We present a new stabilizing control scheme for linear discrete-time systems with input and state constraints. Essentially, we seek a controller that is able to steer all initial states within a controlled invariant set towards the origin without violating the constraints. The control law builds on a predictive control scheme. We show that a prediction horizon of n steps, where n denotes the di...

We deal with the problem of small time local attainability (STLA) for nonlinear finite-dimensional time-continuous control systems. More precisely, given a nonlinear system ẋ(t) = f(t, x(t), u(t)), u(t) ∈ U , possibly subjected to state constraints x(t) ∈ Ω and a closed set S, our aim is to provide sufficient conditions to steer to S every point of a suitable neighborhood of S along admissible ...

Constraint-based processes require a set of rules that limit their behavior to certain boundaries. In these processes, the control flow is defined implicitly as a set of constraints or rules, and all possibilities that do not violate any of the given constraints are allowed to be executed. The present paper proposes a new approach to deal with constraint-based processes. The proposed approach i...

While Robust Model Predictive Control considers the worst-case system uncertainty, Stochastic Control, using chance constraints, provides less conservative solutions by allowing a certain constraint violation probability depending on predefined risk parameter. However, for safety-critical systems it is not only important to bound but reduce this as much possible. Therefore, an approach necessar...

inventory control problem is one of the problems in decision making and management that contains non-crisp parameters in real world. economic production quantity (epq) model in fuzzy environment has been studied so far by many researchers. one of the main assumptions in all previous researches was neglecting the inventory shortage. in this research, a new multi-item epq model with fuzzy random ...