Jaho Seo

Feedback linearization based control of a rotational hydraulic drive

Abstract The technique of feedback linearization is used to design controllers for displacement, velocity and differential pressure control of a rotational hydraulic drive. The controllers, which take into account the square-root nonlinearity in the systems dynamics, are implemented on an experimental test-bench and results of performance evaluation tests are presented. The objective of this research is two-fold: firstly, to present a unified method for tracking control of displacement, velocity and differential pressure; and secondly, to experimentally address the issue of whether the system can be modeled with sufficient accuracy to effectively cancel out the nonlinearities in a real-world system.

An investigation on steering optimization for minimum turning radius of multi-axle crane based on MPC algorithm

The study investigates on the optimized steering wheel angle for minimum turning radius of all-terrain crane by applying a model predictive control (MPC) strategy. For this, the simplified linear bicycle model and error dynamic model are firstly derived for the crane with a multi-axle steering system. Then, MPC controller is designed with an optimal objective function to minimize the vehicle dynamic error for minimum turning radius. The minimum turning radius and corresponding optimized steering angle at different vehicle speeds are analyzed in the MATLAB/Simulink environment.

Implementation of Position and Force Control by Modelling of a Miniatured Excavator

This paper presents the implementation and control of a small-scaled excavator system. The commercial miniature of an excavator system has been modified and its control hardware is embedded to access the feedback control. Encoder sensors are attached to the joint and a force sensor is mounted on the end-effector so that feedback position control is accessible as well as force control. The dynamic model of the excavator system is derived as a four linkage robot arm and its control performances are simulated. Experimental studies of contact force control tasks are conducted to test the control algorithm for the excavator system.

Thermal dynamic modeling and control of injection moulding process

Thermal control of a mould is the key in the development of high efficiency injection moulds. For an effective thermal management system, this research provides a strategy to identify the thermal dynamic model for the design of a controller. Using neural networks and finite element analysis, system identification is carried out to deal with various cycle-times for moulding process and uncertain dynamics of the mould. Based on the system identification, a selfadaptive PID controller with radial basis function (RBF) is designed to tune controller parameters. The controllers performance is studied in terms of tracking accuracy under different moulding processes.

Identification of Die Thermal Dynamics Using Neural Networks

The objective of this research is to identify a dynamic model that describes the temperature distribution in a die with uncertain dynamics using a neural network (NN) approach. By using data sets obtained from a finite element analysis (FEA) of the thermal dynamics of a die and applying NN off-line and on-line learning algorithms, the die model is identified. This identification approach has been conducted assuming fully measurable and partially measurable states. For the latter, a NN based adaptive observer is employed to estimate unmeasurable states. It is shown that the complex behavior of the die system with cooling channels can be accurately identified in both cases of fully and partially measurable states.

Optimal Sensor Location to Estimate Temperature Distribution in an Injection Mould

The objective of this research is to identify optimal sensor locations to estimate temperature distribution in an injection mould using finite element analysis. Potential locations (referred to as target nodes) are grouped based on the similarity of their thermal response using a proposed temperature-ratio clustering method. A sensitivity analysis of the temperature distribution for these groups of target nodes identifies the sensor location for each cluster that exhibits the highest sensitivity to variable inputs. Using identified sensor locations with a neural network model, the accuracy in estimation of temperature response is evaluated.Copyright

Modeling of Thermal Dynamics of Dies Using Neural Networks

The objective of this research is to identify a dynamic model that describes the temperature distribution in a die using a neural network (NN) approach. By using data sets obtained from a finite element analysis (FEA) of the thermal dynamics of a die and applying NN off-line and on-line learning algorithms, the die model is identified. This identification approach has been conducted assuming fully measurable and partially measurable states. For the latter, a NN based adaptive observer is employed to estimate unmeasurable states. It is shown that the complex behavior of the die system with cooling channels can be accurately identified in both cases of fully and partially measurable states.Copyright