The closed-loop performance of model-based controllers often degrades over time due to increased model uncertainty. Some form of model maintenance must be performed to regularly adapt the system model using closed-loop data. This paper addresses the problem of control-oriented model adaptation in the context of predictive control of stochastic linear systems. A stochastic predictive control approach is presented that integrates stochastic optimal control with control-oriented input design in order to confer some degree of probing effect to the control inputs. The probing effect will enable generating informative closed-loop data for (online) control-oriented model maintenance. In a simulation study, the performance of the proposed stochasti...
In a major breakthrough, Guo and Chen [1] have recently shown how to establish the self--optimality ...
The optimization of predicted control policies in Model Predictive Control (MPC) enables the use of ...
We present Shrinking Horizon Model Predictive Control (SHMPC) for discrete-time linear systems with ...
The performance of predictive control strategies often degrades over time due to growing plant-model...
Abstract This paper presents a stochastic model predictive control method for linear time‐invariant ...
Model Predictive Control has become a prevailing technique in practice by virtue of its natural incl...
The main topic of this thesis is control of dynamic systems that are subject to stochastic disturban...
For the first time, a textbook that brings together classical predictive control with treatment of u...
Model predictive control is an attractive control method applicable to a wide range of real-world pr...
In this paper we consider model predictive control with stochastic disturbances and input constraint...
The work in this thesis focuses on the development of a Stochastic Model Predictive Control (SMPC) a...
With the steady growth in the availability of fast computing machines, control techniques based on a...
This thesis examines the basic asymptotic properties of various stochastic adaptive systems for iden...
Model predictive control (MPC) has demonstrated exceptional success for the high-performance control...
In this work, the model predictive control problem is extended to include not only open-loop control...
In a major breakthrough, Guo and Chen [1] have recently shown how to establish the self--optimality ...
The optimization of predicted control policies in Model Predictive Control (MPC) enables the use of ...
We present Shrinking Horizon Model Predictive Control (SHMPC) for discrete-time linear systems with ...
The performance of predictive control strategies often degrades over time due to growing plant-model...
Abstract This paper presents a stochastic model predictive control method for linear time‐invariant ...
Model Predictive Control has become a prevailing technique in practice by virtue of its natural incl...
The main topic of this thesis is control of dynamic systems that are subject to stochastic disturban...
For the first time, a textbook that brings together classical predictive control with treatment of u...
Model predictive control is an attractive control method applicable to a wide range of real-world pr...
In this paper we consider model predictive control with stochastic disturbances and input constraint...
The work in this thesis focuses on the development of a Stochastic Model Predictive Control (SMPC) a...
With the steady growth in the availability of fast computing machines, control techniques based on a...
This thesis examines the basic asymptotic properties of various stochastic adaptive systems for iden...
Model predictive control (MPC) has demonstrated exceptional success for the high-performance control...
In this work, the model predictive control problem is extended to include not only open-loop control...
In a major breakthrough, Guo and Chen [1] have recently shown how to establish the self--optimality ...
The optimization of predicted control policies in Model Predictive Control (MPC) enables the use of ...
We present Shrinking Horizon Model Predictive Control (SHMPC) for discrete-time linear systems with ...