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SummaryThis article bestows the linear quadratic Gaussian (LQG)/Loop Transfer Recovery (LTR) optimal controller design for a perturbed linear system having insufficient information about systems states through a multiobjective optimization approach. A Kalman filter observer is required to estimate the unknown states at the output from the noisy data. However, the main downside of the LQG controller's is that its robustness cannot be guaranteed because it consists of linear quadratic regulator (LQR) and Kalman observer, and due to observer incorporation within the LQR framework results in loss of robustness which is undesirable. Therefore, it is necessary to recover the robustness by tuning the controller which further plays havoc with system performance and control effort for certain plants. The present work addresses the investigation of the trade‐off between multiobjective indexes (formulated on the basis of robustness, optimal control, and performances) through three multiobjective optimization algorithms as NSGA‐II, multiobjective simulated annealing and multiobjective particle swarm optimization. The tuned parameters meet the competitive multiobjective performance indexes that are verified through simulation results. The Pareto front with multiple solutions helps to design a robust controller depending on the weightage given to the respective performance indexes. Simulation results reveal that the proposed multiobjective control strategy helps in recovering the characteristics of LQG/LTR.

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Industry-related competitiveness and reduction in manufacturing cost can be achieved by selection of ideal tolerance. Implementing non-traditional techniques for tolerance allocation to various parts in a mechanical assembly is an enervating procedure. Analytical methods are implemented to obtain creditable design. The main objective of this article is to minimize the manufacturing cost, quality loss, and TRSS (root sum square tolerance) for complex mechanical assemblies. The proposed methodology deals with the reciprocal exponential function. Hence, it is used to solve the problem for obtaining the closed-form solution by Lagrange multiplier method which integrates Lambert W function. The illustrations used in the research article demonstrate the feasibility and effectiveness of the traditional approaches. The results obtained also show that tolerance can be allocated economically and precisely. Here, non-traditional optimization methods are correlated and finally their performances are analyzed.