This paper presents a model predictive controller (MPC) for position control of a vertical take-off and landing (VTOL) tail-sitter unmanned aerial vehicle (UAV) in hover flight. A ‘cross’ configuration quad-rotor tail-sitter UAV is designed with the capabilities for both hover and high efficiency level flight. The six-degree-of-freedom (DOF) nonlinear dynamic model of the UAV is built based on aerodynamic data obtained from wind tunnel experiments. The model predictive position controller is then developed with the augmented linearized state-space model. Measured and unmeasured disturbance model are introduced into the modeling and optimization process to improve disturbance rejection ability. The MPC controller is first verified and tuned in the hardware-in-loop (HIL) simulation environment and then implemented in an on-board flight computer for real-time indoor experiments. The simulation and experimental results show that the proposed MPC position controller has good trajectory tracking performance and robust position holding capability under the conditions of prevailing and gusty winds.
Due to its non-holonomic constraints and a highly unstable nature, the autonomous bicycle is difficult to be controlled for tracking a target path while retaining its balance. As a result of the non-holonomic constraint conditions, the instantaneous velocity of the vehicle is limited to certain directions. Constraints of this kind occur under the no-slip condition. In this study, the problem of optimization of fuzzy logic controllers (FLCs) for path-tracking of an autonomous robotic bicycle using genetic algorithm (GA) is focused. In order to implement path-tracking algorithm, strategies for balancing and tracking a given roll-angle are also addressed. The proposed strategy optimizes FLCs by keeping the rule-table fixed and tuning their membership functions by introducing the scaling factors (SFs) and deforming coefficients (DCs). The numerical simualtions prove the effectiveness of the proposed structure of the genetic fuzzy controller for the developed bicycle system.
This study proposes the design of an active stabilizing system (ASAS) for a single-track vehicle. Using the gyroscopic effects of two flywheels, this system can generate control torque to stabilize the vehicle in cases where there is centrifugal force of turning. To control the flywheel gimbals to generate stabilizing torque, a model predictive controller (MPC) is applied to control the system. For the controller design and performance evaluations, a model of a gyroscopic inverted pendulum is developed. Control strategies are proposed to stabilize the vehicle in the cases of straight running, circular motion, and path following. The results of the proposed stratgies when controlling the gyroscopic inverted pendulum showed good performance even with physical limitations of the control torques. In order to evaluate the real-time performance and the feasibility of the MPC, a real-time simulator is employed, which includes two embedded STM32F407 boards. The dynamic system and the control algorithms are respectively embedded into two STM32F407 boards for real-time simulation. Implementations of the MPC in this study demonstrate that the proposed controllers are feasible for real-time applications.
This paper presents the development of dynamic equations and roll-angle-tracking controller of an unmanned bicycle. First, the equations of motion and constraints of a bicycle with rolling-withoutslipping contact condition between wheels and ground are developed using Lagrange's equations. The equations are then used to implement the simulation of the bicycle dynamics. With the bicycle model, a fuzzy-logic controller, which is adaptive to the speed change is implemented to control the bicycle to follow the roll-angle commands. The controller parameters where fuzzy membership functions are presented by scaling factors and deforming coefficients are optimised using genetic algorithms. Results show that the bicycle can follow the roll-angle command with short time delay and the control structure can adapt to a wide range of speed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.