Vladimir Bugarski

Fuzzy decision support system for ship lock control

This paper presents the development of a Decision Support System (DSS) for the management of ship locks that relies on fuzzy logic. It contains a brief overview of the history and the construction of locks and basic information related to fuzzy logic, fuzzy linguistic variables and methods used in approximate reasoning. In reality, ship lock control is mostly based on the subjective estimations and the experience of a lock master (ship lock operator). The fuzzy set theory is the most favourable mathematical approach for consideration of indefiniteness and subjective estimates. This paper analyses the control process of a ship lock on a two-way waterway, with one chamber designed for one vessel. A control algorithm is constructed according to a set of linguistic rules that describes the operators control strategy. The subjective estimations are therefore implemented in the algorithm as fuzzy sets. Fuzzy rules aggregate the final fuzzy set and defuzzification produces a decision. A set of ship traffic data is generated for analysis and simulation purposes based on the annual distribution of ship arrivals at the lock. Two criteria are presented and used in parallel with the Fuzzy DSS (FDSS). These two extreme criteria reflect the interests of shippers on one side and workers and owners of the lock on the other side. These interests occur in actual systems and are used here to evaluate the results obtained using the FDSS. This paper additionally describes the design of the SCADA (Supervisory Control And Data Acquisition) software. This software relies on a PLC (Programmable Logic Controller) and provides a platform on which to implement the desired fuzzy algorithm. The software was developed with the suggestions of operators who have extensive experience in ship lock control. The presented control system can be used for support in decision making in control processes and in the training of new operators of ship locks.

Design of support vector machine classifier for broken bar detection

This paper proposes method for broken bar detection in induction motors at very low slip. The proposed method consists of extracting reliable discriminative feature from a steady state one-phase current signal and design of optimal classifier via a support vector machine. The fault related features are extracted from frequency spectra of a modulus of a motor phase current Hilbert transform series. The features are fed to the support vector machine input and the output indicates rotor condition in respect of broken bar appearance independently of a slip value. Tests are conducted on 1.1kW two poles induction machine in an industrial environment. It is shown that proposed method is accurate, fast, reliable, not hardware costly.

Realization of control of pneumatic system for positioning of nozzle based on fuzzy logic

This paper presents control of nozzle position in three dimension space using pneumatic muscles as actuators. Adequate fuzzy algorithm is designed using Matlab fuzzy inference system and implemented using PLC (programmable logic controller) unit and proportional pressure regulators. Algorithm is tested on real model.

Application of Nature-Inspired Optimization Techniques in Vessel Traffic Control

This chapter aims to present the analysis and comparison of some well-known nature-inspired global optimization techniques applied to an expert system controlling a ship locking process. A ship lock zone represents a specific area on waterway, and control of the ship lockage process requires a comprehensive approach. The initially proposed Fuzzy Expert System (FES) was developed using suggestions obtained from lockmasters (ship lock operators) with extensive experience. Further optimization of the membership function parameters of the input variables was performed to achieve better results in the local distribution of vessel arrivals. The purpose of the analysis and comparison is to find the best algorithm for optimization of membership functions parameters of FES for the ship lock control. The initially proposed FES is optimized (fine-tuned) with three global optimization algorithms from the group of evolutionary and swarm intelligence algorithms, in order to achieve the best value of the economic criterion defined as a linear combination of two opposed criteria: minimal average waiting time per vessel and minimal number of empty lockages (lockages without a vessel in a chamber). Besides the well known and widely applied Genetic Algorithm (GA), two relatively new but very promising global optimization techniques were used: Particle Swarm Optimization (PSO), the technique based on behavior of animals living in swarms and Artificial Bee Colony (ABC) algorithm, inspired by social organization of honey bees. Although all these algorithms have been widely applied and showed a great potential in engineering applications in general, their application in ship lock control and similar transportation problems is not so common. However, this chapter will present that all three algorithms may obtain the significant improvement of the adopted economic criterion value and succeed to find its (possibly global) optimum. Furthermore, the performances of these algorithms in FES parameters optimization are compared and some conclusions are adopted on their applicability, efficiency, and effectiveness in similar systems. The developed fuzzy algorithm is a rare application of artificial intelligence in navigable canals and significantly improves the performance of the ship lockage process. This adaptable FES is designed to be used as a support in decision-making processes or for the direct control of ship lock operations.