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The purpose of this paper is to develop and compare conventional and neural network-based controllers for gas turbines.

Design of two different controllers is considered. These controllers consist of a NARMA-L2 which is an artificial neural network-based nonlinear autoregressive moving average (NARMA) controller with feedback linearization, and a conventional proportional-integrator-derivative (PID) controller for a low-power aero gas turbine. They are briefly described and their parameters are adjusted and tuned in Simulink-MATLAB environment according to the requirement of the gas turbine system and the control objectives. For this purpose, Simulink and neural network-based modelling is used. Performances of the controllers are explored and compared on the base of design criteria and performance indices.

It is shown that NARMA-L2, as a neural network-based controller, has a superior performance to PID controller.

This study aims at using artificial intelligence in gas turbine control systems.

This paper provides a novel methodology for control of gas turbines.

The system proposed in this paper is the Alicia³ robot, which is based on the Alicia II module. Its aim is to inspect non‐porous vertical walls like those of aboveground petrochemical tanks, with a wide range of surface materials and cleanliness levels. To meet this aim, pneumatic‐like adhesion has been selected for the system. The system is also required to move over the surface at a suitable speed, to pass over obstacles and to have a suitable payload to carry mission‐specific instrumentation. The robot design mainly aimed at finding a solution with a high degree of modularity, so that it can easily be disassembled for maintenance purposes and to replace consumable parts such as the wheels and the sealing, making its design easier. Some onboard control algorithms have also been introduced to increase system reliability and reduce energy consumption.