Young Soo Suh

Technical communique: Stability and stabilization of nonuniform sampling systems

This paper is concerned with nonuniform sampling systems, where the sampling interval is time-varying within a certain known bound. The system is transformed into a time-varying discrete time system, where time-varying parts due to the sampling interval variation are treated as norm bounded uncertainties using robust control techniques. To reduce conservatism arising from modeling time-varying parts as a single uncertainty, the time-varying parts are modeled as N uncertainties. With larger N, a less conservative stability condition is derived at sacrifice of more computation. It is shown through a numerical example that the proposed stability condition is better than existing stability conditions.

Orientation Estimation Using a Quaternion-Based Indirect Kalman Filter With Adaptive Estimation of External Acceleration

This paper is concerned with orientation estimation using inertial and magnetic sensors. A quaternion-based indirect Kalman filter structure is used. The magnetic sensor output is only used for yaw angle estimation using two-step measurement updates. External acceleration is estimated from the residual of the filter and compensated by increasing the measurement noise covariance. Using the direction information of external information, the proposed method prevents unnecessarily increasing the measurement noise covariance corresponding to the accelerometer output, which is not affected by external acceleration. Through numerical examples, the proposed method is verified.

A zero velocity detection algorithm using inertial sensors for pedestrian navigation systems.

In pedestrian navigation systems, the position of a pedestrian is computed using an inertial navigation algorithm. In the algorithm, the zero velocity updating plays an important role, where zero velocity intervals are detected and the velocity error is reset. To use the zero velocity updating, it is necessary to detect zero velocity intervals reliably. A new zero detection algorithm is proposed in the paper, where only one gyroscope value is used. A Markov model is constructed using segmentation of gyroscope outputs instead of using gyroscope outputs directly, which makes the zero velocity detection more reliable.

Brief paper: Modified Kalman filter for networked monitoring systems employing a send-on-delta method

In this paper, we consider a networked estimation problem in which sensor data are transmitted only if their values change more than the specified value. When this send-on-delta method is used, no sensor data transmission implies that the sensor value does not change more than the specified value from the previously transmitted sensor value. Using this implicit information, we propose a modified Kalman filter algorithm. The proposed filter reduces sensor data traffic with relatively small estimation performance degradation. Through experiments, we demonstrate the feasibility of the proposed filter algorithm.

Pedestrian inertial navigation with gait phase detection assisted zero velocity updating

An inertial navigation system for pedestrian position tracking is proposed, where the position is computed using inertial and magnetic sensors on shoes. Using the fact that there is a zero velocity interval in each stride, estimation errors are reduced. When implementing this zero velocity updating algorithm, it is important to know when is the zero velocity interval. The gait states are modeled as a Markov process and gait state is estimated using the hidden Markov model filter. With this gait estimation, the zero velocity interval is more accurately estimated, which helps to reduce the position estimation error.

Improving Estimation Performance in Networked Control Systems Applying the Send-on-delta Transmission Method

This paper is concerned with improving performance of a state estimation problem over a network in which a send-on-delta (SOD) transmission method is used. The SOD method requires that a sensor node transmit data to the estimator node only if its measurement value changes more than a given specified δ value. This method has been explored and applied by researchers because of its efficiency in the network bandwidth improvement. However, when this method is used, it is not ensured that the estimator node receives data from the sensor nodes regularly at every estimation period. Therefore, we propose a method to reduce estimation error in case of no sensor data reception. When the estimator node does not receive data from the sensor node, the sensor value is known to be in a (−δi,+δi) interval from the last transmitted sensor value. This implicit information has been used to improve estimation performance in previous studies. The main contribution of this paper is to propose an algorithm, where the sensor value interval is reduced to (−δi/2,+δi/2) in certain situations. Thus, the proposed algorithm improves the overall estimation performance without any changes in the send-on-delta algorithms of the sensor nodes. Through numerical simulations, we demonstrate the feasibility and the usefulness of the proposed method.

Attitude Estimation by Multiple-Mode Kalman Filters

This letter proposes a multiple-mode Kalman filter for one-dimensional attitude estimation using low-cost accelerometer and gyroscope. The nonlinearity and time-varying parameters are partitioned into several modes; for each mode, a linear time-invariant Kalman filter is selected. Experimental results are given to verify the proposed Kalman filter

An online self-gain tuning method using neural networks for nonlinear PD computed torque controller of a 2-dof parallel manipulator

Abstract Parallel manipulators have advantages like high accuracy, high stiffness, high payload capability, low moving inertia, and so on. In this paper, a detailed study to apply an online self gain tuning method using neural networks for nonlinear PD computed torque controller to a 2-dof parallel manipulator is presented. A novel nonlinear PD computed torque controller is achieved by combining conventional computed torque controller and auto tuning method using neural networks which has advantages such as flexibility, adaptation and learning ability. The proposed controller has a simple structure and little computation time while securing good performance in tracking trajectories of parallel manipulators. To verify the control performance, various simulations of a 2-dof parallel manipulator are conducted. Simulation results show the effectiveness of the proposed method in comparison with the conventional computed torque controller.

Golf Swing Motion Tracking Using Inertial Sensors and a Stereo Camera

A golf swing motion tracking algorithm is proposed in which the golf club trajectory (position and velocity) and club face orientation information are given. Two sensors are used in the algorithm: an inertial sensor unit on the golf club, and a stereo camera that captures infrared light emitting diodes (LEDs), also on the golf club. During the address and impact golf swing phases, the camera captures the infrared LEDs on the golf club. Using the infrared LEDs as landmarks, the position and orientation of the golf club are computed. When the golf club is moving, an inertial navigation algorithm is used to compute the golf club trajectory. An indirect Kalman filter with nine states is used to combine the inertial sensor and vision data. The average position accuracy is about 3.6 cm and the maximum error is about 13.2 cm. The proposed system can be used to analyze golf swings quantitatively.

Inertial Sensor-Based Two Feet Motion Tracking for Gait Analysis

Two feet motion is estimated for gait analysis. An inertial sensor is attached on each shoe and an inertial navigation algorithm is used to estimate the movement of both feet. To correct inter-shoe position error, a camera is installed on the right shoe and infrared LEDs are installed on the left shoe. The proposed system gives key gait analysis parameters such as step length, stride length, foot angle and walking speed. Also it gives three dimensional trajectories of two feet for gait analysis.