# Your search: "author:"Horowitz, Roberto""

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## Scholarly Works (39 results)

In this report, we describe the research carried out under PATH Task Order 4208. The objective of this project was to bridge the gap between the Automated Highway System (AHS) simulators SmartAHS and SmartCAP, by implementing an integrated AHS micro-meso simulation environment for analyzing a large-scale AHS network. In fulfillment of this goal, a meso-microscale traffic simulator was developed that allows a stationary region of microsimulation to be defined within a larger, mesosimulated AHS. This simulator permits analysis of traffic behavior in situations where both vehicle-level (microscopic) and aggregate-flow (mesoscopic) effects are important, while avoiding the prohibitive computational cost of microsimulating a large-scale AHS. The accomplishments of this project, including the development of the meso-micro batch compiler, user interface, and a manual traffic extension to SmartCAP, are detailed in this report.

Executive Summary A large portion of PATH's e.ort in the area of Advanced Vehicle Control Systems (AVCS) during the past several years has focused on the development of theory and technologies that may signi.cantly increase highway safety and capacity through the use of Automated Highway Systems (AHS). To continue with the analysis of AHS feasibility, it is necessary to perform exhaustive large scale simulations that will provide information on the impact of the proposed technologies upon system safety and capacity. In particular, it is necessary to study and evaluate how faults in a vehicle impact the overall AHS performance and capacity, how the roadside control systems can react to these faults and perform degraded-mode activities at the higher hierarchical levels, and how the roadside control system can detect faults either in the vehicle or in its own infrastructure. When the response of an AHS is being simulated, di.erent degrees of precision are required. In some sections of the highway the detailed behavior in each vehicle in the section must be simulated to model vehicle or infrastructure faults, or test vehicle level fault detection and handling algorithms or degraded mode maneuvers. This level of AHS simulation, where the dynamics of each vehicle is simulated, is called microsimulation. The AHS microsimulation software currently under development at PATH is SmartAHS. This software package is based on the hybrid systems language SHIFT [7]. In most sections, it is convenient to model the highway using .uid-like conservation models of tra.c .ow, applied to the average tra.c characteristics in that section, such as density, .ow, and velocity. This level of AHS simulation is called mesosimulation. Mesoscale simulators do not provide information about particular vehicles but are instead capable of simulating large highway networks. The mesosimulation software used by PATH for analyzing Automated Highway Systems is SmartCAP [5]. Although it is in principle possible to microsimulate a large scale AHS, the computational cost is prohibitively high and, in many cases yields no advantage over mesosimulation, except for the few sections of the highway where it is needed. The aim of this project is to bridge the simulation gap between SmartAHS and SmartCAP, by implementing an integrated AHS micro-meso simulation environment for simulating a large scale AHS network, where both SmartCAP and SmartAHS run simultaneously and interact with each other. In this simulation environment, most of the highway sections in the AHS are simulated by the SmartCAP mesosimulation software, except for one or more sections, which are simulated by the SmartAHS microsimulation software. Highway sections that are microsimulated using SmartAHS are referred to as u-windows. The topology of the entire AHS, as well as the location and length of each u-window is set up by the user, by means of user interface software, named mminterface. The bene.ts of the meso-micro simulation environment are best realized when u-windows are placed in the regions where microscopic e.ects are of greatest interest; by allowing the remainder of the AHS to be simulated by the more computationally e.cient SmartCAP, the meso-micro software is able to simulate the AHS at a lower computational cost than stand-alone SmartAHS. This .nal report documents the theoretical design and software implementation of the integrated micro-meso simulator. It also provides a brief tutorial on its use, including a simple AHS simulation example. The following tasks were executed in this project: 1. The capabilities of SmartAHS were extended by developing a simpli.ed sensor architecture, a simpli.ed vehicle-roadway environment processor, and a simpli.ed set of regulation-layer components. Under certain conditions, the simpli.ed set produces identical results as the full components, and increases simulation speed 4 to 5 fold. 2 2. The SmartCAP activity model was extended to include platooning and join/split maneuvers. 3. The interface between SmartCAP and SmartAHS was designed and implemented in SHIFT. A SmartAHS component was created to schedule and monitor all aspects of the interface between SmartCAP and SmartAHS. 4. A batch compiler and a MATLAB-based visual interface were created to allow the user to input simulation parameters, de.ne the highway topology, and view both mesoscale and microscale simulation outputs simultaneously. 5. The developed integrated meso/microscale simulation software was tested for di.erent scenarios. The PATH hierarchical control architecture, speci.cally the link, coordination, regulation, and physical layers, was tested in the meso-microscale simulator. The meso-micro simulator is well-suited for testing the response of an AHS to situations that are characterized by localized microscale phenomena, such as: the stalling of an individual vehicle, oversaturation of a particular artery due to external factors (e.g. a sporting event), emergency vehicle maneuvers, and changes in highway capacity due to di.erent on-ramp metering policies. The use of the software is described in Sec. 6 using a simple simulation example. The example shows a tra.c density wave propagating forward from the mesoscale region into the microscale region, thus demonstrating the functionality of the software.

This dissertation presents algorithmic tools that are useful to transportation engineers for freeway traffic modeling and control. A modeling framework that utilizes the link-node cell transmission model (LN-CTM) to simulate traffic dynamics on a chosen freeway network is presented here. A data driven approach, which utilizes available detector measurements on the freeway network to calibrate and specify the model is also illustrated. Flow measurements in ramps, which are needed to specify demands and routing characteristics for the freeway, are usually not available. Two novel imputation algorithms which estimate the missing ramp flows in the freeway network are presented. These algorithms employ a model based estimation procedure, that calculates the unknown on-ramp flows and off-ramp split ratios which can be fed into the model to match the observed mainline density and flow measurements. A detailed analysis of the convergence of these algorithms is presented, along with the advantages of these individual approaches. The final model, specified with the imputed ramp flows is able to replicate the traffic dynamics with good accuracy, as seen by error rates around 5-8% for density/flows contours, and the accurate replication of the bottleneck locations. These imputation algorithms, used within our modeling framework, enables a user to build a freeway model simulating multiple days of freeway behavior, within a week.

A model based optimal predictive controller for freeway congestion control, which utilizes the LN-CTM as its underlying model is also presented. The approach searches for solutions represented by a combination of ramp metering and variable speed limits. The optimization problem corresponding to the optimal control problem based on the LN-CTM is non-convex and non-linear. A relaxation method is presented to solve this problem efficiently using an equivalent linear program, before generating the solution to the original problem using a new mapping algorithm. The predictive controller is also extended to cover situations when ramp weaving and/or capacity drop exists in the freeway network. In this case, a set of heuristics are presented and the optimal control problem is solved using a sequence of linear programs, before mapping the solutions back to the original problem.

This dissertation studies a series of freeway and arterial traffic modeling, estimation and control methodologies.

First, it investigates the Link-Node Cell Transmission Model's (LN-CTM's) ability to model arterial traffic. The LN-CTM is a modification of the cell transmission model developed by Daganzo. The investigation utilizes traffic data collected on an arterial segment in Los Angeles, California, and a link-node cell transmission model, with some adaptations to the arterial traffic, is constructed for the studied location. The simulated flow and the simulation travel time were compared with field measurements to evaluate the modeling accuracy.

Second, an algorithm for estimating turning proportions is proposed in this dissertation. The knowledge about turning proportions at street intersections is a frequent input for traffic models, but it is often difficult to measure directly. Compared with previous estimation methods used to solve this problem, the proposed method can be used with only half the detectors employed in the conventional complete detector configuration. The proposed method formulates the estimation problem as a constrained least squares problem, and a recursive solving procedure is given. A simulation study was carried out to demonstrate the accuracy and efficiency of the proposed algorithm.

In addition to addressing arterial traffic modeling and estimation problems, this dissertation also studies a freeway traffic control strategy and a freeway and arterial coordinated control strategy. It presents a coordinated control strategy of variable speed limits (VSL) and ramp metering to address freeway congestion caused by weaving effects. In this strategy, variable speed limits are designed to maximize the bottleneck flow, and ramp metering is designed to minimize travel time in a model predictive control frame work. A microscopic simulation based on the I-80 at Emeryville, California was built to evaluate the strategy, and the results showed that the traffic performance was significantly improved .

Following the freeway control study, this dissertation discusses the coordinated control of freeways and arterials. In current practice, traffic controls on freeways and on arterials are independent. In order to coordinate these two systems for better performance, a control strategy covering the freeway ramp metering and the signal control at the adjacent intersection is developed. This control strategy uses upstream ALINEA, which is a well-known control algorithm, for ramp metering to locally maximize freeway throughput. For the intersection signal control, the proposed control strategy distributes green splits by taking into account both the available on-ramp space and the demands of all intersection movements. A microscopic simulation of traffic in an arterial intersection with flow discharge to a freeway on-ramp, which is calibrated using the data collected at San Jose, California, is created to evaluate the performance of the proposed control strategy. The results showed that the proposed strategy can reduce intersection delay by 8%, compared to the current field-implemented control strategy.

Transportation mobility can be improved not only by traffic management strategies, but also through the deployment of advanced vehicle technologies. This dissertation also investigates the impact of Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC) on highway capacity. A freeway microscopic traffic simulation model is constructed to evaluate how the freeway lane flow capacity change under different penetration rates of vehicles equipped with either ACC or CACC system. This simulation model is based on a calibrated driver behavioral model and the vehicle dynamics of the ACC and CACC systems. The model also utilizes data collected from a real experiment in which drivers' selections of time gaps are recorded. The simulation shows that highway capacity can be significantly increased when the CACC vehicles reach a moderate to high market penetration, as compared to both regular manually driven vehicles and vehicles equipped with only ACC.

The demand for online storage has been increasing significantly during the last few years. Hard disk drives are the primary storage devices used in data centers for storing these online contents. The servo assembly of the dual-stage Hard Disk Drive (HDD) is composed of the Voice Coil Motor (VCM) and the Mili-Actuator (MA), where the VCM is responsible for coarse positioning at low frequency regions and the MA is responsible for fine positioning at high frequency regions. Controlling these two actuators is very critical in precision positioning of the read/write head, which is mounted at the edge of the servo assembly. In this dissertation, the precision positioning of the head during the self-servo writing process as well as feed-forward and feedback controls in the track following mode are considered.

This dissertation discusses three control design methodologies for hard disk drives servo systems, in order to improve their performance as well as their reliability. The first is a state estimator for non-uniform sampled systems with irregularities in the measurement sampling time, which estimates the states at a uniform sampling time. The second is an online uncertainty identification algorithm, which parameterizes and identifies the uncertain part of transfer functions in a dual-stage HDD. The third is a frequency based data-driven control design methodology, which considers mixed H_2/H_infinity control objectives and is able to synthesize track following servo systems for dual stage actuators utilizing only the frequency response measurement data, without the need of identifying the models of the actuators.

The state estimator design for non-uniform sampled systems with irregularity in the measurement sampling time is considered, where it is proposed to design an observer to estimate the states at a uniform sampling time. This observer is designed using a time-varying Kalman filter as well as a gain-scheduling observer. The Kalman filter has the optimal performance, while the gain-scheduling observer requires relatively lower computational power. Simulations are conducted involving the self-servo writing process in hard disk drives, where performance as well as computational complexity of these two observers are compared under different noise scenarios.

Uncertainties in system dynamics can change the closed loop transfer functions and affect the performance or even stability of the control algorithm. These uncertainties are parameterized as stable terms using coprime factorizations, and are identified in an online fashion. The uncertainty identification, in comparison to the complete transfer function identification, requires less computational power as well as a smaller order for the identified transfer function.

The proposed online uncertainty identification algorithm is utilized to factorize and identify the uncertain part of transfer functions in a dual-stage Hard Disk Drive (HDD). The dual-stage actuators' gains and resonance modes are affected by temperature variations, which in turn affect all closed loop transfer functions. Therefore, these transfer functions must be periodically updated in order to guarantee the convergence and stability criteria for the adaptive Repeatable Run-Out (RRO) following algorithm proposed in [61, 62]. Experimental results conducted on a hard disk drive equipped with dual-stage actuation, confirm the effectiveness of the proposed identification algorithm.

A frequency based data-driven control design considering mixed H_2/H_infinity control objectives is developed for multiple input-single output systems. The main advantage of the data-driven control over the model-based control is its ability to use the frequency response measurements of the controlled plant directly without the need to identify a model for the plant. In the proposed methodology, multiple sets of measurements can be considered in the design process to accommodate variations in the system dynamics. The controller is obtained by translating the mixed H_2/H_infinity control objectives into a convex optimization problem. The H_infinity norm is used to shape closed loop transfer functions and guarantee closed loop stability, while the H_2 norm is used to constrain and/or minimize the variance of signals in the time domain.

The proposed data-driven design methodology is used to design a track following controller for a dual-stage HDD. The sensitivity decoupling structure[34] is considered as the controller structure. The

compensators inside this controller structure are designed and compared by decoupling the system into two single input-single-output systems as well as solving for a single input-double output controller.

Control methodologies for deterministic disturbance rejection and trajectory tracking have been of great interest to researchers in the fields of controls, mechatronics, robotics and signal processing in the past two decades. The applications of these methods span a wide range from satellite attitude control requiring an accuracy of a few meters, to positioning of the read/write head in hard disk drives with an accuracy of less than one nanometer. This dissertation addresses the problem of trajectory tracking and deterministic disturbance rejection in discrete time systems when the trajectory/disturbance is unknown, but can be realized as an a ne combination of known basis functions. Despite the prior work on this problem that assumes known and time invariant plant dynamics, we consider multi input single output systems with unknown dynamics. Moreover, we investigate the cases where the disturbance or system dynamics varies slowly or abruptly but infrequently. Within the broad class of disturbances/trajectories that satisfy the stated criteria, an elaborate study is conducted on periodic and superposition of multiple sinusoidal sequences. We propose two novel adaptive control methods for the aforementioned problem. The first scheme can be classified as an indirect adaptive algorithm since it consists of two parts, namely a system identification mechanism that provides a dynamic model of the closed loop system, and the adaptive compensator which deploys the aforementioned dynamic model to synthesize the control law. The second proposed method is a direct adaptive controller, meaning that the control law is generated directly and the stated separation is not possible.

Besides providing theoretical guarantees, we experimentally evaluate our algorithms on a challenging control task for nano-scale positioning of the read write head in a dual stage hard disk drive (HDD). Even with the advent of NAND ash based data storage devices, the HDD continues to thrive as the most cost effective, reliable solution for rewritable, very high density data storage. It remains a key technology particularly with the tremendously growing popularity of server based cloud computing and novel hybrid enterprise storage solutions. We described that the control methodologies that can address the problem under our study are crucial for Bit Patterned Media Recording which is one of the two breakthroughs in magnetic recording that have been immensely investigated in the past few years. Extensive computer simulations and implementation on a digital signal processor unit are performed to validate the effectiveness of our proposed algorithms in full spectrum compensation of the repeatable runout in dual stage HDDs. This application introduces unique control challenges since it requires estimating a very large number of parameters that is order(s) of magnitude greater than prior work and frequency contents span from 120Hz to extremely large values (above 20KHz) where the plant dynamic uncertainties are large.

In this dissertation, the design of servo control algorithms is investigated to produce high-density and cost-effective hard disk drives (HDDs). In order to sustain the continuing increase of HDD data storage density, dual-stage actuator servo systems using a secondary micro-actuator have been developed to improve the precision of read/write head positioning control by increasing their servo bandwidth. In this dissertation, the modeling and control design of dual-stage track-following servos are considered. Specifically, two track-following control algorithms for dual-stage HDDs are developed. The designed controllers were implemented and evaluated on a disk drive with a PZT-actuated suspension-based dual-stage servo system.

Usually, the feedback position error signal (PES) in HDDs is sampled on some specified servo sectors with an equidistant sampling interval, which implies that HDD servo systems with a regular sampling rate can be modeled as linear time-invariant (LTI) systems. However, sampling intervals for HDD servo systems are not always equidistant and sometimes, an irregular sampling rate due to missing PES sampling data is unavoidable. With the natural periodicity of HDDs, which is related to the disk rotation, those HDD servo systems with missing PES samples can be modeled as linear periodically time-varying (LPTV) systems.

For the control synthesis of HDD servos with irregular sampling rates, an explicit optimal H_infinity control synthesis algorithm for general LPTV systems is first obtained by solving discrete Riccati equations. Then, the optimal H_infinity track-following control for irregular-sampling-rate servos is synthesized. Simulation and experiment studies, which have been carried out on a set of actual single-stage hard disk drives, demonstrate that the proposed control synthesis technique is able to handle irregular sampling rates and can be used to conveniently design a track-following servo that achieves the robust performance of a desired error rejection function for disturbance attenuation. Moreover, experiment results show that compared to the currently-used methodology for irregular sampling rates, the proposed control algorithm has significantly improved the servo performance.

In addition, the feedback signal in HDD servos is generated from the servo patterns that must be pre-recorded using servo track writing process before the HDD can be used. Thus, the quality of the servo track writing process is also crucial to the accuracy of positioning read/write head. Recently, self-servo track writing has been developed in order to improve the quality of the written servo patterns and reduce the cost of servo track writing process. This dissertation considers two novel controller synthesis methodologies employing a feedforward control structure for performing concentric self-servo track writing in hard disk drives. Simulation results confirm that the two proposed control synthesis methodologies prevent error propagation from the previously written tracks and significantly improve servo track writing performance.

This dissertation investigates the design of control algorithms and calibration methods for

Microscale Rate Integrating Gyroscopes (MRIGs). As its name implies, a MRIG operates

in rate integrating mode and can directly measure the rotation angle of the base where it

is mounted. However, the MRIG mechanical system does not spontaneously operate in a

rate integrating mode, but requires an active controller. Such a controller enables the MRIG to oscillate in a specific pattern that is related to the input rotation angle in a measurable way.

Conventional micro-machined gyroscopes (i.e. MEMS gyroscopes) operate in rate mode (as apposed to rate integrating mode). That is, the gyroscope directly measures the rotation rate of the base. The measured rotation rate is then numerically integrated over time to obtain the input rotation angle. The main drawback of this measuring mechanism is that, by integrating, the rate measurement error will propagate over time, causing the angle measurement to drift from the real input angle. MRIG, by its operating principle, can directly measure the input rotation angle; hence it suffers from no such error propagation.

A well-known control scheme for rate integrating gyroscopes was proposed by Lynch in 1995 [51]. This control scheme has demonstrated its efficacy on precisely fabricated rate integrating gyroscopes such as Hemispherical Resonance Gyroscopes (HRGs). However, for MRIGs fabricated by micro-fabrication technology, fabrication imperfections significantly degrade the gyro performance. In addition, the Lynch-proposed-scheme is essentially nonlinear. As a consequence, the controller performance is hard to predict and analyze prior to real tests.

In this dissertation, a novel demodulation method is developed to transform the original nonlinear control problem into a linear time invariant controller design problem. This technique is based on the averaging method proposed by Lynch [50] but enables the use of well studied linear system theory for MRIG controller design and analysis. The resulting controller design for MRIGs is much more tractable and the performance is rather predictable. This fundamental improvement also opens up new opportunities for implementing and analyzing control systems based on linear control theory.

Two schemes are proposed in this dissertation to compensate for the parameter mismatches caused by fabrication imperfections. The first one is based on electrostatic spring softening and tuning. The basic operation principle is first introduced. Then a full derivation of this method on a real MRIG configuration is conducted. Experimental results confirm that this compensation scheme can significantly attenuate the parameter mismatch.

The other compensation scheme considered in this dissertation is an adaptive compensation scheme consisting of three feedforward controllers. Each of them runs on top of the corresponding feedback control loop and estimates and compensates parameter mismatches in real time. We also present a stability and convergence analysis that shows such adaptive controllers converge to the correct values and perfectly cancel the parameter mismatch. A simulation study performed on a MRIG model also confirms the efficacy of the compensation scheme. Then a self-calibration process is proposed to automatically calibrate the gyroscope. This self-calibration method requires no human involvement or auxiliary device, hence enables the gyroscope to calibrate itself whenever necessary, even in use.

This dissertation presents system identification, fault detection and fault handling methodologies for automatically building calibrated models of freeway traffic flow. Using these methodologies, data driven algorithms were developed as part of a larger scheme of a suite of software tools designed to provide traffic engineers with a simulation platform where various traffic planning strategies can be analyzed. The algorithms that are presented work with loop detector data that are gathered from California freeways.

The system identification deploys a constrained linear regression analysis that estimates the so-called fundamental diagram relationship between flow and density at the location of a given sensor. A triangular fundamental diagram is assumed that establishes a bi-modally linear relationship between flow and density, the two modes being free flow and congestion. An approximate quantile regression method is used for the estimation of the congested regime due to this mode's high susceptibility to various external factors.

The fault detection algorithm has been developed to facilitate the automatic model building procedure. The macroscopic cell transmission model, which is the model assumed in this study, requires consistent observations along the modeled freeway section for an accurate calibration to be possible. When detectors are down or missing, the model has to be modified to a less accurate representation to conform with a configuration where a sensor is assigned to each cell of the model. In addition, on most California freeways the ramp flows in and out of the mainline are not observed. Since the estimation of these unknown inputs to the system also hinge on healthy mainline data, the identification of faulty mainline sensors becomes crucial to the automatic model building process. The model-based fault detection algorithm presented herein analyzes the parity between simulated and measured state, along with estimated unknown input profiles. Subsequently, it makes use of a look-up table logic and a threshold scheme to flag erroneous detectors along the freeway mainline.

Finally, the fault handling algorithm that accompanies the fault detection aims to revert the model to its original configuration after the aforementioned modifications are made to the model due to missing or bad sensors. Using a relaxed model-constrained linear optimization, this algorithm seeks to fill in the gaps in the observations along the freeway that are a result of poor detection. This method provides a reconstruction of the unobserved state that conforms with the rest of the measurements and does not produce a state estimate in a control theoretical sense.