LQ/MPC/Model Predictive Control is a control strategy used in engineering and science to optimize the performance of a control system. It involves using mathematical models and optimization techniques to predict the future behavior of a system and adjust the control inputs accordingly to achieve desired performance.
LQ/MPC/Model Predictive Control works by using a mathematical model of the system, along with measurements of the current state, to predict the future behavior of the system. Based on this prediction, the control inputs are adjusted to achieve the desired performance. This process is repeated in a feedback loop to continuously optimize the system's performance.
Some advantages of LQ/MPC/Model Predictive Control include its ability to handle constraints and non-linear systems, its adaptability to changing conditions, and its ability to account for uncertainty in the system. It can also provide better performance compared to traditional control strategies, especially for complex systems.
An example of LQ/MPC/Model Predictive Control in action is the control of a heating, ventilation, and air conditioning (HVAC) system in a building. The control system uses a mathematical model of the building's thermal dynamics, along with temperature and occupancy measurements, to predict future conditions and adjust the HVAC system's inputs to maintain a comfortable and energy-efficient environment.
LQ/MPC/Model Predictive Control is used in various industries and applications, including process control in chemical plants, automotive and aerospace control systems, robotics, and industrial automation. It is also used in energy management systems, traffic control systems, and even in the control of sports equipment, such as Formula 1 racing cars.