Cascade PID controller optimization using bison algorithm

Meta-heuristic algorithms are reliable tools for modern optimization. Yet their amount is so immense that it is hard to pick just one to solve a specific problem. Therefore many researchers hold on known, approved algorithms. But is it always beneficial? In this paper, we use the meta-heuristics for the design of cascade PID controllers and compare the performance of the newly developed Bison Algorithm with well-known algorithms like the Differential Evolution, the Genetics Algorithm, the Particle Swarm Optimization, and the Cuckoo Search. Also, in the proposed approach, the controller parameters were encoded to increase the chance of reducing the controller structure, and thus facilitate the automatic selection of its configuration. The simulations were performed for three different control problems and checked whether the use of cascade structures could bring significant benefits in comparison to the use of classic PID controllers. © 2020, Springer Nature Switzerland AG

Truss geometry and topology optimization with global stability constraints

In this paper, we introduce geometry optimization into an existing topology optimization workflow for truss structures with global stability constraints, assuming a linear buckling analysis. The design variables are the cross-sectional areas of the bars and the coordinates of the joints. This makes the optimization problem formulations highly nonlinear and yields nonconvex semidefinite programming problems, for which there are limited available numerical solvers compared with other classes of optimization problems. We present problem instances of truss geometry and topology optimization with global stability constraints solved using a standard primal-dual interior point implementation. During the solution process, both the cross-sectional areas of the bars and the coordinates of the joints are concurrently optimized. Additionally, we apply adaptive optimization techniques to allow the joints to navigate larger move limits and to improve the quality of the optimal designs