Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.
The International Journal of Robust and Nonlinear Control aims to encourage the development of analysis and design techniques for uncertain linear and nonlinear systems. The main focus of the journal is on the theory and design of regulating and tracking systems, but related areas such as linear and nonlinear filtering, condition monitoring and fault estimation are included. The physical modelling, simulation and identification of systems that may be uncertain or nonlinear is of interest. Papers are also welcome in the area of multi-agent systems considering coordinated control problems. Papers dealing with the general problem of consensus and synchronization that fail to demonstrate an application and/or include significant novelty will not be considered.
Papers that demonstrate the potential for robust or nonlinear controllers in applications will also be welcome, but such papers must include sufficient novel material. The Journal provides a natural forum for papers on the theory and application of robust control design and estimation techniques, including H∞ or H2 design, multi-objective optimization, and variable structure and sliding mode control design methods. Papers will also be welcome on non-optimal methods of improving the robustness of uncertain systems, such as QFT design methods. Papers on linear and nonlinear model based predictive control algorithms are also encouraged, and those concerned with linear parameter varying, switched or hybrid systems.
All aspects of the theory and techniques used in nonlinear control and estimation are also included ranging from gain scheduling to networked robust or nonlinear control systems. The development of nonlinear compensation and design methods using feedback linearization, back-stepping, Lyapunov based techniques, learning control, cooperative control and agent based systems are all of interest. Contributions are also welcome on numerical algorithms for robust or constrained control, using for example linear matrix inequalities, and on the problems of implementation, such as controller tuning and commissioning. Although the journal focus is on model-based control, papers on model free and data driven methods, artificial intelligence and machine learning will also be welcome if they are relevant to the control design problems for engineering systems.