Nonlinear air-to-fuel ratio and engine speed control for hybrid vehicles

Internal combustion spark ignition engine management systems regulate the fuel, spark, and idle air subsystems to achieve sufficient engine performance at acceptable fuel economy and tailpipe emission levels. Engine control units also monitor other engine processes, using a suite of sensors, and periodically check the system actuators' operation to satisfy legislated onboard diagnostics. The majority of production engines regulate the air-to-fuel ratio using a speed-density, or air-flow, control strategy. In this approach, the mass of air drawn into a given cylinder is calculated using the engine speed, manifold absolute pressure, and inlet air temperature. Based on the air mass, appropriate fuel amounts are injected to achieve stoichiometric operation. However, the wide range of operating conditions, inherent induction process nonlinearities, and gradual component degradations due to aging have prompted research into model-based algorithms. In this paper, a nonlinear model-based control strategy will be proposed for simultaneous air-to-fuel ratio control and speed tracking in hybrid electric vehicles. The motivation for engine speed management resides in the integrated control of the engine and a continuously variable transmission for increased efficiency. The proposed backstepping controller uses an observer to reduce the inputs to manifold air mass (e.g., manifold absolute pressure and inlet air temperature) and engine speed. The underlying engine model describes the air intake, fuel injection, and rotational dynamics. For comparison purposes, an existing multisurface sliding mode controller and an integrated speed-density air-to-fuel controller with attached engine speed regulation have been implemented. The performance of each controller is studied using an analytical engine model with representative numerical results presented and discussed to provide insight into the overall performances.     

A neural-network based control solution to air-fuel ratio control for automotive fuel-injection systems

Maximization of the catalyst efficiency in automotive fuel-injection engines requires the design of accurate control systems to keep the air-to-fuel ratio at the optimal stoichiometric value AF/sub S/. Unfortunately, this task is complex since the air-to-fuel ratio is very sensitive to small perturbations of the engine parameters. Some mechanisms ruling the engine and the combustion process are in fact unknown and/or show hard nonlinearities. These difficulties limit the effectiveness of traditional control approaches. In this paper, we suggest a neural based solution to the air-to-fuel ratio control in fuel injection systems. An indirect control approach has been considered which requires a preliminary modeling of the engine dynamics. The model for the engine and the final controller are based on recurrent neural networks with external feedbacks. Requirements for feasible control actions and the static precision of control have been integrated in the controller design to guide learning toward an effective control solution.     

AN H∞ FUZZY TRACKING CONTROL SCHEME FOR AFFINE COUPLED SPATIO-TEMPORAL CHAOS

Due to the interactions among coupled spatio-temporal subsystems and the constant bias term of affine chaos, it is difficult to achieve tracking control for the affine coupled spatiotemporal chaos. However, every subsystem of the affine coupled spatio-temporal chaos can be approximated by a set of fuzzy models; every fuzzy model represents a linearized model of the subsystem corresponding to the operating point of the controlled system. Because the consequent parts of the fuzzy models have a constant bias term, it is very difficult to achieve tracking control for the affine system. Based on these fuzzy models, considering the affine constant bias term, an H∞ fuzzy tracking control scheme is proposed. A linear matrix inequality is employed to represent the feedback controller, and parameters of the controller are achieved by convex optimization techniques. The tracking control for the affine coupled spatio-temporal chaos is achieved, and the stability of the system is also guaranteed. The tracking performances are testified by simulation examples.     

Robust Model Predictive Control for Piecewise Affine Systems

In this paper, we study a robust model predictive control (MPC) strategy for piecewise affine (PWA) systems with uncertainty that is described as a set of polytopic parameter-varying models in a polytope corresponding to each partition of the PWA systems. First, an infinite horizon MPC technique for guaranteeing robust stability is developed for uncertain PWA systems. According to the condition of the PWA system states, the sequence of piecewise linear feedback controller at each sampling time is derived on-line by solving a convex optimization problem involving linear matrix inequalities. The feasible PWA control law design can robustly stabilize the uncertain PWA systems. However, the on-line optimization problems may lead to a computational burden. Then we further propose an improved robust MPC algorithm. When the current state is outside of the region of PWA systems containing the origin, the proposed on-line robust MPC algorithm is utilized; once the current state enters the region with the origin, sequence attraction domains where the origin is included are constructed off-line one within another, and the explicit control laws corresponding to different attraction domains can drive the state to the origin. The two algorithms are illustrated with a numerical example. The simulation results show that both controller design methods can stabilize the PWA systems with polytopic uncertainty, and the improved algorithm can reduce the on-line computation cost.     

基于GPC-PID的制冷系统过热度控制器

针对制冷系统的过热度控制问题,为保证系统动态性能,给出了一种基于广义预测控制滚动优化策略的PID参数自调整算法,设计了GPC-PID控制器.首先根据试验数据,基于最小二乘方法辨识得到过热度与膨胀阀开度间的传递函数模型,随后考虑系统模型的纯滞后环节,基于广义预测控制的性能指标,根据Kuhn-Tucker条件,通过滚动优化策略自动整定PID控制器的参数.最后的仿真结果表明,与传统PID控制器相比,GPC-PID控制器超调小,响应速度快,具有较好的控制效果.     

Fuzzy Gain-Scheduling Proportional–Integral Control for Improving Engine Power and Speed Behavior in a Hybrid Electric Vehicle

With the increased emphasis on improving fuel economy and reducing emissions, hybrid electric vehicles (HEVs) have emerged as very strong candidates to achieve these goals. The power-split hybrid system, which is a complex hybrid powertrain, exhibits great potential to improve fuel economy by determining the most efficient regions for engine operation and thereby high-voltage (HV) battery operation to achieve overall vehicle efficiency optimization. To control and maintain the actual HV battery power, a sophisticated control system is essential, which controls engine power and thereby engine speed to achieve the desired HV battery maintenance power. Conventional approaches use proportional-integral (PI) control systems to control the actual HV battery power in power-split HEV, which can sometimes result in either overshoots of engine speed and power or degraded response and settling times due to the nonlinearity of the power-split hybrid system. We have developed a novel approach to intelligently controlling engine power and speed behavior in a power-split HEV using the fuzzy control paradigm for better performances. To the best of our knowledge, this is the first reported use of the fuzzy control method to control engine power and speed of a power-split HEV in the applied automotive field. Our approach uses fuzzy gain scheduling to determine appropriate gains for the PI controller based on the system's operating conditions. The improvements include elimination of the overshoots as well as approximate 50% faster response and settling times in comparison with the conventional linear PI control approach. The improved performances are demonstrated through simulations and field experiments using a ford escape hybrid vehicle.     

Real-time predictive control strategy for a plug-in hybrid electric powertrain

Model predictive control is a promising approach to exploit the potentials of modern concepts and to fulfill the automotive requirements. Since, it is able to handle constrained multi-input multi-output optimal control problems. However, when it comes to implementation, the MPC computational effort may cause a concern for real-time applications. To maintain the advantage of a predictive control approach and improve its implementation speed, we can solve the problem parametrically. In this paper, we design a power management strategy for a Toyota Prius plug-in hybrid powertrain (PHEV) using explicit model predictive control (eMPC) based on a new control-oriented model to improve the real-time implementation performance. By implementing the controller to a PHEV model through model and hardware-in-the-loop simulation, we get promising fuel economy as well as real-time simulation speed.     

AN H∞ FUZZY TRACKING CONTROL SCHEME FOR AFFINE COUPLED SPATIO-TEMPORAL CHAOS

Due to the interactions among coupled spatio-temporal subsystems and the constant bias term of affine chaos, it is difficult to achieve tracking control for the affine coupled spatiotemporal chaos. However, every subsystem of the affine coupled spatio-temporal chaos can be approximated by a set of fuzzy models; every fuzzy model represents a linearized model of the subsystem corresponding to the operating point of the controlled system. Because the consequent parts of the fuzzy models have a constant bias term, it is very difficult to achieve tracking control for the affine system. Based on these fuzzy models, considering the affine constant bias term, an H∞ fuzzy tracking control scheme is proposed. A linear matrix inequality is employed to represent the feedback controller, and parameters of the controller are achieved by convex optimization techniques. The tracking control for the affine coupled spatio-temporal chaos is achieved, and the stability of the system is also guaranteed. The tracking performances are testified by simulation examples.     

基于高效MPC的活性污泥水处理系统控制

针对具多胞描述不确定性的污水处理控制系统,基于高效预测控制策略（EMPC）设计保性能的控制器。首先给出变参数活性污泥系统的状态空间模型,根据工况特点将其转化为多胞不确定模型,然后结合二次性能指标,通过在线求解凸规划问题,导出保证系统鲁棒渐近稳定的反馈控制律,最后以污水处理厂的处理能力为例,对设计的控制器进行仿真,结果表明,控制器能够保证系统闭环稳定,具有良好的控制效果。     

Impact of Feedback Delay on Closed-Loop Stability in Semiconductor Optical Amplifier Control Circuits

Semiconductor optical amplifiers (SOAs) are attractive for integrated photonic signal processing, but because their response is so fast, delays in a controller feedback path can jeopardize performance and stability. Using state-space methods, we quantify the constraints imposed on feedback controllers by closed-loop delay. We first derive a complete nonlinear state-space control model of a SOA with an equivalent circuit containing parasitics and dynamic impedance; the analytical state-space model agrees well with a validated photonic-only control model. Using a linearized version of the model we demonstrate that time delay in the feedback path can destabilize the SOA through phase accumulation. We then apply linear system theory to calculate the best-case stable delay margin for a given controller norm, and find a potentially severe inverse relationship between delay margin and controller norm. Finally, guided by the delay-controller relationship we design a hybrid feedforward-feedback controller to illustrate that good transient and steady-state regulation is obtained by carefully balancing the feedforward and feedback components. Our state-space modeling and design methods are general and are easily adapted to the design and analysis of more complex photonic circuits.     

Distortion Minimization with Fast Local Search for Fractal Image Compression

Optimal fractal image coding is an NP-hard combinatorial optimization problem, which consists of finding in a finite set of contractive affine mappings one whose unique fixed point is closest to the original image. Current fractal image schemes are based on a greedy suboptimal algorithm known as collage coding. In a previous paper, Hamzaoui, Hartenstein, and Saupe proposed a local search algorithm that iteratively improves an initial solution found by collage coding. For a standard fractal scheme based on quadtree image partitions, peak-signal-to-noise ratio (PSNR) gains are up to 0.8 dB. However, the algorithm is time-consuming because it involves many iteration steps, each of which requires the computation of the fixed point of an affine mapping. In this paper, we provide techniques that drastically reduce the complexity of the algorithm. Moreover, we show that the algorithm is also successful with a state-of-the-art fractal scheme based on highly adaptive image partitions.     

Analysis of a hybrid fractal-predictive-coding compression scheme

There has been tremendous progress in fractal compression since the pioneer work of Barnsley and Jacquin in the late 1980s. As the encoding time complexity issues are gradually being solved, there is a steady growth of applications of fractals, especially in hybrid systems. However, such fractal hybrid systems tend to be rather difficult to analyze, and part of that difficulty lies in the quantization of the scaling and luminance offset parameters adopted in most fractal compression schemes. In this paper, we present theoretical and empirical justification for a well-known but underused alternative parametrization for the fractal affine transform. In particular, we shall present a detailed analysis of a hybrid fractal-LPC (linear predictive coding) compression scheme using the aforementioned alternative affine transform parameters.     

Automatic temperature controller for multielement array hyperthermia systems

This paper concerns the optimization and performance analysis of an automatic control algorithm for managing power output of large multielement array hyperthermia applicators. Simulation and corresponding measurement of controller performance in a solid tissue equivalent phantom model is utilized for analysis of controller response to dynamically varying thermal load conditions that simulate clinical treatments. The analysis leads to an optimum controller which demonstrates the ability to achieve a uniform and stable temperature profile over a large surface area regardless of surrounding thermal load. This paper presents several advancements to the performance of a previously published control routine, including: 1) simplified simulation techniques for thorough characterization of controller performance; 2) an optimization procedure leading to an improved hybrid control algorithm for maintaining optimal performance during periods of both "rising" and "steady-state" temperature; 3) performance analysis of a control algorithm tailored for large area hyperthermia treatments with a mulitelement array applicator. The optimized hybrid controller is applied to the conformal microwave array (CMA) hyperthermia system previously developed for heating large area surface disease such as diffuse chestwall recurrence of breast carcinoma, and shown to produce stable, uniform temperatures under the multielement array applicator for all thermal load conditions.     

Implementation of Hybrid Control for Motor Drives

This paper presents the implementation of a hybrid-control strategy applied to a permanent-magnet synchronous-motor (PMSM) drive. Hybrid control is a general approach for control of a switching-based hybrid system (HS). This class of HS includes a continuous process controlled by a discrete controller with a finite number of states. In the case of ac motor drives, in contrast to conventional vector control like proportional-integral control or predictive control, where the inverter is not taken into account by the controller, hybrid control integrates the inverter model and considers the state of the inverter as a control variable. It allows to obtain faster torque dynamics than vector-control algorithms. The hybrid control algorithm requires both computing velocity for real-time implementation and code flexibility for management of low-performance functions and analog-digital interfaces. Codesign appears as a promising methodology for partitioning hybrid-control algorithm between software (flexible) and hardware (velocity) while taking care of overall time constrains. In this paper, the implementation of hybrid-control algorithm for a PMSM drive is performed through a codesign approach on an Excalibur board, embedding a CPU-core (Nios-2 by Altera) inside an APEX20KE200EFC484-2X field-programmable gate array. The partitioning of software and hardware parts is explained. Experimental results show the effectiveness of the implementation. Performances, advantages, and limitations are discussed.     

Modeling of an Electromechanical Engine Valve Actuator Based on a Hybrid Analytical--FEM Approach

The use of an electromechanical valve actuator (EMVA) formed by two magnets and two balanced springs is a promising tool to implement innovative engine management strategies. This actuator needs to be properly controlled to reduce impact velocities during engine valve operations, but the use of a position sensor for each valve is not possible for cost reasons. It is therefore essential to find sensorless solutions based on increasingly predictive models of such a mechatronic actuator. To address this task, in this paper, we present an in-depth lumped parameter model of an EMVA based on a hybrid analytical--finite-element method (FEM) approach. The idea is to develop a model of EMVA embedding the well-known predictive behavior of FEM models. All FEM data are then fitted to a smooth curve that renders unknown magnetic quantities in analytical form. In this regard, we select a single-wise function that is able to describe global magnetic quantities as the flux linkage and force both for linear and saturation working regions of the materials. The model intrinsically describes all mutual effects between two magnets. The goodness of the dynamic behavior of the model is finally tested on a series of transient FEM simulations of the actuator in different working conditions.      

基于模型匹配的光电侦察无人机飞行控制器设计方法

无人机在光电侦察领域的应用越来越广泛,设计可靠的飞行控制器是完成侦察任务的必要手段。提出了一种基于模型匹配和遗传算法寻优的以非线性模型为被控对象的飞行控制器设计方法。通过该方法可以实现无人机飞行控制器与飞行仿真模型的一体化快速设计与仿真,与经典的飞行控制器设计方法相比,该方法能够比较快速、便捷地获得所需控制器。建立了包含气动、发动机和环境模型的某型无人机六自由度非线性全量数学模型,然后基于此模型,应用上述方法设计了无人机的飞行控制器,基于有限状态机理论建立了飞行管理模型,设计无人机飞行剖面并实现控制器切换,最后进行了六自由度非线性仿真,验证了所设计控制器的有效性。     

Discrete-time model predictive contouring control for biaxial feed drive systems and experimental verification

In this paper we present a novel algorithm to model predictive contouring control for biaxial feed drive systems. model predictive control (MPC) refers to a class of model-based controllers that uses an explicit process model and tracking error dynamics to predict the future behavior of a plant, making it effective for machine tool feed drive systems that must achieve high-precision motion and are severely affected by friction, cutting force and changes in the workpiece mass. To improve contouring performance, we propose a new performance index in which error components orthogonal to the desired contour curve are given more importance than tracking errors with respect to each feed drive axis. Controller parameters are calculated in real time by solving an optimization problem. The parameters depend on the instantaneous slope of the reference trajectory and thus vary with time for curved reference trajectories, resulting in a time-varying controller. Weighting factors for the error components in orthogonal and tangential directions are used to adjust the error importance in each direction. In addition, to consider the required feed drive energy, the control inputs in both directions are included in the performance index. The effectiveness of the proposed control approach is demonstrated with an experimental biaxial feed drive system for circular and non-circular trajectories. The proposed contouring controller allows the feed drive to follow smooth curves and reduces contouring error.     

Analysis of on- and off-line optimized predictive currentcontrollers for PWM converter systems

The problem of on-off current control for coupling of a DC voltage system with a three-phase (polyphase) AC voltage system via a pulsewidth modulated (PWM) converter is discussed. The AC voltage represents either the counter EMF (electromotive force) of an AC machine or the three-phase power supply system (mains). The following control concepts are investigated by digital computer simulation: a simple hysteresis controller; a predictive controller with online optimization (optimization with respect to minimum switching frequency); and a controller based on offline optimization (using a switching table). It is shown that the relatively involved predictive controller can be replaced by a switching table of very limited size. For rating of the treated controllers the switching frequency as a function of the RMS voltage of the AC system and the other system parameters is used     

A Compact Digitally Controlled Fuel Cell/Battery Hybrid Power Source

A compact digitally controlled fuel cell/battery hybrid power source is presented in this paper. The hybrid power source composed of fuel cells and batteries provides a much higher peak power than each component alone while preserving high energy density, which is important and desirable for many modern electronic devices, through an appropriately controlled dc/dc power converter that handles the power flow shared by the fuel cell and the battery. Rather than being controlled to serve only as a voltage or current regulator, the power converter is regulated to balance the power flow to satisfy the load requirements while ensuring the various limitations of electrochemical components such as battery overcharge, fuel cell current limit (FCCL), etc. Digital technology is applied in the control of power electronics due to many advantages over analog technology such as programmability, less susceptibility to environmental variations, and low parts count. The user can set the FCCL, battery current limit, and battery voltage limit in the digital controller. A control algorithm that is suitable for regulating the multiple variables in the hybrid system is described by using a state-machine-based model; the issues about embedded control implementation are addressed; and the large-signal behavior of the hybrid system is analyzed on a voltage–current plane. The hybrid power source is then tested through simulation and validated on real hardware. This paper also discusses some important issues of the hybrid power source, such as operation under complex load profiles, power enhancement, and optimization of the hybrid system. The design presented here can not only be scaled to larger or smaller power capacities for a variety of applications but also be used for many other hybrid power sources.     

Design, modeling and control of a hybrid machine system

A hybrid machine is such a machine where its drive system integrates two types of motors: the servomotor and the constant velocity (CV) motor. The existing research on the hybrid machine prototypes usually uses two servomotors, of which one “mimic” the CV motor by prescribing a constant velocity trajectory profile. It is obvious that this departs away from the real situation where a CV motor is in place. The CV motor will bring in the velocity fluctuation which can not be attenuated by the CV motor itself due to the lack of a control mechanism in the CV motor, yet be propagated to the servomotor, and further to the end-effector of the machine. The general strategy for controlling the hybrid machine is therefore to model this propagated fluctuation and incorporate it into a controller for the servomotor. A controller based on the sliding mode control technique is proposed for the hybrid machine in this paper. The stability analysis shows that the controller is asymptotically stable. Simulation with a preliminary test demonstrates the effectiveness and robustness of this controller. Finally, we examine further performance improvement through attaching a flywheel on the CV motor to demonstrate the effectiveness of the synergy of the integration of mechanical and electrical means.