Sliding mode fuzzy control: Theory and verification on a benchmark structure

Abstract

A sliding mode fuzzy control (SMFC) algorithm is presented for vibration reduction of large structures. The rule base of the fuzzy inference engine is constructed based on the sliding mode control, which is one of the non-linear control algorithms. In general, fuzziness of the controller makes the control system robust against the uncertainties in the system parameters and the input excitation, and the non-linearity of the control rule makes the controller more e!ective than linear controllers. For veri"cation of the present algorithm, a numerical study is carried out on the benchmark problem initiated by the ASCE Committee on Structural Control. To achieve a high level of realism, various aspects are considered such as actuator}structure interaction, sensor noise, actuator time delay, precision of the A/D and D/A converters, magnitude of control force, and order of control model. Performance of the SMFC is examined in comparison with those of other control algorithms such as H , optimal polynomial control, neural networks control, and SMC, which were reported by other researchers. The results indicate that the present SMFC is e$cient and attractive, since the vibration responses of the structure can be reduced very e!ectively and the design procedure is simple and convenient.

A Review of Smart Structures Vibration Control Strategies

“…Through an external control force, the structural control mechanism aims to lessen vibration and improve the building's lateral integrity caused by earthquakes or strong winds [28]. To decrease structural vibration in an active control system, it is crucial to building one controller that can transmit the right control signal to the control devices.…”

Self-constructing wavelet neural network algorithm for nonlinear control of large structures

“…Sarbjeet and Datta (2000) applied the sliding mode control strategy to a 20-story 2D frame subjected to a narrow band ground excitation and report more reduction in displacements compared with conventional linear control strategies such as LQR. Combing the concept of fuzzy logic (Kodogiannis et al, 2013;Rigatos, 2013;Yan and Ma, 2013;Fougères and Ostrosi, 2013) with SMC, Kim and Yun (2000) proposed a fuzzy sliding mode control (FSMC) for a three-story benchmark building considering actuator-structure interaction, sensor noise, actuator time delay, precision of the analog-todigital (A/D) and digital-to-analog (D/A) converters, control force saturation range, and order of the control model. They report improved performance for FSMC compared with other control algorithms such as H 2/1 control, optimal polynomial control, neural networks-based control, and SMC.…”

Sliding mode control of wind-induced vibrations using fuzzy sliding surface and gain adaptation

“…Since SMC provides a stable and fast control, and the fuzzy logic provides the ability to handle a nonlinear system (see Tong & Li, 2003), many research works have been carried out in designing SMC with fuzzy logic, so-called fuzzy SMC (FSMC) (Kim & Yun, 2000;Wang & Lin, 2007). The chattering problem is reduced in most of these FSMC systems.…”