Robust Loopshaping for Process Control

Strong trends in chemical engineering and plant operation have made the control of processes increasingly difficult and have driven the process industry's demand for improved control techniques. Improved control leads to savings in resources, smaller downtimes, improved safety, and reduced pollution. Though the need for improved process control is clear, advanced control methodologies have had only limited acceptance and application in industrial practice. The reason for this gap between control theory and practice is that existing control methodologies do not adequately address all of the following control system requirements and problems associated with control design: * The controller must be insensitive to plant/model mismatch, and perform well under unmeasured or poorly modeled disturbances. * The controlled system must perform well under state or actuator constraints. * The controlled system must be safe, reliable, and easy to maintain. * Controllers are commonly required to be decentralized. * Actuators and sensors must be selected before the controller can be designed. * Inputs and outputs must be paired before the design of a decentralized controller. A framework is presented to address these control requirements/problems in a general, unified manner. The approach will be demonstrated on adhesive coating processes and distillation columns

Robust Adaptive Control in H(infinity).

This dissertation addresses the problem of unifying identification and control in the paradigm of {\cal H}\sb\infty to achieve robust adaptive control. To achieve robust adaptive control, we employ the same approach used for identification in {\cal H}\sb\infty and robust control in {\cal H}\sb\infty. In the modeling part, we aim not only to identify the nominal plant, but also to quantify the modeling error in {\cal H}\sb\infty norm. The linear algorithm based on least-squares is used, and the upper bounds for the corresponding modeling error are derived. In the control part, we aim to achieve the performance specification in frequency domain using innovative model reference control. New algorithms are derived that minimize an {\cal H}\sb\infty index function associated with the deviation between the performance of the feedback system to be designed, and that of the reference model. The results for the modeling and control part are then combined and applied to adaptive control. It is shown that with mild assumption on persistent excitation, the least squares algorithm in frequency domain is equivalent to the recursive least squares algorithm in time domain. Moreover, finite horizon {\cal H}\sb\infty is employed to design feedback controller recursively using the identified model that is time varying in nature. The robust stability of the adaptive feedback system is then established

A method for robust control of systems with parametric uncertainty motivated by a benchmark example

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1991.Title as it appears in the June, 1991 M.I.T. Graduate List: Robust control of systems with parametric uncertainty.Includes bibliographical references (p. 156-158).by Carl S. Resnik.M.S

Systematic procedure to meet specific input/output constraints in the l₁-optimal control problem design

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995; and, (M.S.)--Massachusetts Institute of Technology, Sloan School of Management, 1996.Includes bibliographical references (leaves 86-87).by Marcos Escobar Fernández de la Vega.M.S

Advanced Robust Control Design For High Speed Tilting Trains

Tilting is a worldwide accepted technology concept in railway transportation. The particular benefit from tilting trains use is reduction in journey times due to speed increase on track corners (while maintaining acceptable passenger comfort), a point that facilitates improved customer service. An additional benefit is cost effectiveness due to the train running on existing rail tracks. Many countries opted to using tilting trains as means of fast public transportation (UK, USA, Canada, Sweden, Norway, Switzerland, Germany, Japan). The industrial norm of tilting high speed trains is that of precedence tilt whereby preview tilt enabling signals are used to provide the required information to the vehicles (it can also use a combination of track database information or GPS but the concept is the same). Precedence tilt tends to be complex (mainly due to the signal interconnections between vehicles and the advanced signal processing required for monitoring). Research studies of earlier than precedence schemes,i.e. the so-called nulling-type schemes whereby local-per-vehicle signals are used to provide tilt (a disturbance rejection-scheme although tends to suffer from inherent delays in the control feedback), are still an important research aim due to the simple nature and most importantly due to the more straightforward fault detection compared to precedence. Use of nulling-type tilt has been supported by recent studies in this context. The research presented in this thesis highly contributes to simplified single-inputsingle-output robust tilt control using the simplest rail vehicle tilt structure, i.e. an Active Anti-Roll Bar. Proposed are both robust conventional (integer-type) control approaches and non-conventional (non-integer) schemes with a rigorous investigation of the difficult to achieve deterministic/stochastic tilt trade-off. Optimization has been used extensively for the designs. A by-product of the work is the insight provided into the relevant tilting train model Non Minimum Phase characteristics and its link to uncertainty for control design. Work has been undertaken using Matlab, including proper assessment of tilt ride quality considerations

Model-based and data-based frequency domain design of fixed structure robust controller: a polynomial optimization approach

L'abstract è presente nell'allegato / the abstract is in the attachmen

Performance-driven control of nano-motion systems

The performance of high-precision mechatronic systems is subject to ever increasing demands regarding speed and accuracy. To meet these demands, new actuator drivers, sensor signal processing and control algorithms have to be derived. The state-of-the-art scientific developments in these research directions can significantly improve the performance of high-precision systems. However, translation of the scientific developments to usable technology is often non-trivial. To improve the performance of high-precision systems and to bridge the gap between science and technology, a performance-driven control approach has been developed. First, the main performance limiting factor (PLF) is identified. Then, a model-based compensation method is developed for the identified PLF. Experimental validation shows the performance improvement and reveals the next PLF to which the same procedure is applied. The compensation method can relate to the actuator driver, the sensor system or the control algorithm. In this thesis, the focus is on nano-motion systems that are driven by piezo actuators and/or use encoder sensors. Nano-motion systems are defined as the class of systems that require velocities ranging from nanometers per second to millimeters per second with a (sub)nanometer resolution. The main PLFs of such systems are the actuator driver, hysteresis, stick-slip effects, repetitive disturbances, coupling between degrees-of-freedom (DOFs), geometric nonlinearities and quantization errors. The developed approach is applied to three illustrative experimental cases that exhibit the above mentioned PLFs. The cases include a nano-motion stage driven by a walking piezo actuator, a metrological AFM and an encoder system. The contributions of this thesis relate to modeling, actuation driver development, control synthesis and encoder sensor signal processing. In particular, dynamic models are derived of the bimorph piezo legs of the walking piezo actuator and of the nano-motion stage with the walking piezo actuator containing the switching actuation principle, stick-slip effects and contact dynamics. Subsequently, a model-based optimization is performed to obtain optimal drive waveforms for a constant stage velocity. Both the walking piezo actuator and the AFM case exhibit repetitive disturbances with a non-constant period-time, for which dedicated repetitive control methods are developed. Furthermore, control algorithms have been developed to cope with the present coupling between and hysteresis in the different axes of the AFM. Finally, sensor signal processing algorithms have been developed to cope with the quantization effects and encoder imperfections in optical incremental encoders. The application of the performance-driven control approach to the different cases shows that the different identified PLFs can be successfully modeled and compensated for. The experiments show that the performance-driven control approach can largely improve the performance of nano-motion systems with piezo actuators and/or encoder sensors

Magnetic Actuators and Suspension for Space Vibration Control

The research on microgravity vibration isolation performed at the University of Virginia is summarized. This research on microgravity vibration isolation was focused in three areas: (1) the development of new actuators for use in microgravity isolation; (2) the design of controllers for multiple-degree-of-freedom active isolation; and (3) the construction of a single-degree-of-freedom test rig with umbilicals. Described are the design and testing of a large stroke linear actuator; the conceptual design and analysis of a redundant coarse-fine six-degree-of-freedom actuator; an investigation of the control issues of active microgravity isolation; a methodology for the design of multiple-degree-of-freedom isolation control systems using modern control theory; and the design and testing of a single-degree-of-freedom test rig with umbilicals

Nonlinear and distributed sensory estimation

Methods to improve performance of sensors with regard to sensor nonlinearity, sensor noise and sensor bandwidths are investigated and new algorithms are developed. The necessity of the proposed research has evolved from the ever-increasing need for greater precision and improved reliability in sensor measurements. After describing the current state of the art of sensor related issues like nonlinearity and bandwidth, research goals are set to create a new trend on the usage of sensors. We begin the investigation with a detailed distortion analysis of nonlinear sensors. A need for efficient distortion compensation procedures is further justified by showing how a slight deviation from the linearity assumption leads to a very severe distortion in time and in frequency domains. It is argued that with a suitable distortion compensation technique the danger of having an infinite bandwidth nonlinear sensory operation, which is dictated by nonlinear distortion, can be avoided. Several distortion compensation techniques are developed and their performance is validated by simulation and experimental results. Like any other model-based technique, modeling errors or model uncertainty affects performance of the proposed scheme, this leads to the innovation of robust signal reconstruction. A treatment for this problem is given and a novel technique, which uses a nominal model instead of an accurate model and produces the results that are robust to model uncertainty, is developed. The means to attain a high operating bandwidth are developed by utilizing several low bandwidth pass-band sensors. It is pointed out that instead of using a single sensor to measure a high bandwidth signal, there are many advantages of using an array of several pass-band sensors. Having shown that employment of sensor arrays is an economic incentive and practical, several multi-sensor fusion schemes are developed to facilitate their implementation. Another aspect of this dissertation is to develop means to deal with outliers in sensor measurements. As fault sensor data detection is an essential element of multi-sensor network implementation, which is used to improve system reliability and robustness, several sensor scheduling configurations are derived to identify and to remove outliers