Model-Free Extremum Seeking Control of Bioprocesses: A Review with a Worked Example

Dewasme, Laurent; Wouwer, Alain Vande

doi:10.3390/pr8101209

Cited by 24 publications

(9 citation statements)

References 89 publications

(130 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This first assumption implies that (i) driving the system to a stable steady-state manifold is feasible and that (ii) the system in the steady state is likely to be a convex/concave function of the input. In bioprocess applications, these statements find their justifications in the bifurcation analysis developed in [23], which can be considered as a basis for further investigations of bioprocesses [24]. Assumption 2.…”

Section: A Diso Adaptive Slope-seeking Strategymentioning

confidence: 99%

Dual-Input Slope Seeking Control of Continuous Micro-Algae Cultures with Experimental Validation

et al. 2021

Self Cite

View full text Add to dashboard Cite

This paper investigates the application of adaptive slope-seeking strategies to dual-input single output dynamic processes. While the classical objective of extremum seeking control is to drive a process performance index to its optimum, this paper also considers slope seeking, which allows driving the performance index to a desired level (which is thus sub-optimal). Moreover, the consideration of more than one input signal allows minimizing the input energy thanks to the degrees of freedom offered by the additional inputs. The actual process is assumed to be locally approachable by a Hammerstein model, combining a nonlinear static map with a linear dynamic model. The proposed strategy is based on the interplay of three components: (i) a recursive estimation algorithm providing the model parameters and the performance index gradient, (ii) a slope generator using the static map parameter estimates to convert the performance index setpoint into slope setpoints, and (iii) an adaptive controller driving the process to the desired setpoint. The performance of the slope strategy is assessed in simulation in an application example related to lipid productivity optimization in continuous cultures of micro-algae by acting on both the incident light intensity and the dilution rate. It is also validated in experimental studies where biomass production in a continuous photo-bioreactor is targeted.

show abstract

Section: A Diso Adaptive Slope-seeking Strategymentioning

confidence: 99%

Dual-Input Slope Seeking Control of Continuous Micro-Algae Cultures with Experimental Validation

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…The body frame is specified by the quadrotor orientation, with the rotor axes positioned in the positive direction and the arms positioned in the and directions. The quadrotor attitude is concluded by three angles, which are terms, roll-φ, pitch-θ, and yaw-ψ [14,20]. The equations of quadrotor linear motion are [21]:…”

Section: Modelling Of Quadrotormentioning

confidence: 99%

“…Equations (20,21,22) and (27, 28 29) represent linear motions and angular motions of the quadrotor, respectively.…”

Section: Modelling Of Quadrotormentioning

confidence: 99%

Optimized PID Control of Quadrotor System Using Extremum Seeking Algorithm

Muhsen¹,

Raafat²

2021

ETJ

View full text Add to dashboard Cite

This paper presents the development of an automatic tuning of conventional PI-D controllers for Quadrotor system. The most critical problem of tuning the parameters of the PID, is that they can drastically affect the performance of the system. Accordingly, Extremum seeking (ES) algorithm is utilized here to reduce a selected cost function that brings the required performance aspects. The results show that the ES-PID controller provides better stability and performance as compared with only PI-D controller. The parameters of ES-PID controller have been successfully tuned and modified to reach the desired destination with significantly less overshoot, oscillations, and faster convergence.

show abstract

“…This is mainly attributed to the prohibitively slow convergence when using such model‐free methods. For example, in a recent study comparing different extremum seeking control algorithms for a bioprocess, the fastest convergence achieved was around 25 h, (with some other approaches even taking 1000 h to converge) 10 11 .…”

Section: Introductionmentioning

confidence: 99%

“…For example, in a recent study comparing different extremum seeking control algorithms for a bioprocess, the fastest convergence achieved was around 25 h, (with some other approaches even taking 1000 h to converge). 10 Another model-free gradient estimation scheme applied to a bioprocess also took around 1000 h to converge. 11 The main bottleneck arises from the gradient estimation step.…”

mentioning

confidence: 99%

Model‐free real‐time optimization of process systems using safe Bayesian optimization

Krishnamoorthy

Doyle²

2023

AIChE Journal

View full text Add to dashboard Cite

Conventional real‐time optimization (RTO) requires detailed process models, which may be challenging or expensive to obtain. Model‐free RTO methods are an attractive alternative to circumvent the challenge of developing accurate models. Most model‐free RTO methods are based on estimating the steady‐state cost gradient with respect to the decision variables and driving the estimated gradient to zero using integral action. However, accurate gradient estimation requires clear time scale separation from the plant dynamics, such that the dynamic plant can be assumed to be a static map. For processes with long settling times, this can lead to prohibitively slow convergence to the optimum. To avoid the need to estimate the cost gradients from the measurement, this article uses Bayesian optimization, which is a zeroth order black‐box optimization framework. In particular, this article uses a safe Bayesian optimization based on interior point methods to ensure that the setpoints computed by the model‐free steady‐state RTO layer are guaranteed to be feasible with high probability (i.e., the safety‐critical constraints will not be violated at steady‐state). The proposed method can thus be seen as a model‐free variant of the conventional two‐step steady‐state RTO framework (with steady‐state detection), which is demonstrated on a benchmark Williams‐Otto reactor example.

show abstract

Model-Free Extremum Seeking Control of Bioprocesses: A Review with a Worked Example

Cited by 24 publications

References 89 publications

Dual-Input Slope Seeking Control of Continuous Micro-Algae Cultures with Experimental Validation

Dual-Input Slope Seeking Control of Continuous Micro-Algae Cultures with Experimental Validation

Optimized PID Control of Quadrotor System Using Extremum Seeking Algorithm

Model‐free real‐time optimization of process systems using safe Bayesian optimization

Contact Info

Product

Resources

About