Control and Decision Support Systems Laboratory

Research in this laboratory focuses on synthesis of control and decision support information structures and algorithms to produce control and decision support systems of broad application range including environmental systems, industrial processes, defence systems, autonomous intelligent unmanned vehicles, electrical drives, electro-mechanical systems and bossiness systems.

Research activities include:

  • Gray box modelling and identification of drinking water and wastewater systems
  • Interval state and parameter estimation
  • Hierarchical multilevel-multilayer structures and algorithms for monitoring, control and decision support of complex drinking water networks and urban wastewater systems
  • Genetic multiobjective optimisation
  • Genetic multiobjective model predictive control for logistics, production planning and scheduling
  • Predictive navigation of autonomous unmanned vehicles under dynamic obstacles
  • Data driven algorithms for process fault detection and diagnosis
  • Robustly feasible and softly switched hybrid model predictive control
  • Risk based model predictive control
  • Optimised risk based investment strategies in business systems
  • Fault tolerant model predictive control
  • Fuzzy-neural learning control of nonlinear uncertain systems
  • Optimising control of industrial processes
  • Networked control systems
  • Target tracking and sea
  • Multi-target tracking and control
  • State estimation and particle filtering
  • Robotics
  • Fuzzy Clustering
  • Information retrieval
  • Information fusion and uncertainty handling
  • Reliability and Dependability
  • Location based systems

Projects and recent achievements

  • SMArt Control of Wastewater Systems – SMAC, EU funded project EVK1-CT-2000-00056EC, March 2001 – February 2004, Fifth Framework Programme.
  • Nuffield Foundation: Approximate Probabilistic tracking in Cluttered Environment, 2004-2007

People academic staff

  • Dr Mourad Oussalah – Associate of CDSS Laboratory

PhD students

  • Jing Song Wang – Softly switched model predictive control: Generic development and application to water supply and distribution systems
  • Sekela Margaret Mwandosya – Hierarchical control of communication networks
  • Duc T. Nguyen – Neural adaptive control of uncertain nonlinear systems under not measurable states
  • Ruiyun Qi – Learning fuzzy modelling and control for discrete-time nonlinear uncertain Systems
  • Sadek Hamani - Location based system and 3G positioning

Selected publications

  • G. J. Kulawski, M. A. Brdys (2000). Stable adaptive control with recurrent networks. Automatica, 36(1), pp. 5-22.
  • P. Tatjewski, M. A. Brdys, J. Duda (2001). Optimising control of uncertain plants with constrained, feedback controlled outputs. International Journal of Control, 74(15), pp. 1510-1520.
  • M. A. Brdys, J. J Littler (2002). Fuzzy logic gain scheduling for non-linear servo tracking. Int. J. App. Math. Comput. Sci., 12(2), pp. 209-219.
  • M. M. Polycarpou, J. J. Uber, Z. Wang, F. Shang, M. A. Brdys (2002). Feedback control of water quality. IEEE Control Systems Magazine. June 02, pp.68-87.
  • W. Chotkowski, M. A. Brdys, K. Konarczak (2005). Dissolved oxygen control for activated sludge processes. International Journal of Systems Science, 36(2), 727-736.
  • K. Duzinkiewicz, M. A. Brdys, T. Chang (2005). Hierarchical model predictive control of integrated quality and quantity in drinking water distribution systems. Urban Water Journal, 2(2), 125-137.
  • M.A Brdys and P. Tatjewski (2005). Iterative Algorithms for Multilayer Optimizing Control. World Scientific Publishing, Co, Pte. Ltd., Main Street, River Edge, New York, USA; Imperial College Press, London, Singapore.
  • Anis Ahmed, Mietek A. Brdys (2006). Servo tracking of targets at sea. Int. J. App. Math. Comput. Sci., 16(2), pp.197-207.
  • K. Mazur, A. Borowa and M. A Brdys (2005). Identification and diagnosis of processes by AdMS - PCA method. Measurements, Control and Monitoring (PAK). Special Issue, vol.9, pp. 42-44.
  • M.A. Brdys, M. Grochowski, T. Gminski, K. Konarczak, M. Drewa.(2006). Hierarchical predictive control of integrated wastewater treatment systems. Control Engineering Practice, (in print).
  • G. Ewald, W. Kurek, M.A. Brdys (2006). Grid implementation of parallel multi-objective genetic algorithm for optimized allocation of chlorination stations in drinking water distribution systems: Chojnice case study. IEEE Trans. on System, Man and Cybernetics – Part C: Applications and Reviews (accepted for publication).
  • M. Oussalah, J. De Schutter (2002). Hybrid Fuzzy Probabilistic Data Association Filter and Joint Probabilistic Data Association Filter, Information Science, 142, pp. 195-226.
  • M. Oussalah, M. Newby (2003). Analysis of serial-parallel systems in the framework of fuzzy/possibility theory. Part I: Appraisal. Case of Independent components, Journal of Reliability Engineering and System Safety, vol. 79/3 pp. 353 – 368.
  • M. Oussalah (2003). Building fusion architecture for intelligent robotics applications. In Fusion of Soft Computing and Hard Computing Techniques for Autonomous Robotic Systems, (Eds: Changjiu Zhou, Dario Maravall and Da Ruan) Physica-Verlag, pp. 35-73, ISSN 1434-9922.
  • M. Oussalah (2004). Some Notes on fusion of uncertain information, International Journal of Intelligent Systems, 19(6), pp. 491-563.
  • M. Oussalah (2005). Bipolar logic for analysis of human-computer interaction. Application to probabilistic argumentation system. Kebernetee, Vol. 34, 9/10, pp. 1349-1383.


Head of the CDSS laboratory is the General Chair and Chair of International Programme Committee of International Federation on Automatic Control (IFAC) Symposium on ‘Large Scale Complex Systems’, Gdansk, Poland, July 23-25, 2007.