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Receding horizon control of air operation resource allocationRuschmann, Matthew Charles. January 2006 (has links)
Thesis (M.S.)--State University of New York at Binghamton, Dept. of Electrical & Computer Engineering, 2006. / Includes bibliographical references.
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Nonlinear model predictive controlSriniwas, Ganti Ravi 08 1900 (has links)
No description available.
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ℓasso-MPC - predictive control with ℓ₁-regularised least squaresGallieri, Marco January 2014 (has links)
No description available.
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Predictive iterative learning controlTownley, Tracy Yvette January 2002 (has links)
No description available.
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Model Predictive Control in Flight Control Design : Stability and Reference TrackingSimon, Daniel January 2014 (has links)
Aircraft are dynamic systems that naturally contain a variety of constraints and nonlinearities such as, e.g., maximum permissible load factor, angle of attack and control surface deflections. Taking these limitations into account in the design of control systems are becoming increasingly important as the performance and complexity of the controlled systems is constantly increasing. It is especially important in the design of control systems for fighter aircraft. These require maximum control performance in order to have the upper hand in a dogfight or when they have to outmaneuver an enemy missile. Therefore pilots often maneuver the aircraft very close to the limit of what it is capable of, and an automatic system (called flight envelope protection system) against violating the restrictions is a necessity. In other application areas, nonlinear optimal control methods have been successfully used to solve this but in the aeronautical industry, these methods have not yet been established. One of the more popular methods that are well suited to handle constraints is Model Predictive Control (MPC) and it is used extensively in areas such as the process industry and the refinery industry. Model predictive control means in practice that the control system iteratively solves an advanced optimization problem based on a prediction of the aircraft's future movements in order to calculate the optimal control signal. The aircraft's operating limitations will then be constraints in the optimization problem. In this thesis, we explore model predictive control and derive two fast, low complexity algorithms, one for guaranteed stability and feasibility of nonlinear systems and one for reference tracking for linear systems. In reference tracking model predictive control for linear systems we build on the dual mode formulation of MPC and our goal is to make minimal changes to this framework, in order to develop a reference tracking algorithm with guaranteed stability and low complexity suitable for implementation in real time safety critical systems. To reduce the computational burden of nonlinear model predictive control several methods to approximate the nonlinear constraints have been proposed in the literature, many working in an ad hoc fashion, resulting in conservatism, or worse, inability to guarantee recursive feasibility. Also several methods work in an iterative manner which can be quit time consuming making them inappropriate for fast real time applications. In this thesis we propose a method to handle the nonlinear constraints, using a set of dynamically generated local inner polytopic approximations. The main benefits of the proposed method is that while computationally cheap it still can guarantee recursive feasibility and convergence. / <p>The series name "<em>Linköping studies in science and technology. Licentiate Thesis</em>" is incorrect. The correct series name is "<em>Linköping studies in science and technology. Thesis</em>".</p>
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Reduced-order model identification for long-range prediction /Shrinivas, Srikrishna. January 2003 (has links)
Thesis (M.S.)--University of Missouri-Columbia, 2003. / Typescript. Includes bibliographical references (leaves 110-111). Also available on the Internet.
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Reduced-order model identification for long-range predictionShrinivas, Srikrishna. January 2003 (has links)
Thesis (M.S.)--University of Missouri-Columbia, 2003. / Typescript. Includes bibliographical references (leaves 110-111). Also available on the Internet.
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Clearing bubble blockages in micro channels using a model predictive controller /Patton, Chris. January 1900 (has links)
Thesis (M.S.)--Oregon State University, 2010. / Printout. Includes bibliographical references (leaves 40-41). Also available on the World Wide Web.
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Critical Zone Calculation For Automated Vehicles Using Model Predictive ControlGlasky, Enimini Theresa 05 1900 (has links)
Indiana University-Purdue University Indianapolis (IUPUI) / This thesis studies critical zones of automated vehicles. The goal is for the automated
vehicle to complete a car-following or lane change maneuver without collision. For instance,
the automated vehicle should be able to indicate its interest in changing lanes and plan
how the maneuver will occur by using model predictive control theory, in addition to the
autonomous vehicle toolbox in Matlab. A test bench (that includes a scenario creator, motion
logic and planner, sensors, and radars) is created and used to calculate the parameters of a
critical zone. After a trajectory has been planned, the automated vehicle then attempts the
car following or lane change while constantly ensuring its safety to continue on this path. If
at any point, the lead vehicle brakes or a trailing vehicle accelerates, the automated vehicle
makes the decision to either brake, accelerate, or abandon the lane change.
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Model Predictive Control of Magnetic Bearing SystemHuang, Yang, S3110949@student.rmit.edu.au January 2007 (has links)
Magnetic Bearing Systems have been receiving a great deal of research attention for the past decades. Its inherent nonlinearity and open-loop instability are challenges for controller design. This thesis investigates and designs model predictive control strategy for an experimental Active Magnetic Bearing (AMB) laboratory system. A host-target development environment of real-time control system with hardware in the loop (HIL) is implemented. In this thesis, both continuous and discrete time model predictive controllers are studied. In the first stage, local MPC controllers are applied to control the AMB system; and in the second stage, concept of supervisory controller design is then investigated and implemented. Contributions of the thesis can be summarized as follows; 1. A Discrete time Model Predictive Controller has been developed and applied to the active magnetic bearing system. 2. A Continuous time Model Predictive Controller has been developed and applied to the active magnetic bearing system. 3. A frequency domain identification method using FSF has been applied to pursue model identification with respect to local MPC and magnetic bearing system. 4. A supervisory control strategy has been applied to pursue a two stages model predictive control of active magnetic bearing system.
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