Adaptive neurofuzzy H-infinity control of DC–DC voltage converters

A novel adaptive neurofuzzy H-infinity control approach to feedback control of DC–DC converters, being used in applications of renewable energy generation, is developed. The form and the parameters of the differential equations that constitute the dynamic model of the controlled system are considered to be unknown, while there is only knowledge about the order of the system. The model of the controlled system undergoes approximate linearization around a temporary operating point which is re-computed at each iteration of the control algorithm. The linearization procedure makes use of Taylor series expansion and the computation of Jacobian matrices. For the approximately linearized model of the DC–DC converter, it is possible to design a stabilizing H-infinity feedback controller, provided that knowledge about the matrices of the linearized state-space description is available. In such a case, the computation of the feedback controller’s gain comes from the solution of an algebraic Riccati equation taking also place at each iteration of the control method. However, since the elements of these matrices are unknown, it is proposed to estimate them with the use of neurofuzzy approximators. Actually, neurofuzzy networks are employed for learning the constituent functions of the system’s dynamic model. Based on these function estimates, the system’s Jacobian matrices are also obtained and this allows the implementation of the H-infinity feedback controller. To assure the stability of the control loop, the learning rate of the neurofuzzy approximators is chosen from the requirement that the first derivative of the system’s Lyapunov function to be always a negative one. The global asymptotic stability and the robustness properties of the control method are proven through Lyapunov stability analysis.