Robust Control Methods for a Recycle Bioreactor

The paper presents a robust control design strategy for bioprocesses, which are characterized by strongly nonlinear dynamics. More precisely, we present the H2 methodology in order to compute the controller for a recycle Continuous Stirred Tank Bioreactor (CSTB). We consider a general method of formulating control problem, which makes use of linear fractional transformation as introduced by Doyle (1978). The formulation makes use of the general two-port configuration of the generalized plant with a generalized controller. The H2 norm is the quadratic criterion used in optimal control as LQG. The overall control objective is to minimize the H2 norm of the transfer matrix function from the weighted exogenous inputs to the weighted controlled outputs. The advantage of H2 control technique, which uses the linearized model of the CSTB, is that it is completely automated and very flexible. Finally, we prove that the closed loop control structure has very good inner robustness

A dynamic opposite learning assisted grasshopper optimization algorithm for the flexible job scheduling problem

Job shop scheduling problem (JSP) is one of the most difficult optimization problems in manufacturing industry, and flexible job shop scheduling problem (FJSP) is an extension of the classical JSP, which further challenges the algorithm performance. In FJSP, a machine should be selected for each process from a given set, which introduces another decision element within the job path, making FJSP be more difficult than traditional JSP. In this paper, a variant of grasshopper optimization algorithm (GOA) named dynamic opposite learning assisted GOA (DOLGOA) is proposed to solve FJSP. )e recently proposed dynamic opposite learning (DOL) strategy adopts the asymmetric search space to improve the exploitation ability of the algorithm and increase the possibility of finding the global optimum. Various popular benchmarks from CEC 2014 and FJSP are used to evaluate the performance of DOLGOA. Numerical results with comparisons of other classic algorithms show that DOLGOA gets obvious improvement for solving global optimization problems and is well-performed when solving FJSP

Robust-Adaptive Control Strategies for a Time Delay Bioelectrochemical Process Using Interval Observers

International audienceThe paper addresses the design and the analysis of adaptive and robust-adaptive control strategies for a complex recycled wastewater treatment bioprocess. The design procedures are developed under the realistic assumptions that the bacterial growth rates are unknown and the influent flow rates are time-varying and uncertain, but some lower and upper bounds of these uncertainties are known. The proposed control structures are achieved by combining a linearizing control law with an appropriately (asymptotic or interval based) state observer and with a parameter estimator used for on-line estimation of unknown kinetics. These approaches are applied to a complex time delay bioprocess resulting from the association of a recycling bioreactor with an electrochemical reactor. Numerical simulations are performed in order to validate the proposed algorithms