664 research outputs found
Cumulative reports and publications through December 31, 1988
This document contains a complete list of ICASE Reports. Since ICASE Reports are intended to be preprints of articles that will appear in journals or conference proceedings, the published reference is included when it is available
Cumulative reports and publications through December 31, 1990
This document contains a complete list of ICASE reports. Since ICASE reports are intended to be preprints of articles that will appear in journals or conference proceedings, the published reference is included when it is available
Robust stability and stabilization of discrete singular systems: An equivalent characterization
This note deals with the problems of robust stability and stabilization for uncertain discrete-time singular systems. The parameter uncertainties are assumed to be time-invariant and norm-bounded appearing in both the state and input matrices. A new necessary and sufficient condition for a discrete-time singular system to be regular, causal and stable is proposed in terms of a strict linear matrix inequality (LMI). Based on this, the concepts of generalized quadratic stability and generalized quadratic stabilization for uncertain discrete-time singular systems are introduced. Necessary and sufficient conditions for generalized quadratic stability and generalized quadratic stabilization are obtained in terms of a strict LMI and a set of matrix inequalities, respectively. With these conditions, the problems of robust stability and robust stabilization are solved. An explicit expression of a desired state feedback controller is also given, which involves no matrix decomposition. Finally, an illustrative example is provided to demonstrate the applicability of the proposed approach.published_or_final_versio
New optimization methods in predictive control
This thesis is mainly concerned with the efficient solution of a linear discrete-time
finite horizon optimal control problem (FHOCP) with quadratic cost and linear constraints
on the states and inputs. In predictive control, such a FHOCP needs to be
solved online at each sampling instant. In order to solve such a FHOCP, it is necessary
to solve a quadratic programming (QP) problem. Interior point methods (IPMs) have
proven to be an efficient way of solving quadratic programming problems. A linear system
of equations needs to be solved in each iteration of an IPM. The ill-conditioning
of this linear system in the later iterations of the IPM prevents the use of an iterative
method in solving the linear system due to a very slow rate of convergence; in some cases
the solution never reaches the desired accuracy. A new well-conditioned IPM, which increases
the rate of convergence of the iterative method is proposed. The computational
advantage is obtained by the use of an inexact Newton method along with the use of
novel preconditioners.
A new warm-start strategy is also presented to solve a QP with an interior-point
method whose data is slightly perturbed from the previous QP. The effectiveness of
this warm-start strategy is demonstrated on a number of available online benchmark
problems. Numerical results indicate that the proposed technique depends upon the
size of perturbation and it leads to a reduction of 30-74% in floating point operations
compared to a cold-start interior point method.
Following the main theme of this thesis, which is to improve the computational efficiency
of an algorithm, an efficient algorithm for solving the coupled Sylvester equation
that arises in converting a system of linear differential-algebraic equations (DAEs) to
ordinary differential equations is also presented. A significant computational advantage
is obtained by exploiting the structure of the involved matrices. The proposed algorithm
removes the need to solve a standard Sylvester equation or to invert a matrix. The
improved performance of this new method over existing techniques is demonstrated by
comparing the number of floating-point operations and via numerical examples
Custom optimization algorithms for efficient hardware implementation
The focus is on real-time optimal decision making with application in advanced control
systems. These computationally intensive schemes, which involve the repeated solution of
(convex) optimization problems within a sampling interval, require more efficient computational
methods than currently available for extending their application to highly dynamical
systems and setups with resource-constrained embedded computing platforms.
A range of techniques are proposed to exploit synergies between digital hardware, numerical
analysis and algorithm design. These techniques build on top of parameterisable
hardware code generation tools that generate VHDL code describing custom computing
architectures for interior-point methods and a range of first-order constrained optimization
methods. Since memory limitations are often important in embedded implementations we
develop a custom storage scheme for KKT matrices arising in interior-point methods for
control, which reduces memory requirements significantly and prevents I/O bandwidth
limitations from affecting the performance in our implementations. To take advantage of
the trend towards parallel computing architectures and to exploit the special characteristics
of our custom architectures we propose several high-level parallel optimal control
schemes that can reduce computation time. A novel optimization formulation was devised
for reducing the computational effort in solving certain problems independent of the computing
platform used. In order to be able to solve optimization problems in fixed-point
arithmetic, which is significantly more resource-efficient than floating-point, tailored linear
algebra algorithms were developed for solving the linear systems that form the computational
bottleneck in many optimization methods. These methods come with guarantees
for reliable operation. We also provide finite-precision error analysis for fixed-point implementations
of first-order methods that can be used to minimize the use of resources while
meeting accuracy specifications. The suggested techniques are demonstrated on several
practical examples, including a hardware-in-the-loop setup for optimization-based control
of a large airliner.Open Acces
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
Cumulative reports and publications
A complete list of Institute for Computer Applications in Science and Engineering (ICASE) reports are listed. Since ICASE reports are intended to be preprints of articles that will appear in journals or conference proceedings, the published reference is included when it is available. The major categories of the current ICASE research program are: applied and numerical mathematics, including numerical analysis and algorithm development; theoretical and computational research in fluid mechanics in selected areas of interest to LaRC, including acoustics and combustion; experimental research in transition and turbulence and aerodynamics involving LaRC facilities and scientists; and computer science
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