| Nonlinear dynamical systems face numerous challenges that need to be addressed such as, severe nonlinearities, varying operating conditions, structured and unstructured dynamical uncertainties, and external disturbances. In spite of the recent advances in the area of nonlinear control systems, conventional control techniques depend heavily on precise mathematical system models to provide satisfactory performance In real life, and due to high nonlinearities, deriving a precise model could be a difficult undertaking. Although conventional nonlinear control strategies, such as adaptive and sliding mode controllers, compensate for parametric uncertainties, they are still vulnerable in the presence of unstructured modeling uncertainties. On the other hand, computational intelligence based controllers do not have such a limitation, thanks to their mathematical model dependence free characteristic. Despite of the recent results, neural network-based controllers remain incapable of incorporating any human-like expertise and fuzzy logic-based controllers are unable to incorporate any learning already acquired about the dynamics of the system in hand.;Driven by the aforementioned motivation, this thesis is meant to contribute to the latest developments and merits of such tools by novel adaptive control methodologies developments. The proposed controllers assume uncertain/unknown systems dynamics to achieve robustness to both structured and unstructured uncertainties of higher and different magnitudes. Conventional control structures offer poor performance in the presence of these kinds of uncertainties. Unlike these approaches, the proposed controllers are based on soft-computing tools, which do not have such limitations, thanks to their learning and generalization capabilities. However, these tools are often based on heuristics and tuning may not be trivial. Furthermore, many soft-computing based controllers lack stability proofs in various control applications. In this thesis, the proposed control architectures are designed using Lyapunov-based adaptation techniques instead of conventional heuristic tuning methods. Thus, the stability of the proposed controllers is guaranteed unlike many computational intelligence-based control schemes. |
