Mansoor Ahsan

Heading control of a fixed wing UAV using alternate control surfaces

Unmanned Air Vehicles (UAVs) have incredible competencies in forces and civil relevancies. The flight and navigation of a UAV is autonomously controlled by an onboard autopilot. The heading control of a UAV is a vital operation of autopilot, executed by employing a design algorithm that controls its direction and navigation. Commercially available autopilots mostly exploit Proportional-Integral-Derivative (PID) based heading controllers using aileron deflection as the input variable. In this paper, we give a comparison of the performances of two heading-controller design techniques i.e. aileron based heading controller and rudder based heading controller using a Proportional Integral Differential (PID) controller. We have taken a nonlinear model of a small sized UAV-Aerosonde. This model is then linearized around a stable trim point and subsequently decoupled for longitudinal and lateral designs. The small perturbation control theory helps us test the designed controllers with the nonlinear model. The compensated linear and nonlinear models along with their results are presented. Our exploration reveals that rudder based heading controller has intrinsic potency compared to commercially employed aileron based heading controllers for UAV heading control, with regard to better transient response, thus improving the overall response as well as payload performance during a heading change maneuver. The conclusion may show the way to a valuable outcome in autopilot design of UAV.

Modeling of lateral dynamics of a UAV using system identification approach

System identification of an Unmanned Aerial Vehicle (UAV) for its mathematical modeling is an important step towards the automation of aircraft. A reliable mathematical model is also required for simulator design, aircraft crash analysis, studying the effects of aircraft modifications, preflight testing, stress distribution analysis and fatigue life appraisal etc. In this research work flight experiment was conducted for identification of lateral dynamics of a fixed wing UAV. Aircraft states were recorded during specifically designed flight maneuvers. After necessary preprocessing and bias removal, acquired data was further processed in Matlab System Identification toolbox for estimating lateral dynamics transfer functions related to aileron and rudder inputs. The black box identification based transfer function models were estimated using least square error estimation technique. The identification results present a reliable lateral model of the UAV with high level of confidence and goodness of fit between the actual system response and estimated model. The identified transfer function models can be used for various applications including heading controller design for an autopilot.

A new perspective to velocity control for fixed wing UAVs

Unmanned Aerial Vehicles (UAVs) are used in wide ranging applications by civil as well as military organizations all over the world. In an autonomous UAV, a controller onboard the vehicle automatically controls the aircrafts flight and navigation. Airspeed is an important aircraft state that is generally controlled using Proportional Integral Derivative (PID) controller in most of the commercially available autopilots. In this research work, we first used a PID controller to control the airspeed of Aerosonde UAV that has been modeled as a nonlinear system in Matlab software. On the other hand, we have also used a Phase Lead compensator to control Aerosondes airspeed. For controller design, the nonlinear aircraft model is linearized around a stable trim point and airspeed controller is designed for the decoupled longitudinal model. Controller performance has been subsequently verified against the nonlinear aircraft model. A performance comparison of airspeed PID controller and Phase Lead compensator is carried out. Our findings show that the Phase Lead compensator performs better in controlling velocity of the UAV. It is expected that this research may lead to a more effective and efficient autopilot design for airspeed control of UAVs.

Time domain system identification of longitudinal dynamics of a UAV: A grey box approach

System identification is very effective for aircraft modeling because many aircraft motions cannot be duplicated accurately using analytical methods. The identified model can represent an aircraft in all flight regimes and thus can be used for the development of flight simulators and automatic flight controllers. In this research, a unique three stage procedure is presented for time domain system identification of small scale fixed wing UAV. Flight experiment was conducted with specifically designed maneuvers for identification of the UAVs longitudinal dynamics. Initial reference model was developed using UAVs geometrical information in DATCOM. Recorded data, from flight tests, was processed in MATLAB system identification toolbox for estimating grey box aircraft models by applying Prediction Error Method. The model was iteratively improved through Adaptive Gauss Newton optimization. Model validation and error analysis were performed and the UAVs aerodynamic coefficients were determined. Excellent validation results show that the identified model can be used for various applications including the design of altitude and airspeed controllers of autopilot.

Performance evaluation of two linear controllers for heading control of a UAV

In recent years, Unmanned Aerial Vehicles (UAVs) have become very popular in civil and defense sectors. Due to their numerous applications, the UAV industry is flourishing with great pace. An autopilot having the complete flight control system is the heart of a UAV. It is an onboard system which controls and navigates the in-flight UAV autonomously. These days, multiple commercial solutions for UAV autopilot are available in the market. In these commercially off the shelf (COTS) solutions, mostly ailerons are used as the input control surface. PID (Proportional-integral-derivative) based heading controllers are generally used for steering the UAV in the desired direction. In our work, performance evaluation of two different heading-controllers is carried out. In both the schemes, aileron deflection acts as the input variable for the feedback and control mechanism. One of the controllers uses PID based controller while the other one makes use of phase-lag based heading controller. First step in the controller design process includes selection of a nonlinear UAV model, which is linearized at steady state trim conditions. The next step includes design of linear PID and phase-lag controllers and their subsequent application on nonlinear model. Finally, the effectiveness of deigned controllers is gauged by comparing the simulation results of compensated linear and nonlinear models. Our investigation clearly proves that PID-based heading controllers are a better choice, as these have better transient and steady-state response as compared to Phase-lag controllers.

Analysis of varying control inputs for a fixed wing unmanned aerial vehicle

In UAVs, autopilot substitutes the pilot and undertakes autonomous control and navigation of the aircraft. The airspeed controller is an important part of autopilot which adjusts the speed of the aircraft according to the requirement in different phases of the flight, and avoids aircraft instability during difficult maneuvers. In the design of airspeed controller, most designers implement Proportional Integral Derivative (PID) compensators by using control input from throttle only. In this paper, we present the performance-based comparison of two airspeed control schemes. One method controls the airspeed in a conventional way i.e. by throttle as an input variable; while the other algorithm uses combination of throttle and elevator for tracking the airspeed commands. In our work, a non-linear model of UAV has been trimmed, linearized and decoupled for designing the linear controllers. The controllers are then applied to nonlinear model and simulation results for both are compared. The research clearly indicates that by using combination of throttle and elevator as control inputs, better airspeed control can be achieved both in terms of transient response and payload performance. This finding may be a useful contribution towards the effective airspeed control of aircrafts and UAVs.