Gang-Gyoo Jin

Real-coded genetic algorithm benchmarked on noiseless black-box optimization testbed

Genetic algorithms--a class of stochastic population-based optimization techniques--have been widely realized as the effective tools to solve complicated optimization problems arising from the diverse application domains. Originally developed version was a genetic algorithm with the binary representation of candidate solutions (i.e. chromosomes), the real-coded versions are, however, basically superior and frequently utilized in tackling complex real-valued optimization tasks. In this paper, a real-coded genetic algorithm (RCGA), which employs an adaptive-range variant of the well-known non-uniform mutation, is furnished with a multiple independent restarts mechanism to benchmark the noise-free black-box optimization testbed. The maximum number of function evaluations for each run is set to 50000 times the search space dimension. For low search space dimensions, the algorithm shows encouraging results on several functions. Although the algorithm is unable to solve all the functions to the highest required accuracy, for each type of functions, some of them can be solved, especially to lower precision, with the dimension up to 40.

Fuzzy-based nonlinear PID controller and its application to CSTR

This study presents a new design method for a nonlinear variable-gain PID controller, the gains of which are described by a set of fuzzy rules. User-defined parameters are tuned using a genetic algorithm by minimizing the integral of absolute error and the weighted control input deviation index. It was observed in the experimental results on a continuous stirred tank reactor (CSTR) that the proposed controller provided performances: overshoot Mp≤1.25%, 2% settling time ts≤1.71 s and IAE≤1.26 for set-point tracking, perturbance peak Mpeak≤0.05%, 2% recovery time trcy≤3.97 s and IAE≤0.10 for disturbance rejection, and Mpeak≤0.04%, trcy≤2.74 s and IAE≤0.04 for parameter changes. Comparison with those of two other methods revealed that the proposed controller not only led to less overshoot and shorter settling time for set-point tracking and less perturbance peak and shorter recovery time for disturbance rejection, but also showed less sensitivity to parameter changes.

Traversability analysis for navigation of unmanned robots

In this study, a framework for navigation of unmanned robots by combining information extraction from terrain maps with regional traversability and speed assessment is presented. The proposed method extracts the slope and roughness of a terrain patch along four heading directions and then uses them to evaluate the level of difficulty associated with the traversal. The slope is estimated through curved surface fitting incorporating the least squares method. The roughness is obtained using the fractal-based analysis together with another two RMS metrics. As navigation systems can cope with imprecision and uncertainty of input data, we modify and extend the Serajipsilas fuzzy-based measures to assess the traversability and speed of each patch for path planning. The proposed method is tested on both fractal and real terrains to verify its effectiveness.

Stabilization and Tracking Algorithms of a Shipboard Satellite Antenna System

This paper presents the development of development of stabilization and tracking algorithms for a shipboard satellite antenna system. In order to stabilize the satellite antenna system designed in the previous work, a model for each control axis is derived and its parameters are estimated using a genetic algorithm, and the state feedback controller is designed based on the linearized model. Then a tracking algorithm is derived to overcome some drawbacks of the step tracking. The proposed algorithm searches for the best position using gradient-based formulae and signal intensities measured according to a search pattern. The effectiveness of both the stabilization and tracking algorithms is demonstrated through experiment using real-world data.

Enhancement of the Box-Counting Algorithm for fractal dimension estimation

Abstract The box-counting (BC) method is frequently used as a measure of irregularity and roughness of fractals with self-similarity property due to its simplicity and high reliability. It requires a proper choice of the number of box sizes, corresponding sizes, and size limits to guarantee the accuracy of the fractal dimension estimation. Most of the existing BC methods utilize the geometric-step method, which causes a lack of fitting data points and wasted pixels for images of large size and/or arbitrary size. This paper presents a BC algorithm in combination with a novel sampling method and fractional box-counting method which will allow us to overcome some of limitations evident in the conventional BC method. The new sampling method introduces a partial competition based on the coverage of box sizes and takes more number of box sizes than the geometric-step method. To circumvent the border problem occurring for images of arbitrary size, the fractional box-counting method allows the number of the boxes to be real, rather than integer. To show its feasibility, the proposed method is applied to a set of fractal images of exactly known fractal dimension.

Benchmarking real-coded genetic algorithm on noisy black-box optimization testbed

Originally, genetic algorithms were developed based on the binary representation of candidate solutions in which each conjectured solution is a fixed-length string of binary numbers; however, real-valued representation scheme is basically superior and frequently utilized in addressing hard optimization tasks, particularly for the optimization in continuous domains under a black-box scenario. In this paper, we implement a generational real-coded genetic algorithm (RCGA)--which is composed of tournament selection, arithmetical crossover, and adaptive-range mutation--with a multiple independent restarts mechanism and benchmark it on the BBOB-2010 noisy testbed. The maximum number of function evaluations for each run is set to 50000 times the search space dimension. For 40-dimensional search space, the algorithm shows promising results with 6 functions being solved up to the precision of 10-8.

Slope and Roughness Extraction Method from Terrain Elevation Maps

Recently, the interests in the development and application of unmaned robots are increasing in various fields including surveillance and reconnaissance, planet exploration, and disaster relief Unmaned robots are usually controlled from distance using radio communications but they should be equipped with an autonomous travelling function to cope with unexpected terrains and obstacles. This means that they should be able to evaluate terrain’s characteristics quantitatively using mounted sensors so as to traverse harsh natural terrains autonomously. For this purpose, this paper presents a method for extracting terrain information, that is, slope and roughness from elevation maps as a prior step of traversability analysis. Slope is extracted using the curve fitting based on the least squares method and roughness using three metrics and their weighted average. The effectiveness of the proposed method is verified on both a fractal map and the world model map of a real terrain.

PID Control of a Shell and Tube Heat Exchanger System Incorporating Feedforward Control and Anti-windup Techniques

PID control is widely used to control stable processes, however, PID control for unstable processes is less common. In this paper, systematic tuning methods are derived to tune the PID controller for unstable FOPTD(Forst Order Plus Time Delay) processes. The proposed PID controllers for set-point tracking and disturbance rejection problem are tuned based on minimizing the performance indexes (IAE, ISE, ITAE) using a real-coded genetic algorithm. Simulation example is given to illustrate the set-point tracking and disturbance rejection performance of the proposed method.

Development of a Traversability Map for Safe Navigation of Autonomous Mobile Robots

Abstract: This paper presents a method for developing a TM (Traversability Map) from a DTM (Digital Terrain Model) collected by remote sensors of autonomous mobile robots. Such a map can be used to plan traversable paths and estimate navigation speed quantitatively in real time for robots capable of performing autonomous tasks over rough terrain environments. The proposed method consists of three parts: a DTM partition module which divides the DTM into equally spaced patches, a terrain information module which extracts the slope and roughness of the partitioned patches using the curve fitting and the fractal-based triangular prism method, and a traversability analysis module which assesses traversability incorporating with extracted terrain information and fuzzy inference to construct a TM. The potential of the proposed method is validated via simulation works over a set of fractal DTMs.Keywords: autonomous mobile robot, digital terrain model, terrain information, traversability map Copyright© ICROS 2014

Elevation Restoration of Natural Terrains Using the Fractal Technique

In this paper, we presents an algorithm which restores lost data or increases resolution of a DTM(Digital terrain model) using fractal theory. Terrain information(fractal dimension and standard deviation) around the patch to be restored is extracted and then with this information and original data, the elevations of cells are interpolated using the random midpoint displacement method. The results of the proposed algorithm are compared with those of the bilinear and bicubic methods on a fractal terrain map.