Robust Kernel-Based Model Reference Adaptive Control for Unstable Aircraft

In this chapter, a robust kernel-based model reference adaptive control (KMRAC) is proposed for an unstable nonlinear aircraft. The heart of the proposed KMRAC scheme is comprised of an offline neural identifier and an online neural controller. In the offline neural identifier, the kernel-based unified extreme learning machine (U-ELM) algorithm is used to identify the aircraft model with the available input-output data in a finite time interval. The finite time interval is selected to avoid the response of the unstable aircraft growing unbounded. In the kernel-based U-ELM, the hidden layer feature mapping is determined by the kernel matrix. However, the U-ELM is a batch learning algorithm and is not suitable to the online control learning. To solve the problem, a recursive version of the U-ELM (RU-ELM) is developed in the study. Based on a given reference model and the identified model, the RU-ELM algorithm is applied to construct the online control law to compensate for the changes in the aircraft dynamics or characteristics. The performance of the proposed KMRAC scheme is validated through the simulation studies of a locally nonlinear longitudinal high-performance aircraft. Simulation studies are also compared with a MRAC based on the Back-Propagation algorithm and a MRAC based on the basic ELM algorithm in terms of the identification and tracking abilities. The results show that the proposed KMRAC can achieve better identification and tracking performance.