Hybrid optimization based PID control of ball and beam system

Ball and beam is a popular benchmark problem in control engineering. Various control strategies have been proposed on ball & beam system in literature, In this paper, hybrid optimization algorithms have been implemented on PID controller to control ball position and beam angle. Hybrid algorithms combine exploration and exploitation ability of individual algorithm and find optimized value of performance index. In this paper, two hybrid algorithms namely PSO-GSA and PSO-GWO are used to tune controller parameters which in turn improve the system performance. Simulation results show effective and efficient improvement in system performance with these hybrid algorithms. To analyze the performance of these algorithms, time domain parameters and mean square error (MSE) has been taken as performance index. A comparative study of these algorithms with that of individual algorithms namely PSO, GWO, GSA has also been done.

The potential of quantum annealing for rapid solution structure identification

The recent emergence of novel computational devices, such as quantum computers, coherent Ising machines, and digital annealers presents new opportunities for hardware-accelerated hybrid optimization algorithms. Unfortunately, demonstrations of unquestionable performance gains leveraging novel hardware platforms have faced significant obstacles. One key challenge is understanding the algorithmic properties that distinguish such devices from established optimization approaches. Through the careful design of contrived optimization tasks, this work provides new insights into the computation properties of quantum annealing and suggests that this model has the potential to quickly identify the structure of high-quality solutions. A meticulous comparison to a variety of algorithms spanning both complete and local search suggests that quantum annealing’s performance on the proposed optimization tasks is distinct. This result provides new insights into the time scales and types of optimization problems where quantum annealing has the potential to provide notable performance gains over established optimization algorithms and suggests the development of hybrid algorithms that combine the best features of quantum annealing and state-of-the-art classical approaches.

Coupling matrix synthesis of general chebyshev filters

A single optimization algorithm based on SolvOpt that synthesizes coupling matrices for cross-coupled microwave filters is presented. The rules for setting initial values of SolvOpt are proposed to find global minimum of the cost function. SolvOpt method provides faster convergence and higher accuracy to find the final solution compared with hybrid optimization algorithms. Application examples illustrate the excellent performance and the validity of this method.