The International Journal of Robust and Nonlinear Control aims to encourage the development of analysis and design techniques for uncertain systems and for systems that are nonlinear. The basic physical modeling and identification of systems that may be uncertain, or include significant nonlinearities, and the simplification of models for subsequent control design are of interest. The main focus of the journal is on the theory and design of control systems but related areas such as nonlinear filtering, fault estimation, controller tuning and implementation are all covered. Papers will be particularly welcome that demonstrate the potential for nonlinear or robust controllers in applications through either design studies or real applications experience.

The Journal provides a natural forum for papers on the theory and application of robust control design techniques, including H∞ or H ₂ design and Multi-objective Optimization methods. Papers on the robust control design of Model Predictive Controllers and algorithms for constrained optimization are encouraged. Contributions on numerical algorithms for robust control, using for example Linear Matrix Inequalities , and the problems of real time implementation are also of interest.

All aspects of the theory and techniques used in nonlinear control and estimation are included ranging from Gain Scheduling to Nonlinear Predictive Control . This includes the development of nonlinear compensation and design methods, using Feedback Linearization, Lyapunov based techniques and Nonlinear H∞ design. Control techniques based on Heuristic or rule based design methods for uncertain or nonlinear systems will be considered, together with procedures based on adaptive back-stepping or iterative learning control. Papers will also be welcome on methods of improving the robustness of uncertain or parameter varying systems using for example QFT design methods. Papers on the control of distributed parameter systems are welcome, covering the main types of partial differential equations and concentrating on robustness and/or nonlinear issues.