Non-overshooting stabilisation via state and output feedback

The concept of “strong stability” of LTI systems has been introduced in a recent paper [KHP2]. This is a stronger notion of stability compared to alternative definitions (e.g. stability in the sense of Lyapunov, asymptotic stability), which allows the analysis and design of control systems with non-overshooting response in the state-space for arbitrary initial conditions. The paper reviews the notion of “strong stability” [KHP2] and introduces the problem of non-overshooting stabilization. It is shown that non-overshooting stabilization under dynamic and static output feedback are, in a certain sense, equivalent problems. Thus, we turn our attention to static non-overshooting stabilization problems under state-feedback, output injection and output feedback. After developing a number of preliminary results, we give a geometric interpretation to the problem in terms of the intersection of an affine hyperplane and the interior of an open convex cone. A solution to the problem is finally obtained via Linear Matrix Inequalities, along with the complete parametrization of the optimal solution set

Mentoring language teaching professionals in/through Exploratory Practice

This chapter tells the story of our experiences of starting Exploratory Practice (EP) in a new and vibrant setting (Professional Development in Turkey and Northern Cyprus). We explore in-service language teacher education with the introduction of EP as an inquiry-based tool for professional development. Engaging teachers as learners of ‘doing-being’ research can be a catalyst for developing from a focus on practitioners as passive recipients of knowledge to active generators of understandings and knowledge. However, this shift poses epistemological challenges for teachers who do not yet have or are still developing a researcher identity. Together with teachers, teacher educators and curriculum developers, we work to understand the complexities of our various educational contexts

Non-overshooting stabilisation via state and output feedback

The concept of “strong stability” of LTI systems has been introduced in a recent paper [KHP2]. This is a stronger notion of stability compared to alternative definitions (e.g. stability in the sense of Lyapunov, asymptotic stability), which allows the analysis and design of control systems with non-overshooting response in the state-space for arbitrary initial conditions. The paper reviews the notion of “strong stability” [KHP2] and introduces the problem of non-overshooting stabilization. It is shown that non-overshooting stabilization under dynamic and static output feedback are, in a certain sense, equivalent problems. Thus, we turn our attention to static non-overshooting stabilization problems under state-feedback, output injection and output feedback. After developing a number of preliminary results, we give a geometric interpretation to the problem in terms of the intersection of an affine hyperplane and the interior of an open convex cone. A solution to the problem is finally obtained via Linear Matrix Inequalities, along with the complete parametrization of the optimal solution set

Linearized min‐max robust model predictive control: Application to the control of a bioprocess

International audienceThis work deals with the problem of trajectory tracking for a nonlinear system with unknown but bounded model parameter uncertainties. First, this work focuses on the design of a robust nonlinear model predictive control (RNMPC) law subject to model parameter uncertainties implying solving a min-max optimization problem. Secondly, a new approach is proposed, consisting in relating the min-max problem to a more tractable optimization problem based on the use of linearization techniques, to ensure a good trade-off between tracking accuracy and computation time. The developed strategy is applied in simulation to a simplified macroscopic continuous photobioreactor model and is compared to the RNMPC and nonlinear model predictive controllers. Its efficiency and its robustness against parameter uncertainties and/or perturbations are illustrated through numerical results