Noise suppression method for an AM radio receiver using digital signal processing
Recently, electromagnetic interference in AM radio receivers associated with noise generated from the power control unit (DC-DC converters/inverters) and other power electronics devices, such as an electric power steering (EPS) control modules, in hybrid vehicles (HVs) has become a serious problem. In order to address this problem, most noise suppression methods use either noise filters (for noise sources) or shield wiring and ferrite cores (for noise propagation paths). We have proposed a noise suppression method through digital signal processing in an AM radio receiver. In this method, the received AM radio signal containing HV noise is first quadrature demodulated. Next, a replica signal of the noise is generated using the noise signal in the quadrature component. The replica signal is then subtracted from the AM radio signal containing the noise of the in-phase component. In this paper, first, we show the noise canceling method. And then, we construct a prototype of the radio receiver system based on the method. Finally, we demonstrate that the system can reduce the DC-DC converter noise and the EPS noise superimposed at the same time on the AM radio signal by more than 15 dB.
Automotive electromagnetic compatibility (EMC)
Preface. 1: What is EMC. 1.1. Background. 1.2. Technology and EMC. 1.3. Communication Technology Evolution. 1.4. Convergence of Technology and Automotive Systems. 1.5. Future Trends. 2: System Level Issues. 2.1. Definition of Component and System. 2.2. Significance to EMC. 3: Power and Signal Return. 3.1. Introduction. 3.2. Current Path. 3.3. Safety Grounding. 3.4. Single Point Ground (Single Reference). 4: Basic Concepts Used in EMC. 4.1. Antennas. 4.2. Omni-Directional Antennas. 4.3. Other components in EMC. 4.4. Ideal and Actual Components. 4.5. Transmission Lines. 4.6. Shields. 4.7. Fourier series and Frequency Spectrum Envelope. 4.8. Capacitors, Inductors, and Actual Properties. 4.9. Filtering Overview. 4.10. Enclosure Shielding. 4.11. Shield Discontinuities. 5: Electromagnetic Fields. 5.1. Introduction. 5.2. Characteristics of the Electromagnetic Environment. 5.3. Comparison of Circuit Theory with EM Field Theory. 5.4. Maxwell's equations. 5.5. Regions Around a Source: 5.6. Polarization. 6: EMC Testing. 6.1. EMC Disciplines. 6.2. Radiated Emissions Diagnostics. 6.3. How a Switching Transient Occurs. 6.4. Test Methods. 6.5. Analysis of Results. 6.6. Coaxial Cables. 6.7. A 'Virtual' Tour of AN EMC Lab. 7: EMC Modeling. 7.1. The Value of EMC Modeling. 7.2. Emissions modeling. 7.3. Goal of modeling. 8: Effects of Cable and Harnessing. 8.1. Conducted emissions and immunity. 8.2. Auto Industry EMC Approaches. 8.3. Filter Placement. 8.4. Coupling between wires. 8.5. Grounding and PCB layout. 8.6. Ferrites. 8.7. Attenuating Common Mode Currents on Unshielded Cables. 8.8. Higher-Frequency Emissions. 9: Automobile Electrical/Electronics Systems. 9.1. Vehicle Generated Radiated Emissions. 9.2. Bandwidth Relates to 'Selectivity'. 9.3. Broadband Noise. 9.4. Narrowband noise. 9.5. Signal Characteristics. 9.6. RE Differences Between 'Identical' Components. 9.7. Vehicle radiated emissions test. 9.8. Summary. 9.9. Digital System Design. 9.10. Electromagnetic Environment. 9.11. EMC Issue: Immunity to External Fields. 9.12. Inexpensive shielding methods. 9.13. EMC Design for Immunity. 9.14. Immunity Threshold. 9.15. Auto Industry 'Best Practices'. 9.16. Ignition Systems. 10: EMC Rules and Regulations. 10.1. Introduction. 10.2. Radiated Emissions Requirements. 10.3. Governmental Requirements. 10.4. FCC Part 15. 10.5. 'Microvolts per Meter' and Watts. 11: Vehicle System Electrical Transients. 11.1. Background. 11.2. Overview of the Vehicle Transient Environment. 11.3. Component Selection. 11.4. Logic Families and dV/dt. 11.5. Logic Families and dI/dt. 11.6.
L1 Adaptive Controller for Attitude Control of Multirotors
In this work we show an application of L1 Adaptive control theory for attitude control of UAVs. We implement the flight control system on a multirotor to show robustness and precise attitude tracking in the presence of modeling uncertainties and environmental disturbances. We choose backstepping control architecture, since the kinematics and dynamics of multirotors in most cases can be written in strict feedback form. We further exploit the fact that the kinematics of the plant, while free of uncertainties, is nonlinear, which makes it highly suitable for dynamic inversion control at each level of backstepping. On the other hand, plant dynamics is uncertain and is affected by environmental disturbances such as wind gusts, unmodeled dynamics etc. Therefore, we consider 3 variants of the control architecture. The first method uses backstepping to determine the moment demand and augments it with the L1 adaptive controller to account for uncertainties and provide robustness with guaranteed transient performance. The second architecture apply the concept of L1 adaptive backstepping to the same problem; and the third architecture uses L1 backstepping for quaternion representation of the system dynamics, which helps to avoid the singularities associated with Euler angles.