System Dynamics and Applied Control Laboratory
Road-Noise Active Noise control (RANC)
There are various types of noise generated by automobiles. Typical examples are noise from the engine and drivetrain, wind noise caused by air resistance as the vehicle is driving, and road noise generated when tires come into contact with the road surface. To reduce the noise, passive noise control technologies such as dynamic dampers are used to reduce the vibration at the source.
However, it is difficult to completely eliminate the transmission due to the characteristics of the road noise, which mainly occurs in the low frequency band below 500Hz due to the increased body weight.
In order to improve the quietness of the interior of the car, Road-Noise Active Noise Control (RANC) was introduced.
In order to improve the quietness of the interior of the car, Road-Noise Active Noise Control (RANC) was introduced.
Engine Sound Tracking Active Noise Control
With engine downsizing, powertrain electrification, and variable driving mode, engine sound can be different within a single engine. Generating company’s signature engine sound regardless of engine property is needed. Engine sound needs to be tunable with the driver’s preference. Therefore, Engin Active Noise Control (EANC) & Active Sound Design (ASD) have been researched to reduce engine noise or generate a desired sound.
Road Classification for Road Active Noise Control
In the case of road noise, different characteristics are shown depending on the road. When the road changes while driving, the control filter that matches it also changes. The control filter changes gradually according to the change of the road, but it takes a certain time and the control performance decreases during that time.
On the other hand, the convergence speed of the control filter can be increased by detecting a road change and using an appropriate control filter for the corresponding road. Therefore, a method for classifying roads based on driving data for each road is being studied.
OPTIMAL SENSOR AND ACTUATOR CONFIGURATION FOR ACTIVE NOISE CONTROL SYSTEM
Sensor and actuator configuration: Major effect on the performance of controller and observer. How to determine the optimal configuration of actuators and sensors: error or input energy. In the traditional control problem, controllability or degree of controllability based on input energy can be used for optimal configuration. However, the distributed parameter system such as ANC system, previous definition can not be applied. Therefore, new performance measure that can determine sensor and actuator configuration for ANC system has been researched.
Example) Find the best configuration (sensors) for ANC system.
Circular hybrid noise control system using theoretically calculated control filter
This finite-sized noise control system is consisted circular noise barrier and control speaker for Active Noise Control (ANC). By using the two noise reduction methods together, the control frequency band can be expanded.
Since the theoretically calculated control filter is used, microphones are not required in the control space. Also, it is possible to easily respond to changes in the control space or the location of the noise source. It is expected as a way to reduce the noise of resting or working spaces in industrial sites.
Vision-based Modal Analysis for Hyper-Nyquist Frequency Range
Vision-based vibration measurement has the great advantage that the displacement response of a whole surface, not a point or a line, can be measured at one time. However, the measurement using a camera has a problem in that the sampling rate is insufficient as compared with the conventional contact type sensor. For the experimental modal analysis, we propose a new measurement method using features that the experimental designer can select and use the input signal of the excitation part. The input signal phase can be obtained in advance through the Hilbert transform process for the excitation input signal. Based on this phase signal, a specific trigger signal can be designed to take samples at the desired instantaneous phase. All data acquisition process for the modal testing is operated with this trigger signal to avoid aliasing issues. The proposed method was implemented in hardware using an FPGA (field-programmable gate array) board, and 3D displacement information was extracted from a set of images acquired using the proposed measurement method using a commercial tool 3D-DIC.
