This paper presents a data-driven H-infinity controller for active vibration control in structural systems having saturated actuators. The data-driven approach addresses parameter uncertainties by eliminating the need for system identification. The full-block S-procedure is used to formulate a convex optimization problem in the form of linear matrix inequalities (LMIs), though additional constraints may introduce conservatism. To mitigate this, the dilation technique with non-common Lyapunov matrices is employed, reducing conservatism and achieving a 12.7% lower H-infinity norm compared to common Lyapunov matrices. A seismically excited three-storey structure is used to validate the method. Simulations based on real-time data from the Kobe earthquake show that the proposed synthesis effectively reduces vibrations while control inputs never become saturated.

Piezoelectric stack-actuated serial kinematic nanopositioning stages are widely utilized in nanopositioning applications but are plagued by challenges such as hysteresis, creep, and mechanical resonance, which degrade system performance. Closed-loop control, particularly positive position feedback (PPF) control, has shown the potential in mitigating these issues and achieving robust nanopositioning. This study focuses on evaluating the performance of PPF control in nanopositioning, specifically considering closed-loop stability. To address the inherent time delay effects in piezoelectric stack actuated nanopositioners, a PPF controller is designed to achieve stable and robust operation. The impact of time delay in flexure nanopositioners is analyzed through simulation-based frequency response analysis, revealing the relationship between the period of the peak-to-peak of the error signal simulation and the performance of the PPF controller. The study demonstrates that a gain of 7.84 dB is required for the PPF controller with delay to become unstable. The design methodology incorporates second-order Padé approximations, allowing the system to be represented by eight poles. Among these poles, five are determined by the controller's parameter design, while the remaining three are influenced by the system's delay. To ensure desirable system behavior, the five designed poles are positioned closer to the imaginary axis compared to the three poles introduced by the delay. The analysis identifies an upper limit of$\tau=342us$for the permissible delay, beyond which the poles introduced by the delay surpass some of the designed poles' proximity to the imaginary axis. This situation undermines the dominance of the designed poles and compromises system performance. The findings emphasize the critical relationship between the error signal simulation and the performance of the PPF controller. This study provides valuable insights for improving controller design and ensuring stable nanopositioning systems. The results also highlight the importance of addressing time delay effects in flexure nanopositioners to achieve robust and reliable performance.

Unmanned underwater vehicles are crucial for deep-sea exploration and inspection without imposing any danger to human life due to extreme environmental conditions. But, designing a robust controller that can cope with model uncertainties, external disturbances, and time delays for such vehicles is a challenge. This paper implements a sliding mode position control algorithm with a time-delay estimation term to a remotely operated underwater vehicle to deal with disturbances, such as waves, and time delays. The controller is implemented on an underwater vehicle (BlueRov) and compared with a proportional-integral-derivative (PID) controller in a wave tank with different disturbances and when there exist delays within the communication channel. The experimental results show that the proposed control method provides better performance than the conventional PID in the presence of extreme disturbances with less control efforts.