Results 251 to 260 of about 959,000 (302)

Combined/Composite Model Reference Adaptive Control

IEEE Transactions on Automatic Control, 2009
This technical note introduces a provably stable state-feedback design modification for combined/composite adaptive control of multi-input multi-output dynamical systems with matched uncertainties. The proposed design methodology is applied to control longitudinal dynamics of an aerial vehicle.
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Adaptive Cruise Control: A Model Reference Adaptive Control Approach

2020 24th International Conference on System Theory, Control and Computing (ICSTCC), 2020
This paper presents a Model Reference Adaptive Control (MRAC) based framework for vehicle adaptive cruise control systems. The linear longitudinal dynamic model is created with uncertainties in the state and input matrix. The controller structure aims to handle these uncertainties and also maintains the desired distance with the lead vehicle.
Adamu Abdullahi, Sirin Akkaya
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Fuzzy model-reference adaptive control

Proceedings of 1994 33rd IEEE Conference on Decision and Control, 1995
This paper proposes a fuzzy model-reference adaptive control (fuzzy-MRAC) to deal with controlling a plant with unknown parameters which are dependent on known variables. The proposed method uses the fuzzy basis function expansion (FBFE) to represent the unknown parameters and change the identification problem from identifying the original unknown ...
Tang-Kai Yin, C. S. George Lee
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Fuzzy model reference adaptive control

IEEE Transactions on Fuzzy Systems, 2002
This paper investigates a fuzzy model reference adaptive controller (FMRAC) for continuous-time multiple-input-multiple-output (MIMO) nonlinear systems. The proposed adaptive scheme uses a Takagi-Seguno (TS) fuzzy adaptive system, which allows for the inclusion of a priori information in terms of qualitative knowledge about the plant operating points ...
N. Golea, A. Golea, K. Benmahammed
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Genetic model reference adaptive control

Proceedings of 1994 9th IEEE International Symposium on Intelligent Control, 2002
A genetic algorithm (GA) uses the principles of evolution, natural selection, and genetics to offer a method for parallel search of complex spaces. Motivated by the past GA's use in off-line design of controllers we introduce a new approach to adaptive control, which we call "genetic model reference adaptive control" (GMRAC), where a GA manipulates a ...
L.M.L. Porter, K.M. Passino
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Stochastic model reference adaptive controllers

1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes, 1980
The behaviour of Model Reference Adaptive Controllers (MRAC) shemes designed in the deterministre framework is analysed and studied in the stochastic environment. The case of discret single input - single output (SISO) minimum phase systems is considered.
L. Dugard, I. Landau
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Model‐reference adaptive control system

AIChE Journal, 1967
AbstractAn adaptive process control scheme—which uses a differential equation model, requires no differentiations in the adaptive circuitry, and no identification of the varying process parameter—was analyzed mathematically and studied on an analog computer.
Robert M. Casciano, H. Kenneth Staffin
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Model-Reference Adaptive Control System Design Technique

Journal of Dynamic Systems, Measurement, and Control, 1973
This paper considers the model-reference adaptive control problem which has received considerable attention in the literature in the last few years. An adaptive control scheme is proposed which has terms in the Liapunov function used in the design procedure which are not included in previously proposed schemes.
Sutherlin, D. W., Boland, J. S. III
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MODEL REFERENCE ADAPTIVE CONTROL OF MECHANICAL MANIPULATORS

IFAC Proceedings Volumes, 1983
Abstract The dynamic equations of mechanical manipulators are highly nonlinear and complex. Furthermore, the inertia characteristics of the manipulator depend on the payload which is often unknown. Because of these reasons, there is an increasing interest in adaptive control of mechanical manipulators.
M. Tomizuka, R. Horowitz
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Model Reference Adaptive Control

2000
Control algorithms discussed in Chapters 2–5 are generally based on the discrete-time model and in most cases the input and output models are used to formulate the algorithm. In this chapter, an alternative approach will be described using the state space model.
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