scholarly journals A Modified Model Reference Adaptive Controller (M-MRAC) Using an Updated MIT-Rule for the Altitude of a UAV

Electronics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1104
Author(s):  
Julian Rothe ◽  
Jasper Zevering ◽  
Michael Strohmeier ◽  
Sergio Montenegro
Keyword(s):  
Adaptive Controller ◽  
Massachusetts Institute Of Technology ◽  
Model Reference ◽  
Adaptive Controllers ◽  
Modified Model ◽  
Altitude Control ◽  
Institute Of Technology ◽  
The Stability ◽  
The University ◽  
Model Reference Adaptive

Unmanned Aerial Vehicles (UAVs) are playing an increasingly important role in a wide variety of areas and the range of applications increases daily, which can also be seen in the research of the topic. At the University of Wuerzburg drones are to be used in a project, where the aim is to catch possibly dangerous UAVs in mid air using a net, carried by two drones. This very special scenario poses new problems to the control of the drones, so that traditionally used Proportional-Integral-Differential (PID) controllers are no longer sufficient. Therefore a model-based adaption mechanism was chosen to be used to control the altitude of the drones. Though adaption based controllers have been used in the field of drone research before, the existing algorithms had to be modified to work with the special conditions of the altitude control of UAVs. The design and implementation of the modified Model Reference Adaptive Controllers (MRACs) with an updated Massachusetts Institute of Technology (MIT)-rule will be presented in this work. The behavior of the drones with and without the adaption as well as the changes to the original MRAC are then compared in simulation as well as on a real system and show very promising results in further improving the stability of the altitude control of the drones.

A Parametric Study of the Modified Model Reference Adaptive Control Scheme

1998 ◽  
Vol 120 (3) ◽  
pp. 814-821
Author(s):  
H. M. Sardar ◽  
M. Ahmadian
Keyword(s):  
Closed Loop ◽  
Simulation Analysis ◽  
Adaptive Controller ◽  
Loop System ◽  
Closed Loop System ◽  
Model Reference ◽  
Modified Model ◽  
Control Scheme ◽  
Inertial Nonlinearities ◽  
Model Reference Adaptive

The validity of the claim by many studies that the damping and stiffness forces can be ignored when designing a model reference adaptive controller, is examined. For a simple plant, the sensitivity of the closed loop system to the inertial, damping, and stiffness nonlinearities are investigated, through a simulation analysis. It is shown that the closed loop system is sensitive to the changes in the inertial nonlinearities, and relatively insensitive to variations in the damping and stiffness forces. This supports the assumption made in many previous studies.

scholarly journals Improved Transient Performance of a Fuzzy Modified Model Reference Adaptive Controller for an Interacting Coupled Tank System Using Real-Coded Genetic Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Asan Mohideen Khansadurai ◽  
Valarmathi Krishnasamy ◽  
Radhakrishnan Thota Karunakaran
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic Controller ◽  
Adaptive Controller ◽  
Transient Performance ◽  
Model Reference ◽  
Modified Model ◽  
Real Coded Genetic Algorithm ◽  
Tank System ◽  
Model Reference Adaptive ◽  
The Membership Function

The main objective of the paper is to design a model reference adaptive controller (MRAC) with improved transient performance. A modification to the standard direct MRAC called fuzzy modified MRAC (FMRAC) is used in the paper. The FMRAC uses a proportional control based Mamdani-type fuzzy logic controller (MFLC) to improve the transient performance of a direct MRAC. The paper proposes the application of real-coded genetic algorithm (RGA) to tune the membership function parameters of the proposed FMRAC offline so that the transient performance of the FMRAC is improved further. In this study, a GA based modified MRAC (GAMMRAC), an FMRAC, and a GA based FMRAC (GAFMRAC) are designed for a coupled tank setup in a hybrid tank process and their transient performances are compared. The results show that the proposed GAFMRAC gives a better transient performance than the GAMMRAC or the FMRAC. It is concluded that the proposed controller can be used to obtain very good transient performance for the control of nonlinear processes.

A Model Reference Adaptive Control Framework for Uncertain Dynamical Systems With High-Order Actuator Dynamics and Unknown Actuator Outputs

2017 ◽  
Author(s):  
Benjamin C. Gruenwald ◽  
K. Merve Dogan ◽  
Tansel Yucelen ◽  
Jonathan A. Muse
Keyword(s):  
Adaptive Control ◽  
Linear Matrix Inequalities ◽  
High Order ◽  
Linear Matrix ◽  
Matrix Inequalities ◽  
Model Reference ◽  
Adaptive Controllers ◽  
Actuator Dynamics ◽  
The Stability ◽  
Model Reference Adaptive

As it is well-known, the stability properties of model reference adaptive controllers can be seriously affected by the presence of actuator dynamics. To this end, the authors recently proposed linear matrix inequalities-based hedging approaches to compute the stability limits of model reference adaptive controllers in the presence of a) scalar actuator dynamics with known outputs, b) scalar actuator dynamics with unknown outputs, and c) high-order (linear time-invariant) actuator dynamics with known outputs. The common denominator of these approaches is that they have the capability to rigorously characterize the fundamental stability interplay between the system uncertainties and the necessary bandwidth of the actuator dynamics. Building on these results, the purpose of this paper is to extend the recent work by the authors to the general case, where there exist high-order actuator dynamics with unknown outputs in the closed-loop model reference adaptive control systems. For this purpose, we propose an observer architecture to estimate the unknown output of the actuator dynamics and use the estimated actuator output to design the linear matrix inequalities-based hedging framework. Remarkably, with the proposed observer, the sufficient stability condition in this case of unknown actuator outputs is identical to the case with known actuator outputs that was established in the prior work by the authors. Therefore, a control designer can utilize the proposed framework for practical applications when the output of the actuator dynamics is not measurable, and hence, unknown (e.g., in hypersonic vehicle applications). An illustrative numerical example complements the proposed theoretical contribution.

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