MS2.1 Advances in Control of Structural Vibrations

The concept of tensairity structure is interesting for numerous applications in several application fields according to the low ratio between self-weight and its loading capacity. The basic components of a tensairity are represented by a cylindrical pneumatic/inflatable element, a couple of cables wrapped around and attached to a slender beam positioned along the generatrix of the cylinder. When the tensairity is loaded, the beam is compressed while the cables are subject to tension thus realizing a very efficient structure. However, one of the main limitations for dynamic applications is due to the low damping ratio which makes the structure susceptible of continuous vibrations. This study explores a new concept of semi-active tensairity that provides a solution to this loss of performance. A reduced-scale prototype has been manufactured according to the tensairity concept in U.S. Patent No. 10,407,939. The main innovations are represented by the employment of NiTiNOL wires wrapped around the pneumatic element, the introduction of manual and automatic tensioning systems at the ends of the slender beams and the adoption of a design that provides the capability of sustaining loads in any direction. The load-displacement curves have been acquired with a MTS testing machine for the tensairity equipped with NiTiNOL and steel wires, respectively, and for different pretension levels. Dynamic experiments have been performed to measure the resonance frequencies of the lowest few modes as function of the pretension levels. The results demonstrate the higher dissipation capacity of the tensairity equipped with shape memory wires whose tensions are semi-actively controlled. Finally, a nonlinear FEM model has been implemented in ABAQUS and parametrized in Python to simulate the static and dynamic tests.

The magnetic suspension control system is the key component of the maglev train to ensure the suspension gap between the maglev train and its guideway at a stable value. In general, the maglev car body is supported by several bogies which consist of two suspension modules. The two suspension modules are decoupled mechanically by a well-designed anti-rolling beam making it the fundamental unit of maglev train. Each suspension module contains two suspension control points which are controlled separately by two individual controllers. The current single-suspension control system neglects the coupling disturbance between the two levitation points causing a conservative dynamic performance. Besides, the most common adopted linear suspension controller nowadays has met the basic requirements of engineering application, but some problems occur in the suspension system during complex working conditions and long-term passenger service which seriously affect the stability and reliability of the suspension system and even cause the partial suspension-point failure of the vehicle, which affects the safety and stability of the maglev train. In this paper, an adaptive multi-agent deep reinforcement learning (MADRL) approach for cooperative control of nonlinear maglev suspension system developed, enabling automatically adjust the control strategy through the interaction with the suspension system. The nonlinear state space of two-point suspension control system is established as an agent environment to interact with the developed MADRL control model. Multi-agent deep deterministic policy gradient (MADDPG) in MADRL is adopted to find the agents which maximize the total reward by resembling the control index for optimal control. The uncertainty factors like mass change and disturbance force in the methodological framework of the maglev system are considered in the established method. The effectiveness of the proposed method is verified by comparing with conventional PID controller through simulation.