Virtual technology and haptic interface solutions for design and control of mobile working machines

Commercially available haptic interfaces are usable for many purposes. However, as generic devices they are not the most suitable for the control of heavy duty mobile working machines like mining machines, container handling equipment and excavators. Alternative mechanical constructions for a haptic controller are presented and analysed. A virtual reality environment (VRE) was built to test the proposed haptic controller mechanisms. Verification of an electric motor emulating a hydraulic pump in the electro-hydraulic system of a mobile working machine is carried out. A real-time simulator using multi-body-dynamics based software with hardware-in-loop (HIL) setup was used for the tests. Recommendations for further development of a haptic controller and emulator electric motor are given.

Adaptive control of Hydraulic Drive

Adaptive control systems are one of the most significant research directions of modern control theory. It is well known that every mechanical appliance’s behavior noticeably depends on environmental changes, functioning-mode parameter changes and changes in technical characteristics of internal functional devices. An adaptive controller involved in control process allows reducing an influence of such changes. In spite of this such type of control methods is applied seldom due to specifics of a controller designing. The work presented in this paper shows the design process of the adaptive controller built by Lyapunov’s function method for the Hydraulic Drive. The calculation needed and the modeling were conducting with MATLAB® software including Simulink® and Symbolic Math Toolbox™ etc. In the work there was applied the Jacobi matrix linearization of the object’s mathematical model and derivation of the suitable reference models based on Newton’s characteristic polynomial. The intelligent adaptive to nonlinearities algorithm for solving Lyapunov’s equation was developed. Developed algorithm works properly but considered plant is not met requirement of functioning with. The results showed confirmation that adaptive systems application significantly increases possibilities in use devices and might be used for correction a system’s behavior dynamics.

Study of Control Methods of a High-Frequency Cycloconverter for Domestic Induction Heating Appliances

A high-frequency cyclonverter acts as a direct ac-to-ac power converter circuit that does not require a diode bidge rectifier. Bridgeless topology makes it possible to remove forward voltage drop losses that are present in a diode bridge. In addition, the on-state losses can be reduced to 1.5 times the on-state resistance of switches in half-bridge operation of the cycloconverter. A high-frequency cycloconverter is reviewed and the charging effect of the dc-capacitors in ``back-to-back'' or synchronous mode operation operation is analyzed. In addition, a control method is introduced for regulating dc-voltage of the ac-side capacitors in synchronous operation mode. The controller regulates the dc-capacitors and prevents switches from reaching overvoltage level. This can be accomplished by variating phase-shift between the upper and the lower gate signals. By adding phase-shift between the gate signal pairs, the charge stored in the energy storage capacitors can be discharged through the resonant load and substantially, the output resonant current amplitude can be improved. The above goals are analyzed and illustrated with simulation. Theory is supported with practical measurements where the proposed control method is implemented in an FPGA device and tested with a high-frequency cycloconverter using super-junction power MOSFETs as switching devices.

Control of Robot by Means of Human Thought

Brain computer interface (BCI) is a kind of human machine interface, which provides a new interaction method between human and computer or other equipment. The most significant characteristic of BCI system is that its control input is brain electrical activities acquired from the brain instead of traditional input such as hands or eyes. BCI technique has rapidly developed during last two decades and it has mainly worked as an auxiliary technique to help the disable people improve their life qualities. With the appearance of low cost novel electrical devices such as EMOTIV, BCI technique has been applied to the general public through many useful applications including video gaming, virtual reality and virtual keyboard. The purpose of this research is to be familiar with EMOTIV EPOC system and make use of it to build an EEG based BCI system for controlling an industrial manipulator by means of human thought. To build a BCI system, an acquisition program based on EMOTIV EPOC system is designed and a MFC based dialog that works as an operation panel is presented. Furthermore, the inverse kinematics of RV-3SB industrial robot was solved. In the last part of this research, the designed BCI system with human thought input is examined and the results indicate that the system is running smoothly and displays clearly the motion type and the incremental displacement of the motion.

Using electroencephalography signals to control acoustical processing

Current hearing-assistive technology performs poorly in noisy multi-talker conditions. The goal of this thesis was to establish the feasibility of using EEG to guide acoustic processing in such conditions. To attain this goal, this research developed a model via the constructive research method, relying on literature review. Several approaches have revealed improvements in the performance of hearing-assistive devices under multi-talker conditions, namely beamforming spatial filtering, model-based sparse coding shrinkage, and onset enhancement of the speech signal. Prior research has shown that electroencephalography (EEG) signals contain information that concerns whether the person is actively listening, what the listener is listening to, and where the attended sound source is. This thesis constructed a model for using EEG information to control beamforming, model-based sparse coding shrinkage, and onset enhancement of the speech signal. The purpose of this model is to propose a framework for using EEG signals to control sound processing to select a single talker in a noisy environment containing multiple talkers speaking simultaneously. On a theoretical level, the model showed that EEG can control acoustical processing. An analysis of the model identified a requirement for real-time processing and that the model inherits the computationally intensive properties of acoustical processing, although the model itself is low complexity placing a relatively small load on computational resources. A research priority is to develop a prototype that controls hearing-assistive devices with EEG. This thesis concludes highlighting challenges for future research.