His scientific interests lie mostly in Control theory, Control engineering, Linear system, Iterative learning control and System identification. His research related to Repetitive control, Control system, Stability, Tracking error and State observer might be considered part of Control theory. His Control engineering research is multidisciplinary, incorporating perspectives in Robot, Robotics, Actuator, Artificial intelligence and Automatic control.
His studies in Linear system integrate themes in fields like Orthonormal basis, Realization, Data matrix and Impulse response. His research brings together the fields of Adaptive control and Iterative learning control. His work on Eigensystem realization algorithm as part of general System identification research is frequently linked to Algorithm, Observer, Numerical analysis and Dimension, thereby connecting diverse disciplines of science.
Richard W. Longman mainly focuses on Control theory, Control engineering, Iterative learning control, Repetitive control and System identification. His research in Control system, Optimal control, Tracking error, Control theory and Adaptive control are components of Control theory. His Control engineering research is multidisciplinary, incorporating elements of Automatic control, Robot, Actuator, Artificial intelligence and Robot control.
The study incorporates disciplines such as Stability, Frequency domain and Frequency response in addition to Repetitive control. His work on Eigensystem realization algorithm as part of general System identification study is frequently linked to Markov chain, Algorithm, Kalman filter and Observer, therefore connecting diverse disciplines of science. The concepts of his Observer study are interwoven with issues in State-space representation and Linear system.
Richard W. Longman spends much of his time researching Control theory, Repetitive control, Iterative learning control, Kalman filter and Control engineering. His Control theory study incorporates themes from Vibration and Bilinear interpolation. His Repetitive control research incorporates elements of Disturbance, Mode, Stability, Order and Robustness.
His Iterative learning control research incorporates themes from Control system and Tracking error, Control theory. His Fast Kalman filter and Extended Kalman filter study, which is part of a larger body of work in Kalman filter, is frequently linked to System identification and Alpha beta filter, bridging the gap between disciplines. His research in Control engineering intersects with topics in Actuator, Trajectory and Motion control.
Richard W. Longman mostly deals with Control theory, Kalman filter, System identification, Bilinear interpolation and Repetitive control. His Control theory research includes themes of Basis function and Period. His study in the field of Extended Kalman filter, Fast Kalman filter, Invariant extended Kalman filter and Ensemble Kalman filter is also linked to topics like Alpha beta filter.
His System identification research covers fields of interest such as Bilinear map and Control engineering. His Bilinear interpolation study combines topics in areas such as Linear system, Bilinear form, State, State space and Noise. His work carried out in the field of Repetitive control brings together such families of science as Robustification, Iterative learning control, Tracking error, Adaptive control and Reaction wheel.
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Iterative learning control and repetitive control for engineering practice
Richard W. Longman.
International Journal of Control (2000)
Identification of observer/Kalman filter Markov parameters - Theory and experiments
Jer-Nan Juang;Minh Phan;Lucas G. Horta;Richard W. Longman.
Journal of Guidance Control and Dynamics (1993)
Satellite-mounted robot manipulators- new kinematics and reaction moment compensation
R. W. Longman;R. E. Lindberg;M. F. Zedd.
The International Journal of Robotics Research (1987)
Identification of linear structural systems using earthquake‐induced vibration data
H. Luş;R. Betti;R. W. Longman.
Earthquake Engineering & Structural Dynamics (1999)
Active Control Technology for Large Space Structures
D. C. Hyland;J. L. Junkins;R. W. Longman.
Journal of Guidance Control and Dynamics (1993)
On-line identification of non-linear hysteretic structural systems using a variable trace approach
Jeng-Wen Lin;Raimondo Betti;Andrew W. Smyth;Richard W. Longman.
Earthquake Engineering & Structural Dynamics (2001)
Simple learning control made practical by zero-phase filtering: applications to robotics
H. Elci;R.W. Longman;M.Q. Phan;Jer-Nan Juang.
IEEE Transactions on Circuits and Systems I-regular Papers (2002)
A mathematical theory of learning control for linear discrete multivariable systems
Minh Phan;Richard W. Longman.
AIAA/AAS Astrodynamics Conference (1988)
A method for improving the dynamic accuracy of a robot performing a repetitive task
Richard H. Middleton;Graham C. Goodwin;Richard W. Longman.
The International Journal of Robotics Research (1989)
Linear system identification via an asymptotically stable observer
M. Phan;L. G. Horta;J. N. Juang;R. W. Longman.
Journal of Optimization Theory and Applications (1993)
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