A model predictive control technique based on a step response model is developed using state estimation techniques. The standard step response model is extended so that integrating systems can be treated within the same framework. Based on the modified step response model, it is shown how the state estimation techniques from stochastic optimal control can be used to construct the optimal predic...

A scheme for synthesis of subsystem state-space models to be used for analysis of dynamic behavior of built-up linear structures is presented. Using measurements on each component, subsystem models are identified adopting contemporary system identification methods. The subsystem state-space models are transformed into a coupling form, at which kinematic constraints and equilibrium conditions fo...

Constrained State-space Model Predictive Control is presented in the paper. Predictive controller based on incremental linear state-space process model and quadratic criterion is derived. Typical types of constraints are considered – limits on manipulated, state and controlled variables. Control experiments with nonlinear model of multivariable laboratory process are simulated first and real ex...

Disturbance modeling and design of state estimators for offset-free Model Predictive Control (MPC) with linear state-space process models is considered in the paper for deterministic constant-type external and internal disturbances (modeling errors). The application and importance of constant state disturbance prediction in the state-space MPC controller design is presented. In the case with a ...

This paper presents a fast variational Bayesian method for linear state-space models. The standard variational Bayesian expectationmaximization (VB-EM) algorithm is improved by a parameter expansion which optimizes the rotation of the latent space. With this approach, the inference is orders of magnitude faster than the standard method. The speed of the proposed method is demonstrated on an art...

Model-checking can provide valuable insight into the behaviour of biochemical systems, answering quantitative queries which are more difficult to answer using stochastic simulation alone. However, model-checking is a computationally intensive technique which can become infeasible if the system under consideration is too large. Moreover, the finite nature of the state representation used means t...

Model checking of stochastic processes has been introduced to verify functional as well as performability properties of formally specified systems. In this paper, model checking for a wide class of stochastic fluid models (SFMs) is considered. We present a branching time temporal logic, which is similar to the continuous stochastic logic (CSL), for expressing real-time probabilistic properties ...