I, 4th EditionVol.
Our basic model predictive control MPC scheme consists of a finite horizon MPC technique with the introduction of an additional state constraint which we have denoted contractive constraint. This is a Lyapunov-based approach in which a Lyapunov function chosen a priori is decreased, not continuously, but discretely; it is allowed to increase at other times between prediction horizons.
We will show in this work that the implementation of this additional constraint into the on-line optimization makes it possible to prove rather strong stability properties of the closed-loop system.
In the nominal case and in the absence of disturbances, it is possible to show that the presence of the contractive constraint renders the closed-loop system exponentially stable.
Another important aspect considered in this work is the computational efficiency and implement ability of the algorithms proposed. The MPC schemes previously proposed in the literature which are able to guarantee stability of the closed-loop system involve the solution of a nonlinear programming problem at each time step in order to find the optimal or, at least, feasible control sequence.
Nonlinear programming is the general case in which both the objective and constraint functions may be non-linear, and is the most difficult of the smooth optimization problems. Due to the difficulties inherent to solving nonlinear programming problems and since MPC requires the optimal or feasible solution to be computed on-line, it is important that an alternative implementation be found which guarantees that the problem can be solved in a finite number of steps.
It is well-known that both linear and quadratic programming QP problems satisfy this requirement. Thus, in order to make the algorithm more easily implementable we introduce the difficulty of having to handle the mismatch between the real nonlinear system and its linear approximation which is used for prediction.
In other words, we now have a robust MPC control problem at hand.
In summary, this thesis is an application of contractive principles to model predictive control and it is dedicated to robust stability analysis, design and implementation of state and output feedback "contractive" MPC schemes.Summary This thesis deals with linear Model Predictive Control, MPC, with the goal of making a controller for an arti cial pancreas.
A diabetic is simulated by a math-. The Bellman Award is given for distinguished career contributions to the theory or application of automatic control.
It is the highest recognition of professional achievement for US control systems engineers and scientists. Abstract.
This thesis addresses the development of stabilizing model predictive control algorithms for nonlinear systems subject to input and state constraints and in the presence of parametric and/or structural uncertainty, disturbances and measurement noise. Abstract.
This thesis addresses the development of stabilizing model predictive control algorithms for nonlinear systems subject to input and state constraints and in the presence of parametric and/or structural uncertainty, disturbances and measurement noise. The present manuscript is an empirically based theoretical paper that presents, describes, and examines the Bar-On Model of Emotional-Social Intelligence (ESI) in deep.
I’ve been trying to delve deeper into predictive processing theories of the brain, and I keep coming across Karl Friston’s work on “free energy”. At first I felt bad for not understanding this. Then I realized I wasn’t alone. There’s an entire not-understanding-Karl-Friston internet.