Dynamic Matrix Control (DMC) is a widely used model predictive controller (MPC) in industrial plants. The successful implementation of DMC in practical applications requires a proper tuning of the controller. The available tuning procedures are mainly based on experience and empirical results. This paper develops an analytical tool for DMC tuning. It is based on the application of Analysis of V...

In the nonlinear model predictive control (NMPC) field, it is well-known that the multistep control approach is superior to the single-step approach when examining high-order nonlinear systems. In the multistep control approach, however, the online minimization of a 2-norm square objective function over a control horizon of length M always requires solving a set of complex polynomial equations,...

This paper develops a stochastic hybrid model-based control system that can determine online the optimal control actions, detect faults quickly in the control process, and reconfigure the controller accordingly using interacting multiple-model (IMM) estimator and generalized predictive control (GPC) algorithm. A fault detection and control system consists of two main parts: the first is the fau...

The Tennessee Eastman (TE) Control Challenge proposed by Downs and Vogel [1] is a test bed problem for use in evaluating advanced process control methodologies from a plant-wide perspective. The dynamic model for the process (based on an actual industrial process) integrates the operation of five unit operations; viz. an exothermic, two-phase reactor, a partial condenser, a centrifugal compress...

In this study, the swing-up of a double inverted pendulum is controlled by nonlinear model predictive control (NMPC). The fast computation algorithm called C/GMRES (continuation/generalized minimal residual) is applied to solve a nonlinear two-point boundary value problem over a receding horizon in real time. The goal is to swing-up and stabilize two pendulums from the downward to upright posit...

Solid sorbent-based CO2 capture from flue gas provides a novel alternative to traditional solvent-based processes due to potentially lower energy consumption for regeneration. Optimal operation of such a gas-solid flow process requires an advanced control framework for efficient disturbance rejection and setpoint tracking. This work focuses on the development of computationally fast and accurat...

Abstract: This paper proposes a nonlinear model predictive control scheme with particular application to the regulation of acetate concentration in order to maximize the biomass growth during fed-batch cultures of E. Coli. A reference feed rate which enables to maintain the acetate concentration at a specified level is first determined by means of optimal conditions. This feed rate is further u...

State space based nonlinear model predictive control (NMPC) needs the state for the prediction of the system behavior. Unfortunately, for most applications, not all states are directly measurable. To recover the unmeasured states, typically a stable state observer is used. However, this implies that the stability of the closed-loop should be examined carefully, since no general nonlinear separa...

This article presents a systematic approach to realize highly dynamic control strategies for the air path of diesel engines. It is based on grey-box models individual components, designed be applied in nonlinear model predictive (NMPC). Specifically, they are suited algorithmic differentiation and gradient-based optimization. modular allows derive variety complex systems can identified with low...

In the last decade, increasing use of NanoSats and CubeSats has made re-entry capsule an emerging research field needing updates in configuration technology. particular, door to advancements terms efficiency re-usability been opened by introduction inflatable and/or deployable aerodynamic brakes on-board electronics for active control. Such technologies allow smaller sizes at launch, controlled...