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.
A perturbed fractional-order filter (FOF)-based LQR control design with multiple performance indices is proposed in this paper. Three variants of the FOF-based linear quadratic regulator (LQR) system have been proposed along with choices on the commensurate order of state feedback law. The multi-objective problem formulation has been done with three performance objectives to address the different issues of design. The first performance index constitutes a weighted sum of ITAE (Integral Time of Absolute Error) and the difference between the eigenvalues of the plant and the LQR controlled plant, chosen to minimize the large oscillations as well as relative stability with respect to eigenvalues. A new lower bound of the singular values have been addressed to ensure the robust stability of the system through the formulation of a second performance objective. It is the minimization of singular values of the return ratio matrix at the plant's input. The third and the last performance index aims minimization of maximum singular values of the perturbation transfer function at the plant's output, so as to guarantee no closed-loop right half plane zeros. The multi-objective optimization problem is solved and the optimal solutions are obtained via Grey Wolf Optimization Algorithm. The simulation results validate the performance and effectiveness of the proposed control design.
Due to advancement in reconfigurable computing, Field Programmable Gate Array (FPGA) has gained significance due to its low cost and fast prototyping. Parallelism, specialization, and hardware level adaptation, are the key features of reconfigurable computing. FPGA is a programmable chip that can be configured or reconfigured by the designer, to implement any digital circuit. One major challenge in FPGA design is the Placement problem. In this placement phase, the logic functions are assigned to specific cells of the circuit. The quality of the placement of the logic blocks determines the overall performance of the logic implemented in the circuits. The Placement of FPGA is a Multi-Objective Optimization problem that primarily involves minimization of three or more objective functions. In this paper, we propose a novel strategy to solve the FPGA placement problem using Non-dominated Sorting Genetic Algorithm (NSGA-II) and Simulated Annealing technique. Experiments were conducted in Multicore Processors and metrics such as CPU time were measured to test the efficiency of the proposed algorithm. From the experimental results, it is evident that the proposed algorithm reduces the CPU consumption time to an average of 15% as compared to the Genetic Algorithm, 12% as compared to the Simulated Annealing, and approximately 6% as compared to the Genetic Annealing algorithm.
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