Data-driven predictive functional control of power kites for high altitude wind energy generation

The power kite is a kind of high altitude wind energy (HAWE), which is a still untapped source of renewable energy and has received an increasing attention in the last decade. Automatic control of power kites is a key aspect of HAWE generators and it is a complex issue, since the system at hand is open-loop unstable, difficult to model and subject to significant external disturbances. In order to deal with this issue, a new kind of adaptive predictive functional controller (APFC) is presented in this paper. With subspace identification for predictive model of kites, the maximum generation controller is designed to control kites using PFC principles. The APFC, which is a combination of on-line identification, learning mechanism and predictive controller, is presented to solve the nonlinear real-time receding horizon optimization. The stability of control system is guaranteed by closed-loop subspace identification. The implementation of closed loop control system is given, and the proposed APFC approach for kite control results to be quite effective, as it is shown via numerical simulation tests.