SUMMARYIn this paper, an adaptive simulated annealing genetic algorithm is proposed to solve generation expansion planning of Turkey's power system. Least-cost planning is a challenging optimization problem due to its large-scale, long-term, nonlinear, and discrete nature of power generation unit size. Genetic algorithms have been successfully applied during the past decade, but they show some limitations in large-scale problems. In this study, simulated annealing is used instead of mutation operator to improve the genetic algorithm. The improved algorithm is applied to the power generation system with seven types of generating units and a 20-year planning horizon. The planning horizon is divided into four equal periods. The new algorithm provides approximately 6.6 billion US$ (3.2%) cheaper solution than GA and also shows faster convergence.

A new microrobot manipulation technique with high precision (nano level) positional accuracy to move in a liquid environment with diamagnetic levitation is presented. Untethered manipulation of microrobots by means of externally applied magnetic forces has been emerging as a promising field of research, particularly due to its potential for medical and biological applications. The purpose of the presented method is to eliminate friction force between the surface of the substrate and microrobot. In an effort to achieve high accuracy motion, required magnetic force for the levitation of the microrobot was determined by finite element method (FEM) simulations in COMSOL (version 5.3, COMSOL Inc., Stockholm, Sweden) and verified by experimental results. According to position of the lifter magnet, the levitation height of the microrobot in the liquid was found analytically, and compared with the experimental results head-to-head. The stable working range of the microrobot is between 30 µm to 330 µm, and it was confirmed in both simulations and experimental results. It can follow the given trajectory with high accuracy (<1 µm error avg.) at varied speeds and levitation heights. Due to the nano-level positioning accuracy, desired locomotion can be achieved in pre-specified trajectories (sinusoidal or circular). During its locomotion, phase difference between lifter magnet and carrier magnet has been observed, and relation with drag force effect has been discussed. Without using strong electromagnets or bulky permanent magnets, our manipulation approach can move the microrobot in three dimensions in a liquid environment.

4-pole hybrid electromagnetic systems have a potential usage in many industrial areas, such as clean room design, transportation, semiconductor manufacturing due to providing mechanical contact-free operation with considerably low energy consumption. However, the main problem of magnetic levitation process: it has highly nonlinear nature and even if it can be linearized, it has unstable pole(s), which makes the system vulnerable in terms of stability. In this paper, to overcome the instability issue and track the desired references for each degree of freedom, a modified PID controller (so called I-PD) design technique based on coefficient diagram method (CDM) has been proposed. CDM is an algebraic design applied to polynomial structure of the system on the parameter space, where a specific diagram is used to present and interpret the essential data. It is quite simple to apply with a visual support, requires basic mathematical computations for field engineers, and offers a good equilibrium in terms of simplicity, stability, minimum overshoot and robustness, which are crucial specifications for maglev applications. The effectiveness and feasibility of CDM-based I-PD controller have been compared with CDMbased classical PID controller over an experimental setup .

In this paper, a motion planning system for a mobile robot is proposed. Path planning tries to find a feasible path for mobile robots to move from a starting node to a target node in an environment with obstacles. A genetic algorithm is used to generate an optimal path by taking the advantage of its strong optimization ability. Mobile robot, obstacle and target localizations are realized by means of camera and image processing. A graphical user interface (GUI) is designed for the motion planning system that allows the user to interact with the robot system and to observe the robot environment. All the software components of the system are written in MATLAB that provides to use non-predefined accessories rather than the robot firmware has, to avoid confusing in C++ libraries of robot's proprietary software, to control the robot in detail and not to re-compile the programs frequently in real-time dynamic operations

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