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

Abstract

<jats:p>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.</jats:p>

Novel Asynchronous Activation of the Bio-Inspired Adaptive Tuning in the Speed Controller: Study Case in DC Motors

“…This case is considered ideal and does not include noise, disturbances or changes in the speed profile. • Test Case 2 (TC2): Different from TC1, this case considers a more realistic scenario where random Gaussian noise with mean µ = 0 and standard deviation σ = 0.1 is added to speed signal x 1 , the torque load τ l varies according to (31), and the speed profile x1 alternates as described in (32). The same nominal DC motor parameters, sampling interval and initial condition in TC1 are utilized in this case.…”

“…The most promising controller tuning approach based on the classification in [19,28] is the adaptive tuning method, since it can effectively handle the uncertainties in practical applications [29,30]. In this approach, the controller gains VOLUME 4, 2016 are continuously updated at each fixed time period (online control parameter update), typically by using predefined update rules [31] or computational intelligence techniques such as neural networks [32], fuzzy logic [33], and optimization methods [34].…”

Trajectory Tracking with Adaptive Robust Control for Quadrotor

“…These conditions are when the quadrotor deals with system uncertainties or unknown parameters; the control objective is to obtain a uniform update law for the controller parameters based on the Model Reference Adaptive Control (MRAC). Otherwise, MRAC modification in [20] is used to catch possibly dangerous UAVs mid-air using a net carried by two drones. The modification proposed in [20] is adding a compensation with a proportional-derivative-integral action to avoid instability of the system due to abrupt changes in altitude.…”

“…Otherwise, MRAC modification in [20] is used to catch possibly dangerous UAVs mid-air using a net carried by two drones. The modification proposed in [20] is adding a compensation with a proportional-derivative-integral action to avoid instability of the system due to abrupt changes in altitude. Besides, a comparative between Proportional-Integral-Derivative and MRAC control for a quadrotor to stabilize the Euler angles roll, pitch, and yaw was analyzed in [21].…”

Real-Time Terrain-Following of an Autonomous Quadrotor by Multi-Sensor Fusion and Control

“…The height control of common UAVs applies a proportional-integral-derivative (PID) control structure for the altitude that handles high impact and changed mass [15][16][17]. These controllers compare the behavior of the UAVs with a model and adapt the output of the controller to minimize the error between the model and the system.…”