Publications by Dominique Bonvin

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Iterative real-time optimization schemes that employ modifier adaptation add bias and gradient correction terms to the model that is used for optimization. These affine corrections lead to meeting the first-order necessary conditions of optimality of the p ...

The problem of controlling nonlinear nonminimum-phase systems is considered, where standard input-output feedback linearization leads to unstable internal dynamics. This problem is handled here by using the observability normal form in conjunction with input-output linearization. The system is feedback linearized upon neglecting a part of the system dynamics, with the neglected part being considered as a perturbation. A linear controller is designed to accommodate the perturbation resulting from the approxi

The performance of predictive controller is typically poor when the true plant evolution deviates significantly from that predicted by the model. A robust approach that considers model uncertainty explicitly is then needed. However, it is often difficult to find a single input profile that works for the range of uncertainty considered. Thus, multiple input profiles, i.e. one for each realization of the uncertainty, need to be determined, which is computationally extremely expensive. This paper proposes an a

This paper describes the optimization of batch processes in the presence of uncertainty and constraints. The optimal solution consists of keeping certain path and terminal constraints active and driving the sensitivities to zero. The case where the terminal constraints have a larger bearing on the cost than the sensitivities is considered, for which a two-time-scale methodology is proposed. The problem of meeting the active terminal constraints is addressed on-line using trajectory tracking, whilst pushing

This paper deals with the real-time optimization of uncertain plants and proposes an approach based on surrogate models to reach the plant optimum when the plant cost gradient is imperfectly known. It is shown that, for processes with only box constraints, ...

A direct adaptive control methodology for the rejection of unmeasured non-vanishing disturbances is proposed. The approach uses the framework of polynomial RST controllers and relies on the internal model principle with additional degrees of freedom provid ...

An analysis of the variance of the parameters of a multi-input plant estimated in closed-loop operation is performed. More specifically, the effect of the simultaneous excitation of an additional input on the variance of the estimated parameters is investi ...

This paper describes an optimization strategy for operating solid-oxide fuel-cell systems at optimal efficiency. Specifically, we present the experimental validation of a real-time optimization (RTO) strategy applied to a commercial solid-oxide fuel-cell s ...

In the context of dynamic optimization, plant variations necessitate adaptation of the input profiles in order to guarantee both feasible and optimal operation. For those problems having path constraints, two sets of directions can be distinguished in the ...

An improvement over classical dynamic feedback linearization for the control of a nonholonomic mobile robot is proposed. The use of a state extension of higher dimension than in the case of dynamic feedback linearization helps reject constant disturbances ...

Predictive control and feedback linearization are two popular approaches for the control of nonlinear systems. In this paper, a cascade combination of the two methods is proposed, where control based on input- output feedback linearization forms the inner ...

A gradient-descent method for the run-to-run tuning of MPC controllers is proposed. It is shown that, with an assumption on process repeatability, the MPC tuning parameters may be brought to a locally optimal set. SISO and MIMO examples illustrate the char ...

This note investigates the computation of extents of reaction from a limited number of state measurements. It is shown that this computation differs depending on whether the transformation to vessel extents is based on the heat or the enthalpy balance. Acc ...

In the framework of real-time optimization, measurements are used to compensate for effects of uncertainty. The main approach uses measurements to update the parameters of a process model. In contrast, the constraint-adaptation scheme uses the measurements ...

This paper investigates the relations between three different properties, which are of importance in optimal control problems: dissipativity of the underlying dynamics with respect to a specific supply rate, optimal operation at steady state, and the turnp ...

In the framework of real-time optimization, measurement-based schemes have been developed to deal with plant-model mismatch and process variations. These schemes differ in how the feedback information from the plant is used to adapt the inputs. A recent id ...

In the framework of process optimization, measurements can be used to compensate for the effect of uncertainty. The method studied in this paper combines a process model and measurements to iteratively improve the operation of continuous processes. Unlike ...

The one-dimensional tubular reactor model with advection and possibly axial diffusion is the classical model of distributed chemical reaction systems. This system is described by partial differential equations that depend on the time t and the spati ...

The context of this paper is the use of process measurements to optimize batch processes in the presence of uncertainty. The optimal solution consists of (i) keeping certain path and terminal constraints active and (ii) driving the sensitivities to zero. I ...

Minimal state representations are parsimonious models having no redundant states. For homogeneous reaction systems with S species, R independent reactions, p independent inlet streams and one outlet stream, a nonlinear transformation of the numbers of mole ...

Dominique Bonvin

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Enforcing Model Adequacy in Real-Time Optimization via Dedicated Parameter Adaptation

Approximate Input-Output Linearization of Nonlinear Systems Using the Observability Normal Form

Robust Predictive Control Based on Neighboring Extremals

Combined On-Line and Run-to-Run Optimization of Batch Processes with Terminal Constraints

On Handling Cost Gradient Uncertainty in Real-Time Optimization

Asymptotic Rejection of Nonvanishing Disturbances Despite Plant-Model Mismatch

Direct Closed-Loop Identification of Multi-Input Systems: Variance Analysis

Enforcing optimal operation in solid-oxide fuel-cell systems

Parametric Sensitivity of Path-Constrained Optimal Control: Towards Selective Input Adaptation

Velocity Scheduling Controller for a Nonholonomic Mobile Robot

Analysis of Cascade Structure with Predictive Control and Feedback Linearization

Run-to-Run MPC Tuning via Gradient Descent

On the computation of extents of reaction with a limited number of measurements

Real-Time Optimization via Adaptation and Control of the Constraints

On turnpike and dissipativity properties of continuous-time optimal control problems

Process Optimization via Constraints Adaptation

Real-time optimization of continuous processes via constraints adaptation

Semi-analytical Solutions for Tubular Chemical Reactors

Measurement-based Optimization of Batch Processes: Meeting Terminal Constraints On-line via Trajectory Following

Minimal state representation of homogeneous reaction systems