Structures are exposed to dynamic loads such as ground motions. Structures exposed to ground motion may be subjected to deformation if they do not have control systems. Control systems are used in structures to minimize these deformations. These control systems consist of types such as active, passive, semi-active, and hybrid. The application of these control systems varies depending on the type of structure and the ground motion to which it will be exposed. Since many variables are effective in the design of control systems, it is necessary to optimize these systems. Thanks to metaheuristic-based optimization, the best solution can be obtained under an objective function, as long as certain constraints are provided. In this study, optimization studies for active, passive, semi-active, and hybrid control systems are reviewed.
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The design of structural control systems is inherently complex due to a variety of factors that stem from the dynamic and intricate nature of structures and the challenges associated with ensuring their stability, performance, and safety. Structures are subject to dynamic loads such as wind, earthquakes, and traffic. The dynamic behavior of structures introduces complexities in predicting and responding to various forces, making it challenging to design control systems that can effectively mitigate these dynamic effects. Structural systems often exhibit nonlinear behavior under certain conditions. Additionally, uncertainties in material properties, environmental conditions, and external loads make it difficult to model and predict the exact response of a structure. Designing control systems that can accommodate and respond to these nonlinear and uncertain dynamics is a complex task. Structural control systems involve a multidisciplinary approach that requires expertise in structural engineering, control theory, and often other fields such as dynamics, mechanics, and materials science. Integrating knowledge from these diverse domains adds to the complexity of the design process. Effective structural control systems require real-time monitoring of structural behavior and quick decision-making to apply control actions. Implementing sensors, actuators, and control algorithms that operate in real-time poses additional challenges, as delays or inaccuracies can compromise the system's performance. Implementing sophisticated control systems can be expensive, and there is often a need to balance performance requirements with cost constraints. Designers must optimize the system considering both technical and economic factors, adding another layer of complexity to the decision-making process.
Structural systems are often interconnected with various components and subsystems. The interactions between these elements must be carefully considered to avoid unintended consequences or negative feedback loops that could compromise the stability and safety of the structure. Structures may experience varying environmental conditions and loading scenarios over time. Designing control systems that can adapt to changing conditions and remain effective throughout the structure's lifespan requires a comprehensive understanding of the potential challenges and a flexible design approach. Structural control systems must comply with safety standards and regulations. Navigating the regulatory landscape and ensuring that control systems meet the necessary requirements adds an additional layer of complexity to the design process.
The importance of tuning structural control systems cannot be overstated. It plays a crucial role in optimizing performance, mitigating dynamic forces, adapting to changing conditions, minimizing energy consumption, avoiding unintended consequences, meeting performance objectives, complying with regulations, and enhancing the overall lifespan of structures. Proper tuning is a key factor in achieving the desired balance between structural stability, safety, and efficiency.
Optimization refers to the process of planning and analyzing a system, process, or resource using mathematics or computers. Typically, a mathematical model that takes into consideration variables, limitations, and a specified goal is used to carry out this process. Numerous goals can be achieved by optimization, including cutting expenses, boosting productivity, or identifying the best course of action. Optimization is a versatile concept used in many different fields such as engineering, finance, energy, healthcare, and machine learning. Many algorithms have been produced for the optimization process. A few optimization examples can be given such as Genetic Algorithm (GA) (Gen and Cheng, 2000), Big Bang-Big Crunch Algorithm (BB-BC) (Erol and Eksin, 2006), Particle Swarm Optimization (PSO) (Poli et al., 2007), Ant Colony Algorithm (ACO) (Stützle, 2009), Teaching-Learning Based Optimization (TLBO) (Črepinšek et al., 2012), Flower Pollination Algorithm (FPA) (Balasubramani and Marcus, 2014), Jaya Algorithm (JA) (Rao et al., 2016). As with all systems, optimization is essential for control systems including base isolators.