2008 - Fellow of the Royal Academy of Engineering (UK)
His main research concerns Iterative learning control, Control theory, Mathematical optimization, Linear system and Stability. The study incorporates disciplines such as Optimal control, Iterative method, Rate of convergence, Monotonic function and Robustness in addition to Iterative learning control. The various areas that David H. Owens examines in his Optimal control study include Norm, Discrete time and continuous time and Observability.
His Control theory research includes elements of Feature and Applied mathematics. His Mathematical optimization study integrates concerns from other disciplines, such as Weighting, Intelligent control and Newton's method. His Linear system research is multidisciplinary, relying on both Basis and Of the form.
David H. Owens spends much of his time researching Control theory, Iterative learning control, Mathematical optimization, Linear system and Control engineering. All of his Control theory and Multivariable calculus, Adaptive control, Robustness, Repetitive control and Control system investigations are sub-components of the entire Control theory study. As a member of one scientific family, David H. Owens mostly works in the field of Iterative learning control, focusing on Optimal control and, on occasion, Discrete time and continuous time.
David H. Owens combines subjects such as Model predictive control and Nonlinear system with his study of Mathematical optimization. Basis is closely connected to Stability in his research, which is encompassed under the umbrella topic of Linear system. His study in the fields of Control theory under the domain of Control engineering overlaps with other disciplines such as Process control.
His primary areas of study are Iterative learning control, Mathematical optimization, Norm, Control theory and Monotonic function. His Iterative learning control research integrates issues from Algorithm, Algorithm design, Optimal control and Robustness. His biological study spans a wide range of topics, including Weighting, Linear system and Fixed-point iteration.
His Norm study combines topics from a wide range of disciplines, such as Embedding and Multivariable calculus. His Control theory study frequently draws connections between adjacent fields such as Motion control. The Monotonic function study combines topics in areas such as Dykstra's projection algorithm, Hilbert space, Tracking error, Applied mathematics and Rate of convergence.
Iterative learning control, Mathematical optimization, Repetitive control, Norm and Optimal control are his primary areas of study. Control theory and Artificial intelligence are the subject areas of his Iterative learning control study. His primary area of study in Control theory is in the field of Robustness.
David H. Owens studied Mathematical optimization and Weighting that intersect with Linear system, Projection and Iterative method. As part of the same scientific family, David H. Owens usually focuses on Repetitive control, concentrating on Intelligent control and intersecting with Projection method, Remainder, Rehabilitation robotics and Robotics. His biological study spans a wide range of topics, including Algorithm, Algebraic number and Multivariable calculus.
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Computer-Aided Control System Design
H. H. Rosenbrock;D. H. Owens.
Control Systems Theory and Applications for Linear Repetitive Processes
Eric Rogers;Krzysztof Galkowski;D. H. Owens.
Stability Analysis for Linear Repetitive Processes
E. T. A. Rogers;D. H. Owens.
Iterative learning control for discrete-time systems with exponential rate of convergence
N. Amann;D.H. Owens;E. Rogers.
IEE Proceedings - Control Theory and Applications (1996)
Iterative learning control using optimal feedback and feedforward actions
Notker Amann;David H. Owens;Eric Rogers.
International Journal of Control (1996)
Predictive optimal iterative learning control
Notker Amann;David H. Owens;Eric Rogers.
International Journal of Control (1998)
Iterative Learning Control: An Optimization Paradigm
David H. Owens.
Analysis of Linear Iterative Learning Control Schemes -A 2D Systems/Repetitive Processes Approach
D. H. Owens;N. Amann;E. Rogers;M. French.
Multidimensional Systems and Signal Processing (2000)
Parameter optimization in iterative learning control
D. H. Owens;K. Feng.
International Journal of Control (2003)
LMIs - a fundamental tool in analysis and controller design for discrete linear repetitive processes
K. Galkowski;E. Rogers;S. Xu;J. Lam.
IEEE Transactions on Circuits and Systems I-regular Papers (2002)
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