Parametric Control Approach Research Articles

A novel data-driven model for prediction and adaptive control of pH in raceway reactor for microalgae cultivation

This work proposes a new data-driven model to estimate and predict pH dynamics in freshwater raceway photobioreactors. The resulting model is based purely on data measured from the reactor and divides the pH dynamics into two different behaviors. One behavior is described by the variation of pH due to the photosynthesis phenomena made by microalgae; and the other comes from the effect of CO2 injections into the medium for control purposes. Moreover, it was observed that the model parameters vary throughout the day depending on the weather conditions and reactor status. Thus, a decision tree algorithm is also developed to capture the parameter variation based on measured variables of the system, such as solar radiation, medium temperature, and medium level. The proposed model has been validated for a data set of more than 100 days during 10 months in a semi-industrial raceway reactor, covering a wide range of weather and system scenarios. Additionally, the proposed model was used to design an adaptive control algorithm which was also experimentally tested and compared with a classical fixed parameter control approach.

Editorial for the special issue “Control‐theoretic approaches for systems in the life sciences”

Editorial for the special issue “Control<scp>‐theoretic</scp> approaches for systems in the life sciences”

Learning Adaptive Differential Evolution Algorithm From Optimization Experiences by Policy Gradient

Differential evolution is one of the most prestigious population-based stochastic optimization algorithm for black-box problems. The performance of a differential evolution algorithm depends highly on its mutation and crossover strategy and associated control parameters. However, the determination process for the most suitable parameter setting is troublesome and time consuming. Adaptive control parameter methods that can adapt to problem landscape and optimization environment are more preferable than fixed parameter settings. This article proposes a novel adaptive parameter control approach based on learning from the optimization experiences over a set of problems. In the approach, the parameter control is modeled as a finite-horizon Markov decision process. A reinforcement learning algorithm, named policy gradient, is applied to learn an agent (i.e., parameter controller) that can provide the control parameters of a proposed differential evolution adaptively during the search procedure. The differential evolution algorithm based on the learned agent is compared against nine well-known evolutionary algorithms on the CEC'13 and CEC'17 test suites. Experimental results show that the proposed algorithm performs competitively against these compared algorithms on the test suites.

Simultaneous design & control of a reactive distillation system – A parametric optimization & control approach

We present a detailed dynamic model for a reactive distillation system, based on which we develop design-dependent explicit optimal control strategies. A mixed-integer dynamic optimization formulation is then proposed integrating design, control and operational components, the solution of which allows us to derive explicit closed-loop strategies that maintain stable and operable conditions in the presence of process disturbances. The simultaneous approach is illustrated with the design of a methyl tert-butyl ether reactive distillation system example as a part of the developed model library in the RAPID SYNOPSIS project.

On the existence of solutions of the linear matrix equation [formula omitted] with the rank constraint

In this paper, we present some sufficient conditions for the existence of solutions of the constrained linear matrix equation, which play an important role in designing and analyzing a direct parametric control approach for the eigenstructure assignment problem. Based on these sufficient conditions, the solution of this constrained linear matrix equation is obtained. Finally, two numerical examples are given to illustrate the effectiveness of the proposed algorithm.

A novel hybrid multi-objective immune algorithm with adaptive differential evolution

In this paper, we propose a novel hybrid multi-objective immune algorithm with adaptive differential evolution, named ADE-MOIA, in which the introduction of differential evolution (DE) into multi-objective immune algorithm (MOIA) combines their respective advantages and thus enhances the robustness to solve various kinds of MOPs. In ADE-MOIA, in order to effectively cooperate DE with MOIA, we present a novel adaptive DE operator, which includes a suitable parent selection strategy and a novel adaptive parameter control approach. When performing DE operation, two parents are respectively picked from the current evolved and dominated population in order to provide a correct evolutionary direction. Moreover, based on the evolutionary progress and the success rate of offspring, the crossover rate and scaling factor in DE operator are adaptively varied for each individual. The proposed adaptive DE operator is able to improve both of the convergence speed and population diversity, which are validated by the experimental studies. When comparing ADE-MOIA with several nature-inspired heuristic algorithms, such as NSGA-II, SPEA2, AbYSS, MOEA/D-DE, MIMO and D2MOPSO, simulations show that ADE-MOIA performs better on most of 21 well-known benchmark problems.

A fuzzy logic controller applied to a diversity-based multi-objective evolutionary algorithm for single-objective optimisation

In recent years, Multi-Objective Evolutionary Algorithms (moeas) that consider diversity as an objective have been used to tackle single-objective optimisation problems. The ability to deal with premature convergence has been greatly improved with these schemes. However, they usually increase the number of free parameters that need to be tuned. To improve results and avoid the tedious hand-tuning of algorithms, the use of automated parameter control approaches that are able to adapt parameter values during the course of an evolutionary run are becoming more common in the field of Evolutionary Computation (ec). This research focuses on the application of parameter control approaches to diversity-based moeas. Two external parameter control methods are investigated; a novel method based on Fuzzy Logic and a recently proposed Hyper-heuristic. These are compared to an internal control method that uses self-adaptation. An extensive comparison of the three methods is carried out using a set of single-objective benchmark problems of diverse complexity. Analyses include comparisons to a wide range of schemes with fixed parameters and to a single-objective approach. The results show that the fuzzy logic and hyper-heuristic methods are able to find similar or better solutions than the fixed parameter methods for a significant number of problems, with considerable savings in computational resources and time, whereas the self-adaptive strategy provides little benefit. Finally, we also demonstrate that the controlled diversity-based moea outperforms the single-objective scheme in most cases, thus showing the benefits of solving single-objective problems through diversity-based multi-objective schemes.

Simplified procedure for controlling pressure distribution of a scramjet combustor

Scramjet engines are used at extreme temperatures and velocity. New control problems involving distributed parameter control have been found concerning investigations of the control of scramjet engines whose physical states are spatially interacted. Succeeding the existing theoretical studies on the distributed parameter control for scramjet engines, this paper puts forward a simplified distributed parameter control approach for scramjet engines aimed at engineering application. The simplified control procedure uses the classical proportional-integral (PI) compensation to control the target pressure distribution of scramjet engines, which is effective and applicable for practical implements. Simulation results show the validation of the simplified distributed parameter control procedure.

Fuzzy logic-controlled diversity-based multi-objective memetic algorithm applied to a frequency assignment problem

One of the most commonly known weaknesses of Evolutionary Algorithms (eas) is the large dependency between the values selected for their parameters and the results. Parameter control approaches that adapt the parameter values during the course of an evolutionary run are becoming more common in recent years. The aim of these schemes is not only to improve the robustness of the controlled approaches, but also to boost their efficiency. In this paper we investigate the application of parameter control schemes to address a well-known variant of the Frequency Assignment Problem (fap). The controlled ea is a highly efficient diversity-based multi-objective memetic scheme. In this work, a novel general parameter control method based on Fuzzy Logic is devised. In addition, a hyper-heuristic is also considered as an established parameter control scheme. An extensive experimental evaluation of both methods is carried out that includes a comparison to a wide-range of fixed-parameter schemes. The results show that the fuzzy logic method is able to find similar or even better solutions than the hyper-heuristic and the fixed-parameter methods for several instances of the fap. In fact, this method yielded frequency plans that outperform the best previously published solutions. Finally, the generality of the fuzzy logic-based scheme is demonstrated by controlling different kinds of parameters.

A parameter control method of evolutionary algorithms using exploration and exploitation measures with a practical application for fitting Sovova's mass transfer model

Exploration and exploitation are omnipresent terms in evolutionary computation community that have been broadly utilized to explain how evolutionary algorithms perform search. However, only recently exploration and exploitation measures were presented in a quantitative way enabling to measure amounts of exploration and exploitation. To move a step further, this paper introduces a parameter control approach that utilizes such measures as feedback to adaptively control evolution processes. The paper shows that with new exploration and exploitation measures, the evolution process generates relatively well results in terms of fitness and/or convergence rate when applying to a practical chemical engineering problem of fitting Sovova's model. We also conducted an objective statistical analysis using Bonferroni–Dunn test and sensitivity analysis on the experimental results. The statistical analysis results again proved that the parameter control strategy using exploration and exploitation measures is competitive to the other approaches presented in the paper. The sensitivity analysis results also showed that different initial values may affect output in different magnitude.

Synthesis of linear aperiodic arrays using a self-adaptive hybrid differential evolution algorithm

A self-adaptive hybrid differential evolution algorithm is proposed to optimise linear aperiodic arrays with a minimum peak sidelobe level. The proposed method incorporates a local search operation into a differential evolution (DE) algorithm to accelerate its convergence rate. Moreover, the self-adaptive parameter control approach is employed to avoid tuning the parameters of the DE algorithm. Synthesis examples are compared with the linear aperiodic array designs in the literature. Numerical results demonstrate that our approach is superior to existing algorithms in terms of the high-quality solutions and relatively small computational cost.

Adaptive control of robot manipulators with neural network based compensation of frictional uncertainties

In this paper a framework is proposed for the adaptive control of robotic manipulators which combines parametric adaptive control with Artificial Neural Network (ANN)-based compensation of dynamic uncertainties like friction. The proposed method utilizes a passivity-based parametric adaptive control approach and makes use of the ANN models as generic identifiers to compensate for unmodelled friction effects. Unlike many approaches for ANN based control in the literature, parameter update equations for the ANN model and for the parametric adaptive model are driven by both the tracking error and the system identification error. A stability analysis is given based on the passivity properties of the manipulator dynamics. The methodology is successfully tested for the control of a Direct Drive SCARA arm and performance is compared with standard adaptive control schemes.

Neural Network Based Friction Compensation for the Adaptive Control of Robot Manipulators

This paper presents a methodology for the control of robot manipulators which combines parametric adaptive control with Artificial Neural Network (ANN) compensation of dynamic uncertainties like friction. The proposed method utilizes passivity based parametric adaptive control approach and makes use of the ANN models as generic identifiers to compensate for unmodelled friction effects. The parameter update equations for the parametric adaptive model and the ANN model are driven by both tracking error and the prediction error. The methodology is tested for the control of Direct Drive SCARA arm and performance is compared with standard adaptive control schemes.

Self-Tuning Adaptive Control of a DC Motor Driven Single Axis Table

Abstract This paper describes the application of self-tuning adaptive control on a laboratory model of a DC motor driven uniaxial table. The effects of forgetting factor, initial estimates and initial covariance matrix on the estimation procedure and the control algorithm are investigated. It was observed that the self-tuning adaptive control provided better response than the fixed parameter control approach.

Control of an Unstable Batch Chemical Reactor

As often experienced in industrial practice, a fixed-parameter PID can do a good job, even for potentially challenging problems such as open-loop unstable processes. However in such cases, considerable a priori process knowledge may be required in order to adequately tune the controller and make the control performance robust to changes in operating conditions. Adaptive schemes, on the other hand, require less prior plant information but they should not be regarded as magic solutions to control problems. In this study, alternative adaptive control schemes are presented for the temperature control of an open-loop unstable batch chemical reactor in which the sequential exothermic reactions A→B→C are carried out. The performance of such control systems are compared with that of a PID controller, designed using IMC-based rules, and detuned to ensure robustness to process parameter changes along the temperature trajectory. Since the required detuning results in poor disturbance rejection, one would expect that the adoption of adaptive strategies should improve performance. However, the fact that a fixed-parameter PID controller can be designed to perform reasonably well does not imply that a self-tuning version will do at least as well. A self-tuning scheme combined with a parametric control approach can successfully deal with the reactor start-up and the regulatory problem, provided that the adaptive scheme's process model order is adequately selected. Thus, a self-tuning PID controller, which is based on a second-order model, is liable to failure if the true process is effectively of higher order.

Variable structure and time-varying parameter control for hydroelectric generating unit

A variable-structure control strategy relative to the variable plant structure and output variation is developed, and a nonlinear compensation approach is proposed and analyzed. Moreover, for a certain plant structure with variable parameters a time-varying parameter control approach is developed, and its stability is analyzed and proved. Simulation results show the effectiveness of the proposed control strategy applied to an hydroelectric generating unit.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>