Development of c-means Clustering Based Adaptive Fuzzy Controller for A Flapping Wing Micro Air Vehicle

Advanced and accurate modelling of a Flapping Wing Micro Air Vehicle (FW MAV) and its control is one of the recent research topics related to the field of autonomous Unmanned Aerial Vehicles (UAVs). In this work, a four wing Natureinspired (NI) FW MAV is modeled and controlled inspiring by its advanced features like quick flight, vertical take-off and landing, hovering, and fast turn, and enhanced manoeuvrability when contrasted with comparable-sized fixed and rotary wing UAVs. The Fuzzy C-Means (FCM) clustering algorithm is utilized to demonstrate the NIFW MAV model, which has points of interest over first principle based modelling since it does not depend on the system dynamics, rather based on data and can incorporate various uncertainties like sensor error. The same clustering strategy is used to develop an adaptive fuzzy controller. The controller is then utilized to control the altitude of the NIFW MAV, that can adapt with environmental disturbances by tuning the antecedent and consequent parameters of the fuzzy system.Comment: this paper is currently under review in Journal of Artificial Intelligence and Soft Computing Researc

PAC: A Novel Self-Adaptive Neuro-Fuzzy Controller for Micro Aerial Vehicles

There exists an increasing demand for a flexible and computationally efficient controller for micro aerial vehicles (MAVs) due to a high degree of environmental perturbations. In this work, an evolving neuro-fuzzy controller, namely Parsimonious Controller (PAC) is proposed. It features fewer network parameters than conventional approaches due to the absence of rule premise parameters. PAC is built upon a recently developed evolving neuro-fuzzy system known as parsimonious learning machine (PALM) and adopts new rule growing and pruning modules derived from the approximation of bias and variance. These rule adaptation methods have no reliance on user-defined thresholds, thereby increasing the PAC's autonomy for real-time deployment. PAC adapts the consequent parameters with the sliding mode control (SMC) theory in the single-pass fashion. The boundedness and convergence of the closed-loop control system's tracking error and the controller's consequent parameters are confirmed by utilizing the LaSalle-Yoshizawa theorem. Lastly, the controller's efficacy is evaluated by observing various trajectory tracking performance from a bio-inspired flapping-wing micro aerial vehicle (BI-FWMAV) and a rotary wing micro aerial vehicle called hexacopter. Furthermore, it is compared to three distinctive controllers. Our PAC outperforms the linear PID controller and feed-forward neural network (FFNN) based nonlinear adaptive controller. Compared to its predecessor, G-controller, the tracking accuracy is comparable, but the PAC incurs significantly fewer parameters to attain similar or better performance than the G-controller.Comment: This paper has been accepted for publication in Information Science Journal 201

Aerodynamic Sensing for a Fixed Wing UAS Operating at High Angles of Attack

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/97104/1/AIAA2012-4416.pd

Adaptive fuzzy control of unmanned underwater vehicles

168-175Unmanned Underwater Vehicles (UUVs) have been playing an increasingly important role in military and civilian operations and been widely used in various applications. The main issue associated with the development and design of UUV’s is the control system design. These vehicles have nonlinear dynamics and coupling, and tend to exhibit time varying characteristics. In addition they are subject to different environmental disturbances. Successful completion of the UUV missions depends on the control provided by the autopilot unit mounted on board. These controllers need to be tuned and analysed before implementing them in real environment. In the present work, the first objective is to demonstrate the capability of adaptive network fuzzy inference system, namely ANFIS for modelling of UUVs and the second objective is to design a fuzzy controller using the ANFIS model. The input output data from the UUV are used for the ANFIS modelling. This model is used in the design and validation of the fuzzy controller and the results are compared with a conventional PID controller

The simulation of vibration at the supine patient\u2019s body in HEMS

The associated risk for a patient taking advantage of Helicopter Emergency Medical Services (HEMS) mainly relates to vibrations. To this aim, especially in case of trauma and serious illness and long travels, aspects related to the vibrations effects on the patient needs to be properly investigated, taking into account the particular features of the helicopter used for HEMS. The purpose of this paper is the integrated simulation of vertical vibration at the supine patient\u2019s body, lying down on a rescue litter, in HEMS. Here, a 3D structural model of a reference helicopter, the Aerospatiale Gazelle, has been developed in ABAQUS and integrated with a dynamic model where aerodynamic forcing functions due to rotor are modelled. The accelerations at the litter location in the helicopter structural model are recorded and used as inputs to the supine patient\u2019s body model in MATLAB. The Multi-Body (MB) model of the patient\u2019s body consists of three interconnected masses of the head-neck, torso-arms and pelvis-legs. The simulation is carried out for single manoeuvre and the vibration information is extracted at the three body segments. This predictive approach of integrated simulation of the helicopter-patient is an effective tool in investigating of biodynamic response of the patient in HEMS. The benefits of this approach are reducing the risk and costs of running the experiments, providing suggestions on redesigning of litters and controlling the vibration on the litters in HEMS