Modelling and control of a hybrid electric propulsion system for unmanned aerial vehicles

This paper presents the modelling and control of a hybrid electric propulsion system designed for unmanned aerial vehicles. The work is carried out as part of the AIRSTART project in collaboration with Rotron Power Ltd. Firstly, the entire parallel hybrid powertrain is divided into two powertrains to facilitate the modelling and control. Following this, an engine model is built to predict the dynamics between the throttle request and the resulting output. It is then validated by comparing with experimental data. On the basis of d-q model of the motor/generator, a good estimation of torque loss at steady state is achieved using the efficiency map. Next, a rule-based controller is designed to achieve the best fuel consumption by regulating the engine to operating around its ideal operating line. Following the integration of the models and controller, the component behaviour and control logic are verified via the final simulation. By enabling the engine to operate at its best fuel economy condition, the hybrid propulsion system developed in this research can save at least 7% on fuel consumption when compared with an internal combustion engine powered aircraft

STUDY OF CONTROL SCHEMES FOR SERIES HYBRID-ELECTRIC POWERTRAIN FOR UNMANNED AERIAL SYSTEMS

Hybrid-Electric aircraft powertrain modeling for Unmanned Aerial Systems (UAS) is a useful tool for predicting powertrain performance of the UAS aircraft. However, for small UAS, potential gains in range and endurance can depend significantly on the aircraft flight profile and powertrain control logic in addition to the subsequent impact on the performance of powertrain components. Small UAS aircraft utilize small-displacement engines with poor thermal efficiency and, therefore, could benefit from a hybridized powertrain by reducing fuel consumption. This study uses a dynamic simulation of a UAS, representative flight profiles, and powertrain control logic approaches to evaluate the performance of a series hybrid-electric powertrain. Hybrid powertrain component models were developed using lookup tables of test data and model parameterization approaches to generate a UAS dynamic system model. These models were then used to test three different hybrid powertrain control strategies for their ability to provide efficient IC engine operation during the charging process. The baseline controller analyzed in this work does not focus on optimizing fuel efficiency. In contrast, the other two controllers utilize engine fuel consumption data to develop a scheme to reduce fuel consumption during the battery charging operation. The performance of the powertrain controllers is evaluated for a UAS operating on three different representative mission profiles relevant to cruising, maneuvering, and surveillance missions. Fuel consumption and battery state of charge form two metrics that are used to evaluate the performance of each controller. The first fuel efficiency-focused controller is the ideal operating line (IOL) strategy. The IOL strategy uses performance maps obtained by engine characterization on a specialized dynamometer. The simulations showed the IOL strategy produced average fuel economy improvements ranging from 12%-15% for a 30-minute mission profile compared to the baseline controller. The last controller utilizes fuzzy logic to manage the charging operations while maintaining efficient fuel operation where it produced similar fuel saving to the IOL method but were generally higher by 2-3%. The importance of developing detailed dynamic system models to capture the power variations during flight with fuel-efficient powertrain controllers is key to maximizing small UAS hybrid powertrain performance in varying operating conditions

Evaluation of Hybrid-Electric Power System Integration Challenges for Multi-Rotor UAS

The modern standard for multi-rotor unmanned aircraft system (UAS) propulsion is an electric configuration typically consisting of battery-powered electric motors. The primary issue with this type of propulsion system is the low energy density of the battery, which results in shorter flight times or lower payload capacity than typically desired. One possible solution for this is implementation of a hybrid-electric power system, which has been demonstrated to improve endurance in both fixed and rotary wing aircraft. The purpose of this study is to investigate the integration challenges of implementing a hybrid-electric power system on multi-rotor UAS, specifically one that utilizes a gasoline internal combustion engine. While design of a fully-integrated hybrid-electric power system is not within the scope of this study, rather experiments are conducted with intentions to provide crucial insights to expedite the design and implementation process. Integration challenges such as vibration, cooling requirements, and additional noise of small combustion engines are investigated. It is shown that a small combustion engine can produce forceful vibration signatures, the affects of which must be considered when designing a hybrid-electric power system. Without proper dampening, this additional vibration has been shown to negatively impact the function of the on-board sensors necessary for controlling small UAS (sUAS). It has also been shown that forced convection, or other external cooling, is a requirement for the small combustion engine used for this study, which presents a unique challenge in hover-capable aircraft that don't inherently supply active airflow. However, the cooling requirements of these types of engines can be estimated when designing a hybrid-electric power system, and weight-efficient solutions can be found. Finally, it has been shown that a small, two-stroke combustion engine would have a significant contribution to the overall noise signature of a hybrid-electric sUAS. Through a computational method of isolating and combining individual noise sources, the theoretical acoustic signature of a multi-rotor sUAS with a hybrid-electric power system was produced. The conclusion of this study confirms that each of the initially identified major integration challenges (vibration, cooling, and noise) must be considered when designing a hybrid-electric power system for multi-rotor sUAS, and also provides insights into how each challenge could be addressed.Mechanical and Aerospace Engineerin

Scaling effects of 5- to 13-kw turboelectric unmanned aircraft power systems

This thesis details ground tests of three different turboelectric systems for unmanned aircraft applications based on existing 5-kW, 7-kW and 13-kW turbine engines. The motivation for this study is the continuing emergence of hybrid gas-electric power systems for manned and unmanned aircraft to extend the range over battery-only aircraft and decrease carbon emissions associated with hydrocarbon fuels. However, there is currently a lack of published experimentalperformance data on turboelectric power systems, though there are many paper designs and analytical studies of aircraft turboelectric systems. This thesis compares the effect of scale inperformance parameters by comparing the three different turboelectric systems. A bench test stand with representative electrical loads was built and used for static ground testing of these turboelectric systems. Steady-state tests measured turboelectric system fuel usage and power production for calculating brake specific fuel consumption and power-to-weight ratio of the turboelectric systems. Transient tests measured response time and rate-of-change of power. Data from both steady state and transient tests highlight electrical safety challenges. A better understanding of the effects of scale on turboelectric system will allow for better performance estimates for design purposes, inform mission planning for unmanned aircraft, and enable future comparisons of turboelectric systems to piston-based hybrid gas-electric systems and battery-only systems for unmanned aircraft

The Control of a Parallel Hybrid-Electric Propulsion System for a Small Unmanned Aerial Vehicle Using a CMAC Neural Network

A Simulink model, a propulsion energy optimization algorithm, and a CMAC controller were developed for a small parallel hybrid-electric unmanned aerial vehicle (UAV). The hybrid-electric UAV is intended for military, homeland security, and disaster-monitoring missions involving intelligence, surveillance, and reconnaissance (ISR). The Simulink model is a forward-facing simulation program used to test different control strategies. The flexible energy optimization algorithm for the propulsion system allows relative importance to be assigned between the use of gasoline, electricity, and recharging. A cerebellar model arithmetic computer (CMAC) neural network approximates the energy optimization results and is used to control the parallel hybrid-electric propulsion system. The hybrid-electric UAV with the CMAC controller uses 67.3% less energy than a two-stroke gasoline-powered UAV during a 1-h ISR mission and 37.8% less energy during a longer 3-h ISR mission

Error minimising gradients for improving cerebellar model articulation controller performance

In motion control applications where the desired trajectory velocity exceeds an actuator’s maximum velocity limitations, large position errors will occur between the desired and actual trajectory responses. In these situations standard control approaches cannot predict the output saturation of the actuator and thus the associated error summation cannot be minimised.An adaptive feedforward control solution such as the Cerebellar Model Articulation Controller (CMAC) is able to provide an inherent level of prediction for these situations, moving the system output in the direction of the excessive desired velocity before actuator saturation occurs. However the pre-empting level of a CMAC is not adaptive, and thus the optimal point in time to start moving the system output in the direction of the excessive desired velocity remains unsolved. While the CMAC can adaptively minimise an actuator’s position error, the minimisation of the summation of error over time created by the divergence of the desired and actual trajectory responses requires an additional adaptive level of control.This thesis presents an improved method of training CMACs to minimise the summation of error over time created when the desired trajectory velocity exceeds the actuator’s maximum velocity limitations. This improved method called the Error Minimising Gradient Controller (EMGC) is able to adaptively modify a CMAC’s training signal so that the CMAC will start to move the output of the system in the direction of the excessive desired velocity with an optimised pre-empting level.The EMGC was originally created to minimise the loss of linguistic information conveyed through an actuated series of concatenated hand sign gestures reproducing deafblind sign language. The EMGC concept however is able to be implemented on any system where the error summation associated with excessive desired velocities needs to be minimised, with the EMGC producing an improved output approximation over using a CMAC alone.In this thesis, the EMGC was tested and benchmarked against a feedforward / feedback combined controller using a CMAC and PID controller. The EMGC was tested on an air-muscle actuator for a variety of situations comprising of a position discontinuity in a continuous desired trajectory. Tested situations included various discontinuity magnitudes together with varying approach and departure gradient profiles.Testing demonstrated that the addition of an EMGC can reduce a situation’s error summation magnitude if the base CMAC controller has not already provided a prior enough pre-empting output in the direction of the situation. The addition of an EMGC to a CMAC produces an improved approximation of reproduced motion trajectories, not only minimising position error for a single sampling instance, but also over time for periodic signals