scholarly journals Control of Microalgae Growth in Artificially Lighted Photobioreactors Using Metaheuristic-Based Predictions

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8065
Author(s):  
Viorel Minzu ◽  
George Ifrim ◽  
Iulian Arama
Keyword(s):  
Optimal Control ◽  
Computational Complexity ◽  
Process Model ◽  
Closed Loop ◽  
Biomass Concentration ◽  
Computational Effort ◽  
Soft Sensor ◽  
Metaheuristic Algorithm ◽  
Batch Mode ◽  
Microalgae Growth

A metaheuristic algorithm can be a realistic solution when optimal control problems require a significant computational effort. The problem stated in this work concerns the optimal control of microalgae growth in an artificially lighted photobioreactor working in batch mode. The process and the dynamic model are very well known and have been validated in previous papers. The control solution is a closed-loop structure whose controller generates predicted control sequences. An efficient way to make optimal predictions is to use a metaheuristic algorithm, the particle swarm optimization algorithm. Even if this metaheuristic is efficient in treating predictions with a very large prediction horizon, the main objective of this paper is to find a tool to reduce the controller’s computational complexity. We propose a soft sensor that gives information used to reduce the interval where the control input’s values are placed in each sampling period. The sensor is based on measurement of the biomass concentration and numerical integration of the process model. The returned information concerns the specific growth rate of microalgae and the biomass yield on light energy. Algorithms, which can be used in real-time implementation, are proposed for all modules involved in the simulation series. Details concerning the implementation of the closed loop, controller, and soft sensor are presented. The simulation results prove that the soft sensor leads to a significant decrease in computational complexity.

scholarly journals Implementation Aspects Regarding Closed-Loop Control Systems Using Evolutionary Algorithms

Inventions ◽  
2021 ◽  
Vol 6 (3) ◽  
pp. 53
Author(s):  
Viorel Minzu ◽  
Saïd Riahi ◽  
Eugen Rusu
Keyword(s):  
Optimal Control ◽  
Evolutionary Algorithms ◽  
Optimal Control Problem ◽  
Control Problem ◽  
Closed Loop ◽  
Batch Reactor ◽  
General Structure ◽  
Computational Effort ◽  
Loop Control ◽  
The Impact

When an optimal control problem requires an important computational effort, a metaheuristic algorithm (MA) can be a useful approach. An MA is conceived to solve a specific optimal control problem having a characteristic objective function. This algorithm solely yields only an optimal offline solution. The desideratum to have a closed-loop implementation can be fulfilled through a supplementary “tool”, the Receding Horizon Control (RHC) structure. This paper addresses a particular case and integrates Evolutionary Algorithms into the RHC structure. The main objective is to propose a general harmonization between the Controller of the closed loop and the Evolutionary Algorithm. Some details concerning the implementation of the closed loop and Controller are described. The impact of the RHC’s prediction technique upon the control sequences’ encoding is also analyzed. Two general structure Controllers are proposed, one of them conceived to cope with restrictive time constraints. Practical ideas have been illustrated through a case study: the well-known optimal control of a fed-batch reactor for ethanol production. This time, our implementation achieves a closed-loop solution. The results from the programs and simulation series validate the Controllers, EAs, and the closed-loop structure. Generally speaking, the association between RHC and EA can be a realistic solution to optimal process control.

scholarly journals Prediction error's minimization through a controller based on a Metaheuristic Algorithm

2020 ◽  
Vol 43 (3) ◽  
pp. 5-11
Author(s):  
Viorel Mînzu
Keyword(s):  
Optimal Control ◽  
Theoretical Analysis ◽  
Optimal Control Problem ◽  
Control Problem ◽  
Predictive Control ◽  
Closed Loop ◽  
Open Loop ◽  
The Other ◽  
Metaheuristic Algorithm ◽  
Prediction Technique

A Metaheuristic Algorithm (MA) can be a realistic method to solve a given Optimal Control Problem (OCP), but the result is an open-loop solution. If the Metaheuristic Algorithm is integrated within the Model Predictive Control (MPC) structure, a closed-loop solution can be achieved. The controller works using a prediction technique and prediction error's minimization. On the other side, the MA optimizes (minimizes or maximizes) the OCP's objective function. The controller is faced with two optimization tasks. This paper proves through theoretical analysis and simulations that the prediction error's minimization is implicitly accomplished.

On closed-loop equilibrium strategies for mean-field stochastic linear quadratic problems

2020 ◽  
Vol 26 ◽  
pp. 41
Author(s):  
Tianxiao Wang
Keyword(s):  
Optimal Control ◽  
Closed Loop ◽  
Optimal Control Problems ◽  
Mean Field ◽  
Open Loop ◽  
Time Inconsistency ◽  
Control Problems ◽  
Linear Quadratic ◽  
Linear Quadratic Optimal Control ◽  
Equilibrium Strategies

This article is concerned with linear quadratic optimal control problems of mean-field stochastic differential equations (MF-SDE) with deterministic coefficients. To treat the time inconsistency of the optimal control problems, linear closed-loop equilibrium strategies are introduced and characterized by variational approach. Our developed methodology drops the delicate convergence procedures in Yong [Trans. Amer. Math. Soc. 369 (2017) 5467–5523]. When the MF-SDE reduces to SDE, our Riccati system coincides with the analogue in Yong [Trans. Amer. Math. Soc. 369 (2017) 5467–5523]. However, these two systems are in general different from each other due to the conditional mean-field terms in the MF-SDE. Eventually, the comparisons with pre-committed optimal strategies, open-loop equilibrium strategies are given in details.

Discrete Time-Delay Optimal Control Method for Experimental Active Chatter Suppression and Its Closed-Loop Stability Analysis

2019 ◽  
Vol 141 (5) ◽  
Author(s):  
Xingwu Zhang ◽  
Ziyu Yin ◽  
Jiawei Gao ◽  
Jinxin Liu ◽  
Robert X. Gao ◽  
...  
Keyword(s):  
Optimal Control ◽  
Closed Loop ◽  
Control Method ◽  
Trial And Error ◽  
Stability Lobe Diagram ◽  
Weighting Matrix ◽  
Chatter Suppression ◽  
Stability Lobe ◽  
Optimal Control Method ◽  
Discrete Time Delay

Chatter is a self-excited and unstable vibration phenomenon during machining operations, which affects the workpiece surface quality and the production efficiency. Active chatter control has been intensively studied to mitigate chatter and expand the boundary of machining stability. This paper presents a discrete time-delay optimal control method for chatter suppression. A dynamical model incorporating the time-periodic and time-delayed characteristic of active chatter suppression during the milling process is first formulated. Next, the milling system is represented as a discrete linear time-invariant (LTI) system with state-space description through averaging and discretization. An optimal control strategy is then formulated to stabilize unstable cutting states, where the balanced realization method is applied to determine the weighting matrix without trial and error. Finally, a closed-loop stability lobe diagram (CLSLD) is proposed to evaluate the performance of the designed controller based on the proposed method. The CLSLD can provide the stability lobe diagram with control and evaluate the performance and robustness of the controller cross the tested spindle speeds. Through many numerical simulations and experimental studies, it demonstrates that the proposed control method can make the unstable cutting parameters stable with control on, reduce the control force to 21% of traditional weighting matrix selection method by trial and error in simulation, and reduce the amplitude of chatter frequency up to 78.6% in experiment. Hence, the designed controller reduces the performance requirements of actuators during active chatter suppression.

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