Robust control for an electric power steering system.

The EPS (electric power steering) system is one of the safety-critical systems in modern automotive control system. In this thesis, a dynamic model for an EPS system was derived. And then, new controller architecture with an extra logical controller in the steering feedback loop, called motion controller, was proposed to increase the sensitivity of the control system to the system. Finally, a robust control system was designed based on the proposed EPS model and the new controller architecture. The disturbance signal is the high frequency part of the torque from road to the rack and pinion. The disturbance can be modeled as bounded noise or white noise. Hinfinity technology is often used to attenuate the bounded noise. The control design was divided into two stages. The first stage is to design a PI motor controller to increase the reaction speed. The next stage is to design an Hinfinity motion controller based on the frequency character of the disturbance signal. Simulations were carried out to demonstrate the controller\u27s performance and robustness characteristics for both bounded noise and white noise. (Abstract shortened by UMI.)Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .L552. Source: Masters Abstracts International, Volume: 43-05, page: 1780. Adviser: X. Chen. Thesis (M.A.Sc.)--University of Windsor (Canada), 2004

The empact CVT : dynamics and control of an electromechanically actuated CVT

The large ratio coverage of a CVT and the possibility to choose the engine speed in a wide range independently of the vehicle speed enables the ICE to operate at more fuel economic operating points, making the vehicle potentially more fuel efficient. Unfortunately, because the energy dissipation of the CVT itself is higher than that of a manual transmission, this efficiency improvement is partly lost. The main power losses in the CVT are due to the inefficient hydraulic actuation system and the excessive clamping forces used to prevent the belt from excessive slippage. Direct control of the slip can significantly increase the efficiency. Due to the low actuation stiffness at low hydraulic pressures, the hydraulically actuated CVT is not well suited for slip control. The Empact CVT, developed at the TU/e, is an electromechanically actuated pushbelt type CVT, which has a high stiffness at low clamping forces and is suitable for slip control. This system reduces the steady-state losses, which are dominantly present in a hydraulic system. The goals of this research are to achieve optimal efficiency of this system, to obtain good tracking performance and to prevent the pushbelt from slipping excessively. These objectives are experimentally validated at a Empact prototype, which is tested at a test rig and implemented in an Audi A3 2.0 FSI. The Empact CVT uses two servomotors to actuate the moveable pulley sheaves. To decouple the rotation of the input and output shaft from the servomotor rotations, a double epicyclic set is used at each shaft. The system is designed, such that one (primary) actuator accounts for the ratio changes and one (secondary) actuator sets the clamping forces in the variator. To optimally use the efficiency potential of the Empact system, the slip in the variator must be controlled. In this way, the clamping forces reduce to small values, thereby reducing the friction forces in the gears, spindles and bearings. Efficiency improvements of up to 20 [%] can then be reached at partial load (during 75 [%] of the duration of the FTP72 cycle) compared to a conventionally controlled CK2 147 transmission and efficiency gains of up to 10 [%] compared to an optimally, slip controlled CK2. To gain insight in the physical behavior of the Empact CVT, a multi-body model of the system has been developed, which incorporates a dynamical description of all major components of the test setup. Results show a realistic behavior of the system for both stationary and transient situations. Although this nonlinear simulation model gives a basis for control design and yields a realistic description of the closed loop system, for the actual control design an approximate, linear plant model that describes the frequency domain behavior of the system is estimated. These linearized descriptions are obtained from the simulation model using approximate realization from pulse response data. An iterative model identification and control design procedure is used, such that the plant is estimated in closed loop. In this way, the uncertainty in the frequency range of importance for the design of the controllers is reduced, which leads to less conservative control designs. Parallel to the identification and control design with the simulation model, this procedure is also applied for the test setup. Due to high measurement noise and excessive friction in the system, the quality of the approximated plants at the test setup is relatively low. The time responses are however comparable to the results from the simulation model. An important constraint for the controlled system is that slip cannot be controlled under all operating conditions. At low variator speeds and low loads, the slip controller must be switched off. A decentralized control structure is chosen. Pairing of the in- and outputs is primarily based on the mechanical design of the Empact CVT and are supported by a interaction analysis. The controllers are designed using a sequential loop closing procedure, in which the slip loop is closed last, such that stability of other loops is guaranteed independent of the switching of the slip controller. Using manual loop-shaping, decentralized lead-lag controllers are designed. Nominal stability and performance can be guaranteed. To obtain robust performance, gain scheduling of the slip controller is implemented. Resulting closed loop bandwidth is 8-10 [Hz] for both the ratio and slip control loops. Because the slip dynamics is not well defined at low or zero variator speeds, the slip controller is partly switched off below 2 [km/h]. Both the simulation model and the experimental setup show very good results for disturbance rejection and tracking performance. Torque disturbances of up to 100 [Nm], applied at the secondary variator shaft, can be suppressed within 0.2 [sec] for all ratios. The ratio tracking error is very small compared to conventional CVT systems. Experimental evaluation of the Empact CVT at the test rig showed that the average power consumption of the Empact CVT on the FTP72 cycle is 155 [W], whereas conventional hydraulically actuated CVTs consume over 400 [W] on the average at this drive cycle. Efficiencies of 90 [%], which is close to the maximum efficiency of the Empact CVT, are reached during these experiments. Evaluation of the Empact CVT in an Audi A3 2.0 FSI shows similar performance. Overall, an efficiency improvement of up to 10 [%] is obtained with the Empact CVT compared to a comparable size hydraulically actuated CVT

Computer simulation of a pilot in V/STOL aircraft control loops

The objective was to develop a computerized adaptive pilot model for the computer model of the research aircraft, the Harrier II AV-8B V/STOL with special emphasis on propulsion control. In fact, two versions of the adaptive pilot are given. The first, simply called the Adaptive Control Model (ACM) of a pilot includes a parameter estimation algorithm for the parameters of the aircraft and an adaption scheme based on the root locus of the poles of the pilot controlled aircraft. The second, called the Optimal Control Model of the pilot (OCM), includes an adaption algorithm and an optimal control algorithm. These computer simulations were developed as a part of the ongoing research program in pilot model simulation supported by NASA Lewis from April 1, 1985 to August 30, 1986 under NASA Grant NAG 3-606 and from September 1, 1986 through November 30, 1988 under NASA Grant NAG 3-729. Once installed, these pilot models permitted the computer simulation of the pilot model to close all of the control loops normally closed by a pilot actually manipulating the control variables. The current version of this has permitted a baseline comparison of various qualitative and quantitative performance indices for propulsion control, the control loops and the work load on the pilot. Actual data for an aircraft flown by a human pilot furnished by NASA was compared to the outputs furnished by the computerized pilot and found to be favorable

Engine thermal management with model predictive control

The global greenhouse gas CO2 emission from the transportation sector is very significant.To reduce this gas emission, EU has set an average target of not more than 95 CO2/km for new passenger cars by the year 2020. A great reduction is still required to achieve the CO2 emission target in 2020, and many different approaches are being considered. This thesis focuses on the thermal management of the engine as an area that promise significant improvement of fuel efficiency with relatively small changes. The review of the literature shows that thermal management can improve engine efficiency through the friction reduction, improved air-fuel mixing, reduced heat loss, increased engine volumetric efficiency, suppressed knock, reduce radiator fan speed and reduction of other toxic emissions such as CO, HC and NOx. Like heat loss and friction, most emissions can be reduced in high temperature condition, but this may lead to poor volumetric efficiency and make the engine more prone to knock. The temperature trade-off study is conducted in simulation using a GT-SUITE engine model coupled with the FE in-cylinder wall structure and cooling system. The result is a map of the best operating temperature over engine speed and load. To quantify the benefit of this map, eight driving styles from the legislative and research test cycles are being compared using an immediate application of the optimal temperature, and significant improvements are found for urban style driving, while operation at higher load (motorway style driving) shows only small efficiency gains. The fuel consumption saving predicted in the urban style of driving is more than 4%. This assess the chance of following the temperature set point over a cycle, the temperature reference is analysed for all eight types of drive cycles using autocorrelation, lag plot and power spectral density. The analysis consistently shows that the highest volatility is recorded in the Artemis Urban Drive Cycle: the autocorrelation disappears after only 5.4 seconds, while the power spectral density shows a drop off around 0.09Hz. This means fast control action is required to implement the optimal temperature before it changes again. Model Predictive Control (MPC) is an optimal controller with a receding horizon, and it is well known for its ability to handle multivariable control problems for linear systems with input and state limits. The MPC controller can anticipate future events and can take control actions accordingly, especially if disturbances are known in advance. The main difficulty when applying MPC to thermal management is the non-linearity caused by changes in flow rate. Manipulating both the water pump and valve improves the control authority, but it also amplifies the nonlinearity of the system. Common linearization approaches like Jacobian Linearization around one or several operating points are tested, by found to be only moderately successful. Instead, a novel approach is pursued using feedback linearization of the plant model. This uses an algebraic transformation of the plant inputs to turn the nonlinear systems dynamics into a fully or predominantly linear system. The MPC controller can work with the linear model, while the actual control inputs are found using an inverse transformation. The Feedback Linearization MPC of the cooling system model is implemented and testing using MathWork Simulink®. The process includes the model transformation approach, model fitting, the transformation of the constraints and the tuning of the MPC controller. The simulation shows good temperature tracking performance, and this demonstrates that a MPC controller with feedback linearization is a suitable approach to thermal management. The controller strategy is then validated in a test rig replicating an actual engine cooling system. The new MPC controller is again evaluated over the eight driving cycles. The average water pump speed is reduced by 9.1% compared to the conventional cooling system, while maintaining good temperature tracking. The controller performance further improves with future disturbance anticipation by 20.5% for the temperature tracking (calculated by RMSE), 6.8% reduction of the average water pump speed, 47.3% reduction of the average valve movement and 34.0% reduction of the average radiator fan speed

Robust Control of Wide Bandgap Power Electronics Device Enabled Smart Grid

abstract: In recent years, wide bandgap (WBG) devices enable power converters with higher power density and higher efficiency. On the other hand, smart grid technologies are getting mature due to new battery technology and computer technology. In the near future, the two technologies will form the next generation of smart grid enabled by WBG devices. This dissertation deals with two applications: silicon carbide (SiC) device used for medium voltage level interface (7.2 kV to 240 V) and gallium nitride (GaN) device used for low voltage level interface (240 V/120 V). A 20 kW solid state transformer (SST) is designed with 6 kHz switching frequency SiC rectifier. Then three robust control design methods are proposed for each of its smart grid operation modes. In grid connected mode, a new LCL filter design method is proposed considering grid voltage THD, grid current THD and current regulation loop robust stability with respect to the grid impedance change. In grid islanded mode, µ synthesis method combined with variable structure control is used to design a robust controller for grid voltage regulation. For grid emergency mode, multivariable controller designed using H infinity synthesis method is proposed for accurate power sharing. Controller-hardware-in-the-loop (CHIL) testbed considering 7-SST system is setup with Real Time Digital Simulator (RTDS). The real TMS320F28335 DSP and Spartan 6 FPGA control board is used to interface a switching model SST in RTDS. And the proposed control methods are tested. For low voltage level application, a 3.3 kW smart grid hardware is built with 3 GaN inverters. The inverters are designed with the GaN device characterized using the proposed multi-function double pulse tester. The inverter is controlled by onboard TMS320F28379D dual core DSP with 200 kHz sampling frequency. Each inverter is tested to process 2.2 kW power with overall efficiency of 96.5 % at room temperature. The smart grid monitor system and fault interrupt devices (FID) based on Arduino Mega2560 are built and tested. The smart grid cooperates with GaN inverters through CAN bus communication. At last, the three GaN inverters smart grid achieved the function of grid connected to islanded mode smooth transitionDissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Real-time multi-domain optimization controller for multi-motor electric vehicles using automotive-suitable methods and heterogeneous embedded platforms

Los capítulos 2,3 y 7 están sujetos a confidencialidad por el autor. 145 p.In this Thesis, an elaborate control solution combining Machine Learning and Soft Computing techniques has been developed, targeting a chal lenging vehicle dynamics application aiming to optimize the torque distribution across the wheels with four independent electric motors.The technological context that has motivated this research brings together potential -and challenges- from multiple dom ains: new automotive powertrain topologies with increased degrees of freedom and controllability, which can be approached with innovative Machine Learning algorithm concepts, being implementable by exploiting the computational capacity of modern heterogeneous embedded platforms and automated toolchains. The complex relations among these three domains that enable the potential for great enhancements, do contrast with the fourth domain in this context: challenging constraints brought by industrial aspects and safe ty regulations. The innovative control architecture that has been conce ived combines Neural Networks as Virtual Sensor for unmeasurable forces , with a multi-objective optimization function driven by Fuzzy Logic , which defines priorities basing on the real -time driving situation. The fundamental principle is to enhance vehicle dynamics by implementing a Torque Vectoring controller that prevents wheel slip using the inputs provided by the Neural Network. Complementary optimization objectives are effici ency, thermal stress and smoothness. Safety -critical concerns are addressed through architectural and functional measures.Two main phases can be identified across the activities and milestones achieved in this work. In a first phase, a baseline Torque Vectoring controller was implemented on an embedded platform and -benefiting from a seamless transition using Hardware-in -the -Loop - it was integrated into a real Motor -in -Wheel vehicle for race track tests. Having validated the concept, framework, methodology and models, a second simulation-based phase proceeds to develop the more sophisticated controller, targeting a more capable vehicle, leading to the final solution of this work. Besides, this concept was further evolved to support a joint research work which lead to outstanding FPGA and GPU based embedded implementations of Neural Networks. Ultimately, the different building blocks that compose this work have shown results that have met or exceeded the expectations, both on technical and conceptual level. The highly non-linear multi-variable (and multi-objective) control problem was tackled. Neural Network estimations are accurate, performance metrics in general -and vehicle dynamics and efficiency in particular- are clearly improved, Fuzzy Logic and optimization behave as expected, and efficient embedded implementation is shown to be viable. Consequently, the proposed control concept -and the surrounding solutions and enablers- have proven their qualities in what respects to functionality, performance, implementability and industry suitability.The most relevant contributions to be highlighted are firstly each of the algorithms and functions that are implemented in the controller solutions and , ultimately, the whole control concept itself with the architectural approaches it involves. Besides multiple enablers which are exploitable for future work have been provided, as well as an illustrative insight into the intricacies of a vivid technological context, showcasing how they can be harmonized. Furthermore, multiple international activities in both academic and professional contexts -which have provided enrichment as well as acknowledgement, for this work-, have led to several publications, two high-impact journal papers and collateral work products of diverse nature

Real-time multi-domain optimization controller for multi-motor electric vehicles using automotive-suitable methods and heterogeneous embedded platforms

Los capítulos 2,3 y 7 están sujetos a confidencialidad por el autor. 145 p.In this Thesis, an elaborate control solution combining Machine Learning and Soft Computing techniques has been developed, targeting a chal lenging vehicle dynamics application aiming to optimize the torque distribution across the wheels with four independent electric motors.The technological context that has motivated this research brings together potential -and challenges- from multiple dom ains: new automotive powertrain topologies with increased degrees of freedom and controllability, which can be approached with innovative Machine Learning algorithm concepts, being implementable by exploiting the computational capacity of modern heterogeneous embedded platforms and automated toolchains. The complex relations among these three domains that enable the potential for great enhancements, do contrast with the fourth domain in this context: challenging constraints brought by industrial aspects and safe ty regulations. The innovative control architecture that has been conce ived combines Neural Networks as Virtual Sensor for unmeasurable forces , with a multi-objective optimization function driven by Fuzzy Logic , which defines priorities basing on the real -time driving situation. The fundamental principle is to enhance vehicle dynamics by implementing a Torque Vectoring controller that prevents wheel slip using the inputs provided by the Neural Network. Complementary optimization objectives are effici ency, thermal stress and smoothness. Safety -critical concerns are addressed through architectural and functional measures.Two main phases can be identified across the activities and milestones achieved in this work. In a first phase, a baseline Torque Vectoring controller was implemented on an embedded platform and -benefiting from a seamless transition using Hardware-in -the -Loop - it was integrated into a real Motor -in -Wheel vehicle for race track tests. Having validated the concept, framework, methodology and models, a second simulation-based phase proceeds to develop the more sophisticated controller, targeting a more capable vehicle, leading to the final solution of this work. Besides, this concept was further evolved to support a joint research work which lead to outstanding FPGA and GPU based embedded implementations of Neural Networks. Ultimately, the different building blocks that compose this work have shown results that have met or exceeded the expectations, both on technical and conceptual level. The highly non-linear multi-variable (and multi-objective) control problem was tackled. Neural Network estimations are accurate, performance metrics in general -and vehicle dynamics and efficiency in particular- are clearly improved, Fuzzy Logic and optimization behave as expected, and efficient embedded implementation is shown to be viable. Consequently, the proposed control concept -and the surrounding solutions and enablers- have proven their qualities in what respects to functionality, performance, implementability and industry suitability.The most relevant contributions to be highlighted are firstly each of the algorithms and functions that are implemented in the controller solutions and , ultimately, the whole control concept itself with the architectural approaches it involves. Besides multiple enablers which are exploitable for future work have been provided, as well as an illustrative insight into the intricacies of a vivid technological context, showcasing how they can be harmonized. Furthermore, multiple international activities in both academic and professional contexts -which have provided enrichment as well as acknowledgement, for this work-, have led to several publications, two high-impact journal papers and collateral work products of diverse nature

Optimal air and fuel-path control of a diesel engine

The work reported in this thesis explores innovative control structures and controller design for a heavy duty Caterpillar C6.6 diesel engine. The aim of the work is not only to demonstrate the optimisation of engine performance in terms of fuel consumption, NOx and soot emissions, but also to explore ways to reduce lengthy calibration time and its associated high costs. The test engine is equipped with high pressure exhaust gas recirculation (EGR) and a variable geometry turbocharger (VGT). Consequently, there are two principal inputs in the air-path: EGR valve position and VGT vane position. The fuel injection system is common rail, with injectors electrically actuated and includes a multi-pulse injection mode. With two-pulse injection mode, there are as many as five control variables in the fuel-path needing to be adjusted for different engine operating conditions. [Continues.

Practical Solutions to the Non-Minimum Phase and Vibration Problems Under the Disturbance Rejection Paradigm

This dissertation tackles two kinds of control problems under the disturbance rejection paradigm (DRP): 1) the general problem of non-minimum phase (NMP) systems, such as systems with right half plane (RHP) zeros and those with time delay 2) the specific problem of vibration, a prevailing problem facing practicing engineers in the real world of industrial control. It is shown that the DRP brings to the table a refreshingly novel way of thinking in tackling the persistently challenging problems in control. In particular, the problem of NMP has confounded researchers for decades in trying to find a satisfactory solution that is both rigorous and practical. The active disturbance rejection control (ADRC), originated from DRP, provides a potential solution. Even more intriguingly, the DRP provides a new framework to tackle the ubiquitous problem of vibration, whether it is found in the resonant modes in industrial motion control with compliant load, which is almost always the case, or in the microphonics of superconducting radio frequency (SRF) cavities in high energy particle accelerators. That is, whether the vibration is caused by the environment or by the characteristics of process dynamics, DRP provides a single framework under which the problem is better understood and resolved. New solutions are tested and validated in both simulations and experiments, demonstrating the superiority of the new design over the previous ones. For systems with time delay, the stability characteristic of the proposed solution is analyze