The MPC ( Model- based Predictive control) is the most popular advanced control technique in process control industry as the safety constraints, performance constraints and economic objectives can be easily incorporated in the controller design. Extensions to non linear MPC are typically limited by computational complexity of underlying algorithms. Utilization of specific problem structure and Iteration Spread in Time approach may provide applicable algorithms, but also further theoretic development is needed e.g. to guarantee the feasibility of solution after each iteration. Feasibility constrained methods are also relevant in other industrial applications - Real Time Optimization and receding horizon state estimation. 

• Topic 1: Develop, implement and analyze feasibility constrained optimization algorithms for MPC using continuous-time model and multishooting problem formulation.
• Topic 2: Develop, implement and analyze feasibility constrained optimization algorithms for RTO with possibly decentralized implementation.
• Topic 3: Develop, implement and analyze optimization algorithms for moving horizon state estimation.
• Topic 4: Develop and implement tool for grey box identification with focus on MPC applications (optimization of multistep prediction error).