Optimal Tracking Controller Design for a Small Scale Helicopter

A model helicopter is more difficult to control than its full scale counterpart. This is due to its greater sensitivity to control inputs and disturbances as well as higher bandwidth of dynamics. This work is focused on designing practical tracking controller for a small scale helicopter following predefined trajectories. A tracking controller based on optimal control theory is synthesized as a part of the development of an autonomous helicopter. Some issues with regards to control constraints are addressed. The weighting between state tracking performance and control power expenditure is analyzed. Overall performance of the control design is evaluated based on its time domain histories of trajectories as well as control inputs.

Automated Flight Test and System Identification for Rotary Wing Small Aerial Platform Using Frequency Responses Analysis

This paper proposes an autopilot system that can be used to control the small scale rotorcraft during the flight test for linear-frequency-domain system identification. The input frequency-sweep is generated automatically as part of the autopilot control command. Therefore the bandwidth coverage and consistency of the frequency-sweep are guaranteed to produce high quality data for system identification. Beside that, we can set the safety parameters during the flight test （maximum roll/pitch value, minimum altitude, etc.） so the safety of the whole flight test is guaranteed. This autopilot system is validated using hardware in the loop simulator for hover flight condition.

Nonlinear system modeling and identification of small helicopter based on genetic algorithm

Purpose–The purpose of this paper is to build nonlinear model of a small rotorcraft-based unmanned aerial vehicles(RUAV),using nonlinear system identification method to estimate the parameters of the model.The nonlinear model will be used in robust control system design and aerodynamic analysis.Design/methodology/approach–The nonlinear model is built based on mechanism theory,aerodynamics and mechanics,which can reflect most dynamics in large flight envelop.Genetic algorithm(GA)and time domain flight data is adopted to estimate unknown parameters of the model.The flight data were collected from a series of fight tests.The identification results were also analyzed and validated.Findings–The nonlinear model of RUAV has better accuracy,the parameters are physical quantities,and having distinctly recognizable values.The GA is suitable for nonlinear system identification.And the results proved the identified model can reflect the dynamic characteristics in extensive area of flight envelop.Research limitations/implications–The GA requires much more computing power,to identify 12 unknown parameters with 30 iterations,will takes more than 18 hours of a four cores desktop computer.Because of this is an off-line identification process,and has more accuracy,extra time is acceptable.Originality/value–GA method has significantly increased the accuracy of the model.The previous work of system identification used a ten states linear model,and using PEM identified 23 coefficients.By carefully building the nonlinear model,it has only 21 unknown parameters,but if the model is linearized,it will get a linear model more than 35 states,which shows nonlinear model contain more dynamics than linear model.