This study addresses the control problem of an unmanned quadrotor in an indoor environment where there is lack of absolute localisation data. Based on an attached inertia measurement unit, a sonar and an optic-flow sensor, the state vector is estimated using sensor fusion algorithms. A novel switching model predictive controller is designed in order to achieve precise trajectory control, under the presence of forcible wind gusts. The quadrotor’s attitude, altitude and horizontal linearised dynamics result in a set of piecewise affine models, enabling the controller to account for a larger part of the quadrotor’s flight envelope while modelling the effects of atmospheric disturbances as additive-affine terms in the system. The proposed controller algorithm accounts for the state and actuation constraints of the system. The controller is implemented on a quadrotor prototype in indoor position tracking, hovering and attitude manoeuvres experiments. The experimental results indicate the overall system’s efficiency in position/altitude/attitude set-point manoeuvres.

Describes the design of generic virtual instruments used for real-time experimentation at Polytechnic University's control engineering laboratory in a remote-access environment. These instruments can be freely downloaded and the remote user can access the laboratory facilities from anywhere at any time. Our Internet-accessed remote laboratory is based on a client/server computer configuration. The server, situated near the experiment, transfers to it the received command signals transmitted by the client. The client locally computes the command signal based on the reference waveform and the transmitted system response. The remote user can select the transmission protocol, switch between asynchronous and synchronous sampling, use either a batch or a recursive data transfer mode, and view the experimental testbed. Our approach is distinct from others in that it offers more flexibility and responsibility to the client side, since the remote user compiles and executes the controller locally. Issues concerned with network reliability, dynamic delays caused by Internet traffic, concurrent user access, and limited computing power have been addressed. The designed set of experiments is the first step toward our remote control laboratory.

This paper presents a pattern recognition approach for the identification of basic hand movements using surface electromyographic (EMG) data. The EMG signal is decomposed using Empirical Mode Decomposition (EMD) into Intrinsic Mode Functions (IMFs) and subsequently a feature extraction stage takes place. Various combinations of feature subsets are tested using a simple linear classifier for the detection task. Our results suggest that the use of EMD can increase the discrimination ability of the conventional feature sets extracted from the raw EMG signal.