Control of heading direction and floating height of a flying robot

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)

Coupland

et al. 2008

Abstract-In this paper we evolve the parameters of a proportional, integral, and derivative (PID) controller for an unstable, complex and nonlinear system. The individuals of the applied genetic algorithm (GA) are evaluated on the actual system rather than on a simulation of it. This makes implicit a formal model identification for the implementation of a simulator. This also calls for the GA to be approached in an unusual way, where we need to consider new aspects not normally present in the usual situations using an unnaturally consistent simulator for fitness evaluation. Although elitism is used in the GAs, no monotonic increase in fitness is exhibited by the algorithm. Instead, we show that the GA's individuals converge towards more robust solutions.

Section: Experimental System Setupmentioning

confidence: 99%

“…Because of these characteristics, a controller for an autonomous helicopter must be fast in computing the control response. Active control is traditionally implemented using a combination of proportional, integral, and derivative (PID) control methods [6] which are suitable for efficient control of a helicopter [7], [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Real-time evolution of an embedded controller for an autonomous helicopter

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)

Coupland

et al. 2008

“…Puntunan and Parnichkun introduce a heading direction and floating height controller for a single rotor helicopter [6]. The control system uses a Proportional plus Derivative controller (PD) to maintain the helicopter's heading and height, while a human pilot controls the horizontal movements remotely.…”

Section: A Helicopter Controlmentioning

confidence: 99%

“…Traditional control techniques using a combination of Proportional-Integral-Derivative (PID) control methods have been successfully used in helicopter control [4], [5], [6].…”

Section: A Helicopter Controlmentioning

confidence: 99%

Managing uncertainty in sound based control for an autonomous helicopter

2009 IEEE Workshop on Computational Intelligence in Vehicles and Vehicular Systems

Smith

et al. 2009

Abstract-In this paper we present our ongoing research using a multi-purpose, small and low cost autonomous helicopter platform (Flyper). We are building on previously achieved stable control using evolutionary tuning. We propose a sound based supervised method to localise the indoor helicopter and extract meaningful information to enable the helicopter to further stabilise its flight and correct its flightpath. Due to the high amount of uncertainty in the data, we propose the use of fuzzy logic in the signal processing of the sound signature. We discuss the benefits and difficulties using type-1 and type-2 fuzzy logic in this real-time systems and give an overview of our proposed system.

“…Others showed that PID controllers are very capable of stabilising helicopters [8], [9]. Nevertheless, determining good PID control parameters can be a challenging task [10].…”

Section: A Autonomous Helicoptermentioning

confidence: 99%

Mitigating the effect of background noise in sound based helicopter control

Coupland

International Conference on Fuzzy Systems

2010

Abstract-Our research focuses on the use of sound to enhance the control of an autonomous indoor helicopterFlyper. One of the many challenging problems in this project is managing the uncertainty which is present in input data and the control actions; this paper focuses on managing the uncertainty in the input data. We present a fuzzy logic system which infers how much confidence should be placed on a control decision based on the data which was used to make that decision. The input data is a supervised and sound based position estimate of the flying robot. The output of the fuzzy inference system provides us with a confidence parameter used to attenuate the position control of the autonomous helicopter. We performed test flights with and without the fuzzy confidence parameter and with and without artificial disturbance in form of concurrent speech. We employed a motion tracker to capture the helicopter's movement during all test flights. The analysis of the data collected shows encouraging results.