Aims and scope

Mathematics of Control, Signals, and Systems (MCSS)  is an international journal devoted to mathematical control and system theory, including system theoretic aspects of signal processing.

Its unique feature is its focus on mathematical system theory; it concentrates on the mathematical theory of systems with inputs and/or outputs and dynamics that are typically described by deterministic or stochastic ordinary or partial differential equations, differential algebraic equations or difference equations.

Potential topics include, but are not limited to, controllability, observability, and realization theory, stability theory of nonlinear systems, optimal control, system identification, mathematical aspects of switched, hybrid, networked, and stochastic systems, and system theoretic aspects of controller design techniques.

The editorial policy of MCSS is to publish original and high quality research papers which contain a substantial mathematical contribution. Mathematically oriented survey papers on topics of exceptional interest to the systems and control community will also be considered.

Papers which merely apply known mathematical techniques, present algorithms without a mathematical analysis or only describe simulation studies are usually not published. MCSS publishes neither brief papers nor technical notes.


Authors of a paper wishing to enquire about the scope of the journal or the suitability of a particular topic are encouraged to contact the Editors informally, preferably by email, prior to submission.

Please e-mail Professor Lars Grüne at:

lars.gruene@uni-bayreuth.de. Authors can submit their papers online (see link to right).

Special Issue on Data-driven systems and control: analysis, modelling, optimisation, and stochasticity
截稿日期: 2025-01-31

In recent years we have seen a rapid increase in the development of novel data-driven techniques in systems and control. While this area has been a core topic in the community for decades, new (or rediscovered) techniques (e.g., Willem's fundamental lemma, neural differential equations, or extended dynamic mode decomposition in the Koopman framework to name just a few) as well as novel concepts like distributional robustness, regret, or Bayesian optimisation have opened new possibilities for analysing highly complex dynamical systems or designing controllers directly on data. Since measured data is always uncertain, this has triggered a revival of stochastic techniques in systems and (optimal) control. In addition, systems described by Markovian processes (e.g., governed by stochastic dynamics) are getting more and more attention. The idea of this topical collection is to bring together research devoted to recent trends in control for dynamical systems governed by stochastic differential or difference equations or on data-driven techniques used in systems and control. Topics include, but are not limited to, data-based methods, methods for complexity reduction, robustness analysis, optimal control, and optimisation-based methods. We welcome both original research articles as well as tutorial-style review or survey papers.

Special Issue on Dissipative Systems – Theory, Numerics, and Applications
截稿日期: 2025-01-31

Considering energy balances and dissipation is essential for constructing physically meaningful models of real-world processes. Energy-based modeling frameworks can encode the underlying physical properties into model structures, leading to dissipative systems (e.g., port-Hamiltonian systems). In practice, it is desirable to exploit these structures, for instance, in numerical schemes for simulation, optimization, control, model order reduction, and data-driven modeling. Mathematics of Control, Signals and Systems invites original research or survey papers containing mathematically rigorous results for the topical collection "Dissipative Systems – Theory, Numerics, and Applications", which address these challenges. We are particularly interested in submissions that discuss “modeling paradigms that result in dissipative systems (such as port-Hamiltonian systems)”, “analysis and control of dissipative systems”, “numerical methods for simulation, optimization, and control of dissipative systems”, “structure-preserving techniques for model order reduction”, “data-driven techniques for handling dissipativity”, and “applications in which dissipativity plays a key role”.