In this paper, aspects of analytical design of PID controllers are studied, by combining pole placement technique with symmetrical optimum criterion. The proposed method is based on low-order plant model with pure integrator, and it can be used for both fast and slow processes. Starting from the desired closed-loop transfer function, which contains a second-order oscillating system and a lead-lag compensator, it is shown that the zero value depends on the real-pole value of closed-loop transfer function. In addition, there is only one pole value, which satisfies the assumptions of symmetrical optimum criterion imposed to open-loop transfer function. In these conditions, by combining the pole placement technique with symmetrical optimum criterion, the analytical expressions of the controller parameters can be simplified. For simulations, PID autopilot design for heading control problem of a conventional ship is considered.

The sound speed is affected by changes in the air properties, such as temperature, pressure, humidity, gas composition, and air turbulence. It has direct influence on the accuracy of distance measuring and the sound wavelength. For many indoor applications, the sound speed in air is considered as constant. For outdoor environments, a constant sound speed generates important errors of distance estimation. Also, for low temperature values, the real distance is smaller than measured distance, and this can be a dangerous situation. The pressure influence on sound speed can be neglected, and the sound speed depends on temperature and relative humidity. In this paper, variations in the speed of sound as a function of air properties are studied, along with their influence on the accuracy of ultrasonic sensing. In addition, fuzzy rules are generated for sound speed values used in outdoor applications.

Time delay estimation (TDE) is an important problem in many fields of the engineering. The work is looking from the sonar based applications point of view, where the time difference between transmitted pulse and received echo is important for target and object detection and recognition. From many available well know methods, only three time domain methods were considered and investigated: Direct Correlation Function (DCF), Average Square Difference Function (ASDF), and Average Magnitude Difference Function (AMDF). Results obtained for TDE are compared by simulation. We investigate the resolutions of the methods depending on SNR. All investigate methods are valid down to 0 dB Signal to Noise Ratio (SNR). DCF algorithms seems to have the best behavior, in the sense that they have the constant estimations over a wide range of SNR. The ASDF and AMDF based methods needs an estimation of the range where the delay could be. All estimations are biased and slightly depend on the value of the time delay.

Many oil‐based high‐voltage circuit breakers are still in use in national power networks of developing countries, like those in Eastern Europe. Changing these breakers with new more reliable ones is not an easy task, due to their implementing costs. The acting device, called oleo‐pneumatic mechanism (MOP), presents the highest fault rate from all components of circuit breaker. Therefore, online predictive diagnosis and early detection of the MOP fault tendencies are very important for their good functioning state. In this paper, fuzzy logic approach is used for the diagnosis of MOP‐type drive mechanisms. Expert rules are generated to estimate the MOP functioning state, and a fuzzy system is proposed for predictive diagnosis. The fuzzy inputs give information about the number of starts and time of functioning per hour, in terms of short‐term components, and their mean values. Several fuzzy systems were generated, using different sets of membership functions and rule bases, and their output performances are studied. Simulation results are presented based on an input data set, which contains hourly records of operating points for a time horizon of five years. The fuzzy systems work well, making an early detection of the MOP fault tendencies.

In general, conventional ships have mono-variable autopilot, which controls the heading of the ship affected by perturbations, in course-keeping or course-changing maneuvers. It takes into account only the yaw angle and ignores the roll movements. Intelligent control systems can be designed, considering the ship as 2 DOF underactuated system, with yaw angle and roll angle as outputs, and rudder as control input. A neuro-fuzzy system is proposed, which modifies the autopilot commands, so that roll damping effects and heading performance to be obtained simultaneously, with small rudder angle and rudder rate values. The system can be used with any conventional autopilot with slow steering machine.