with a wind speed estimator previously proposed by the authors, is proposed. Detailed computer simulations are presented to validate the approach.