Level

Date

 Not chosen  

 30.5.2008 

In this paper, we propose a sliding mode technique to control the field- oriented synchronous machine. The sliding mode controller is designed for a class of non linear dynamic systems to tackle the problems with model uncertainties, parameter fluctuations and external disturbances. Our aim is to make the position control robust to parameter variations. The use of the nonlinear sliding mode method provides very good performance for motor operation and robustness of the control law despite the external/internal perturbations. An observer is considered to overcome the problem of torque disturbance. A load torque observer is designed based on neural network technique without affecting the overall system response. Simulation results are given to highlight the performance of the proposed control technique under load disturbances and parameter uncertainties.

 Not chosen  

 30.5.2008 

The kinematics problems of redundant robots have been investigated for many years. Plenty of different applications for robot redundancy were implemented with success. Some of them were: improvement of redundant robot manipulability, robot obstacle avoidance, robot energy consumption optimization etc. Widely used methods use conventional approaches as for example in case of manipulability enhancement is used the gradient computation. However, the computational effort of these approaches brings many difficulties when it is used with other constraints. The solution to these problems is implementation of new intelligent methods based on artificial neural networks. This paper deals with application such methods where CMAC and HCMAC (Hierarchical Cerebellar Model Arithmetic Controller) neural networks were used in redundancy control of 7 DOF redundant manipulator. And manipulability enhancement constraint was chosen as redundancy constraint. First tested neural network was conventional CMAC neural network with supervised learning. It performed well in the terms of fast learning and local generalization capability. Nevertheless, the conventional CMAC has showed enormous memory requirement. Another tested neural network for the same task was HCMAC. It is shown that HCMAC perfectly approximated the testing function with relatively fast learning.

 Not chosen  

 30.5.2008 

Due to the arising liberalization and deregulation of the electric energy market, the demand on AC-transmission system are steadily growing. Therefore, more sophisticated ac-system controllers are highly needed. This paper is focusing on one of these topologies, i.e the two-levels voltage source inverter static compensator (Statcom), which is an interesting high power circuit for transmission applications. A steady state and transient stability models with GTO thyristors are proposed. Simulations are carried out in a 9 bus, 3 machines AC system. The proposed model is suitable for power system studies that require accurate representation as transient stability and voltage stability.

 Not chosen  

 30.5.2008 

This paper uses an ant system (AS) meta-heuristic optimization method to solve the problem of structure optimization of power cellular systems. We consider the case where redundant cells are introduced to achieve a desirable level of reliability. The cells of the system are characterized by their cost, technology, capacity and availability. The reliability is defined as the ability to satisfy the consumer demand (battery load) which is represented as a piecewise cumulative load curve. The proposed meta-heuristic seeks for the optimal configuration of series-parallel systems cells in which a multiple choice of cells are allowed from a list of different technologies are available in the market. Our approach has the advantage to allow cells with different technologies to be allocated in parallel-series. To allow fast reliability estimation, a universal generating function method is applied.

 Not chosen  

 29.5.2008 

This article is devoted to the simple PI and PID analog and digital controller tuning for non-oscillatory proportional plants of the first and second order with time delay. The approach is a modification of the desired model method and it uses for fine-tune up the only one varying parameter – the controller gain. New identification method is described as well. It enables the acquisition of plant transfer functions in a form suitable for mentioned tuning. All resultant formulas are brought out in easily memorizable forms. The use is shown in the example.