Learn More
— For nonlinear discrete time systems satisfying a controllability condition, we present a stability condition for model predictive control without stabilizing terminal constraints or costs. The condition is given in terms of an analytical formula which can be employed in order to determine a prediction horizon length for which asymptotic stability or a(More)
— We present a networked control scheme which uses a model based prediction and time-stamps in order to compensate for delays and packet dropouts in the transmission between sensor and controller and between controller and actuator, respectively. In order to analyze the properties of our scheme, we introduce the notion of prediction consistency which(More)
— We propose a model predictive control (MPC) strategy for sampled-data implementation (with the zero order hold assumption) of continuous-time controllers for general nonlinear systems. We assume that a continuous-time controller has been designed so that the continuous-time closed-loop satisfies all performance requirements. Then, we use this control law(More)
In this paper we develop and illustrate methods for estimating the degree of subopti-mality of receding horizon schemes with respect to infinite horizon optimal control. The proposed a posteriori and a priori methods yield estimates which are evaluated online along the computed closed–loop trajectories and only use numerical information which is readily(More)
Control of systems where the information between the controller, actuator, and sensor can be lost or delayed can be challenging with respect to stability and performance. One way to overcome the resulting problems is the use of prediction based compensation schemes. Instead of a single input, a sequence of (predicted) future controls is submitted and(More)
In order to guarantee stability, known results for MPC without additional terminal costs or endpoint constraints often require rather large prediction horizons. Still, stable behavior of closed loop solutions can often be observed even for shorter horizons. Here, we make use of the recent observation that stability can be guaranteed for smaller prediction(More)
We consider a distributed non cooperative control setting in which systems are interconnected via state constraints. Each of these systems is governed by an agent which is responsible for exchanging information with its neighbours and computing a feedback law using a nonlinear model predictive controller to avoid violations of constraints. For this setting(More)
— For networked systems, the control law is typically subject to network flaws such as delays and packet dropouts. Hence, the time in between updates of the control law varies unexpectedly. Here, we present a stability theorem for nonlinear model predictive control with varying control horizon in a continuous time setting without stabilizing terminal(More)
  • Stephanie C Knüpfer, Martina D Liechti, Livio Mordasini, Dominik Abt, Daniel S Engeler, Jens Wöllner +8 others
  • 2014
BACKGROUND Sacral neuromodulation has become a well-established and widely accepted treatment for refractory non-neurogenic lower urinary tract dysfunction, but its value in patients with a neurological cause is unclear. Although there is evidence indicating that sacral neuromodulation may be effective and safe for treating neurogenic lower urinary tract(More)