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Hence, multivariable PID controllers can be designed by solving an optimisation problem with bilinear matrix inequalities (BMIs).
The TMD parameters are tuned by solving an optimisation problem such that an effective reduction of the accelerations under realistic walking scenarios is ensured.
The minimum recourse costs can be determined then by solving an optimisation problem having a quadratic objective function and linear constraints.
The bounds are calculated by solving an optimisation problem for maximizing and minimizing the flux of each reaction iteratively.
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In this paper, we propose to solve the joint-estimation problem in a two-step, Dual Estimation approach, where we first solve the parameter-estimation problem by solving a constrained optimisation problem in a recursive manner and secondly, employ a robust polytopic observer design for state estimation.
In the integrated design, the process parameters are evaluated simultaneously with the parameters of the control system by solving a constrained non-linear optimisation problem.
The particle swarm optimisation (PSO) algorithm was used to solve an optimisation problem and achieve this outcome.
It is still unclear when an evolutionary algorithm is expected to solve an optimisation problem efficiently or otherwise.
The optimal operating parameters (feed flow rate, feed pressure) and design parameters (internal diameter, total number of tubes) are determined by solving the optimisation problem using an efficient successive quadratic programming (SQP) based method.
The optimal operating parameters (feed flux, feed pressure) and design parameters (internal diameter, total number of tubes) are determined by solving the optimisation problem using an efficient successive quadratic programming (SQP) based method.
The optimal Youla parameter is finally obtained by solving this optimisation problem.
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