Actuator stiction compensation via model predictive control for nonlinear processes

Durand, Helen; Christofides, Panagiotis D.

doi:10.1002/aic.15171

Cited by 21 publications

(14 citation statements)

References 70 publications

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“…The impacts of the process on the materials, however (for example, impacts of changes in pressure or temperature in the fluid flow on stresses or strains in the equipment), are not typically taken into account when examining the use of EMPC for a chemical process, though these impacts also can be modeled dynamically (e.g., stresses and strains can vary in both time and space in a material). Following recent work on including valve dynamics in EMPC [13], it would be desirable to consider how accounting for the behavior of materials in response to the operating policies set up by an EMPC might affect the solutions for the EMPC. In the following sections, we utilize a simple pipe flow example to explore some of the differences between traditional EMPC design thinking and EMPC accounting explicitly for material behavior.…”

Section: Accounting For Empc Impacts On Equipmentmentioning

confidence: 99%

Economic Model Predictive Control and Process Equipment: Control-Induced Thermal Stress in a Pipe

Durand

2019

2019 American Control Conference (ACC)

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Recent work on economic model predictive control (EMPC) has indicated that some processes may be operated in a more economically-optimal fashion under a time-varying operating policy than under a steady-state operating policy. However, a concern for time-varying operation is how such a change in operating policy might impact the equipment within which the processes being controlled are carried out. While under steady-state operation, the operating conditions to which equipment would regularly be exposed can be estimated, this would be more difficult to assess thoroughly a priori under time-varying operation. It could be explored whether the EMPC could be made aware of any impacts the control actions that it chooses might have on equipment, and then to seek to impose constraints on these impacts. This would require explicit consideration of equipment design, material properties/behavior, and material loading at the EMPC design stage. This work provides an initial exploration of this topic by seeking to extract principles related to the integration of equipment material fidelity considerations and EMPC through an example accounting for a simple preliminary case of thermal stresses in a pipe at equilibrium conditions.

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Section: Accounting For Empc Impacts On Equipmentmentioning

confidence: 99%

Economic Model Predictive Control and Process Equipment: Control-Induced Thermal Stress in a Pipe

Durand

2019

2019 American Control Conference (ACC)

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“…For example, in Ref. [47], a first-principles model for an ethylene oxidation process [48] coupled with a model for a valve experiencing stiction [49] is developed. Though the pressure applied to this valve by a pneumatic actuator is a function of the states and inputs (i.e., it was not itself a measured state), a constraint was imposed in the EMPC design on the pressure.…”

Section: Data-gathering Empc Analysismentioning

confidence: 99%

Data-Based Nonlinear Model Identification in Economic Model Predictive Control

Giuliani

Durand

2018

Smart Sustain. Manuf. Syst.

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Many chemical/petrochemical processes in industry are not completely modeled from a first-principles perspective because of the complexity of the underlying physico-chemical phenomena and the cost of obtaining more accurate, physically relevant models. System identification methods have been utilized successfully for developing empirical, though not necessarily physical, models for advanced model-based control designs such as model predictive control (MPC) for decades. However, a fairly recent development in MPC is economic model predictive control (EMPC), which is an MPC formulated with an economics-based objective function that may operate a process in a dynamic (i.e., off steady-state) fashion, in which case the details of the process model become important for obtaining sufficiently accurate state predictions away from the steady-state, and the physics and chemistry of the process become important for developing meaningful profit-based objective functions and safety-critical constraints. Therefore, methods must be developed for obtaining physically relevant models from data for EMPC design. While the literature regarding developing models from data has rapidly expanded in recent years, many new techniques require a model structure to be assumed a priori, to which the data is then fit. However, from the perspective of developing a physically meaningful model for a chemical process, it is often not obvious what structure to assume for the model, especially considering the often complex nonlinearities characteristic of chemical processes (e.g., in reaction rate laws). In this work, we suggest that the controller itself may facilitate the identification of physically relevant models online from process operating data by forcing the process state to nonroutine operating conditions

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“…performance with minimum actuator wear, is obtained when the plant main controller is aware of the actuator nonlinearity. In this regard, the current trend is achieving stiction compensation through MPC, because of its natural way to handle input constraints [18]. Furthermore, in order to ease the numerical resolution, surro-gate models of limited complexity for the actuator plus its possible local compensation strategy are added to the optimization [19].…”

Section: Literature Review and Motivationmentioning

confidence: 99%

“…3, i.e. (18). The goal of showing this difference was just to stress out the relative importance of making the MPC aware of the external compensator.…”

Section: Simulation Testsmentioning

confidence: 99%

On Stiction Compensation Methods for Practical Nonlinear MPC Implementations

Pitarch

Santos

Prada

2018

2018 22nd International Conference on System Theory, Control and Computing (ICSTCC)

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Stiction in actuators is a common issue within the process industry, which may considerably degrade the control performance in practice. In particular, the improved prediction capabilities of model predictive control (MPC) vanish if such actuator imperfections are neglected in actual implementations. In this work, the authors review the more recent approaches for stiction compensation in literature and propose two practical alternatives that are more suitable to use in large-scale nonlinear MPC problems. The proposals are illustrated and tested over a real industrial case study: the fiber humidity control in a medium-density fibreboard dryer.

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Actuator stiction compensation via model predictive control for nonlinear processes

Cited by 21 publications

References 70 publications

Economic Model Predictive Control and Process Equipment: Control-Induced Thermal Stress in a Pipe

Economic Model Predictive Control and Process Equipment: Control-Induced Thermal Stress in a Pipe

Data-Based Nonlinear Model Identification in Economic Model Predictive Control

On Stiction Compensation Methods for Practical Nonlinear MPC Implementations

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