Abstract
Through combining P-type iterative learning (IL) control, model-free adaptive (MFA) control and sliding mode (SM) control, a robust model-free adaptive iterative learning (MFA-IL) control approach is presented for the active vibration control of piezoelectric smart structures. Considering the uncertainty of the interaction among actuators in the learning control process, MFA control is adopted to adaptively adjust the learning gain of the P-type IL control in order to improve the convergence speed of feedback gain. In order to enhance the robustness of the system and achieve fast response for error tracking, the SM control is integrated with the MFA control to design the appropriate learning gain. Real-time feedback gains which are extracted from controllers construct the basic probability functions (BPFs). The evidence theory is adopted to the design and experimental investigations on a piezoelectric smart cantilever plate are performed to validate the proposed control algorithm. The results demonstrate that the robust MFA-IL control presents a faster learning speed, higher robustness and better control performance in vibration suppression when compared with the P-type IL control.
Highlights
As an intelligent control strategy, iterative learning (IL) control has a simple structure and doesn’t require accurate system modeling
Research on stopping criteria based on evidence theory in this paper involves extracting real-time feedback gains from each controller in the vibration control system, constructing the frame of discernment, choosing appropriate feature vectors that describe the learning process of the robust model-free adaptive (MFA)-IL algorithm, calculating the basic probability assignment (BPA) based on the input signals of actuator, forming the fused BPAs using combination rule and diagnosing the learning states of the control method based on the BPA results
As long as the locations and sizes of actuators and sensors are chosen appropriately, both P-type IL control and robust model-free adaptive iterative learning (MFA-IL) control can effectively suppress structural vibration when the piezoelectric smart plate is excited by its first natural frequency
Summary
As an intelligent control strategy, iterative learning (IL) control has a simple structure and doesn’t require accurate system modeling. Model free adaptive (MFA) control, as an effective data-driven control method, is an attractive technique which has gained a large amount of interest in recent years It is implemented, with small computational burden for its simple structure and strong robustness. Time-varying and uncertain systems, neural network approaches have an excellent approximation ability, and fuzzy logic control possesses remarkable robustness and adaptability, the tuning of numerous parameters and complex rules may decrease the efficiency and possibility of these methods [33]. By using information fusion technology, the learning processes of all controllers can be diagnosed in real-time by the real-time feedback gains obtained from the controllers On this basis, the stopping criteria were designed for overlearning diagnosis of the robust MFA-IL algorithm.
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