[2024-1학기 국민대학교 자동차융합실험] MyRio QUBE-Servo 활용 자동제어 실험(A+)

문서 내 토픽

1. Second order system

실험을 통해 2차 시스템의 폐루프 전달함수, 감쇠비, 고유진동수를 구하고 이론값과 실험값을 비교하였다. 이를 통해 모델링의 단순화, 시스템의 비선형성, 마찰 및 공기 저항, 외란 발생, 계측 장치의 문제 등이 오차 발생의 원인임을 확인하였다.
2. PD control

P 게인과 D 게인의 변화에 따른 시스템 응답 특성을 분석하였다. P 게인이 증가할수록 시스템이 에러에 민감하게 반응하여 상승시간이 감소하지만 오버슈팅이 증가하였다. D 게인이 증가할수록 오버슈팅이 감소하고 정착시간이 줄어들었다.
3. Balance control

Pendulum 각도를 수직으로 유지하면서 arm 각도를 제어하는 Balance control 실험을 수행하였다. PD control 시스템과 비교하여 Balance control 시스템은 출력에 잔진동이 많고, 입력 후 초기에 반대 방향으로 움직이는 등의 차이가 있었다. 이는 Pendulum의 관성과 회전축에 대한 질량 분포의 비대칭성 때문인 것으로 분석되었다.

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1. Second order system

Second order systems are an important class of dynamic systems that are widely used in various engineering applications. These systems are characterized by a second-order differential equation, which describes the relationship between the input and output variables. The behavior of a second-order system is determined by its natural frequency and damping ratio, which govern the system's response to input signals. Understanding the properties and analysis of second-order systems is crucial for designing and optimizing control systems, mechanical systems, and other engineering applications. By studying the transient and steady-state responses, as well as the stability and performance characteristics of second-order systems, engineers can develop effective solutions to a wide range of problems, from control of mechanical systems to the design of electronic circuits. The analysis of second-order systems provides a solid foundation for understanding more complex dynamic systems and is an essential topic in the field of control theory and system dynamics.
2. PD control

PD (Proportional-Derivative) control is a fundamental control algorithm that is widely used in various engineering applications, such as robotics, process control, and automotive systems. The PD controller combines a proportional term, which is proportional to the error between the desired and actual values, and a derivative term, which is proportional to the rate of change of the error. This combination of proportional and derivative actions allows the PD controller to provide both stabilizing and damping effects, making it effective in improving the transient response and reducing the steady-state error of a system. The PD controller is relatively simple to implement and can be tuned to achieve the desired system performance. However, it is important to carefully select the appropriate proportional and derivative gains to ensure stability and optimal performance. PD control is often used as a starting point for more advanced control strategies, such as PID (Proportional-Integral-Derivative) control, and its understanding is crucial for the design and implementation of effective control systems in a wide range of applications.
3. Balance control

Balance control is a critical aspect of many engineering systems, particularly in the field of robotics and mechatronics. Maintaining balance is essential for the stable and efficient operation of various devices, from bipedal robots to self-balancing vehicles. Balance control involves the use of sensors, actuators, and control algorithms to continuously monitor and adjust the system's position and orientation to counteract the effects of external disturbances and maintain a desired equilibrium state. This requires the integration of various disciplines, such as control theory, dynamics, and sensor technology. Effective balance control strategies often employ advanced techniques, such as state estimation, feedback control, and optimization algorithms, to ensure robust and reliable performance. The development of balance control systems has been a significant challenge in the field of robotics and has led to numerous advancements in areas like humanoid robotics, self-balancing scooters, and other dynamic systems. Mastering the principles of balance control is crucial for the design and implementation of innovative and practical engineering solutions that can operate in complex and challenging environments.