Efficiency and performance of embedded model predictive control for active vibration attenuation

The development of efficient solution approaches and technological advances facilitate the use of predictive control on embedded systems, even for fast systems or on computationally limited hardware platforms. The practical implementation of predictive control is, however, still often very time consuming and demands insight into the formulation and solution strategies for predictive control. Tools for automatic code generation tailored for deployment on embedded systems promise to overcome these problems and thus enable the fast and reliable implementation of predictive control. This paper exploits the efficiency and performance of automatic code generation for linear model predictive control for the embedded active vibration control of a flexible mechanical structure. μAO-MPC is used to automatically generate C code, based on a high level problem description. The resulting code is used for real-time implementation on an embedded platform. The embedded model predictive controller efficiently computes the voltage input for a piezoceramic actuator based on state estimates from a Kalman filter. The performance, computational efficiency, memory requirements, task execution timing and other properties of practical interest are examined via experiments on the embedded controller for active vibration control.