Stepping quantum genetic algorithm-based LQR control strategy for lateral vibration of high-speed elevator

Abstract

To effectively restrain the lateral vibration caused by the guide rail excitation and improve the ride comfort of the car system, a state-weighted linear quadratic regulator (LQR) control strategy is proposed. Firstly, based on the active control model of the 4-DOF car system with actuators distributed diagonally along the center of the car frame, an LQR controller for lateral vibration of high-speed elevator car systems is designed. Furthermore, in view of the tedious and time-consuming of the empirical method to choose state-weighted matrix Q, stepping quantum genetic algorithm (SQGA) is proposed to improve the performance of the controller. Finally, the time-frequency characteristic curves of the lateral vibration acceleration and the vibration displacement of the car system are compared and analyzed by MATLAB to verify the effectiveness of the proposed controller.

Adaptive inverse optimal backstepping control strategy for longitudinal vibration of high-speed elevator system based on fuzzy observer

“…A fuzzy adaptive backstepping inverse optimal control scheme is proposed, and it is proved by stability that this scheme not only ensures that the auxiliary system (22) is semi-globally uniform and ultimately bounded but also ensures the input state stability of the system (8) and finally achieves the minimum of the inverse optimal target functional, which provides a globally effective control method for the complex vibration in the field of high-speed elevator longitudinal vibration equivalent.3. Through MATLAB simulation of two typical stages of high-speed elevator constant speed operation and emergency braking, and comparison with the GA-LQR controller (Li et al, 2022) and elementary state-dependent Ricatti equation (SDRE) controller (Santo et al, 2016), the time–frequency response characteristics of car system vibration acceleration are analyzed, which verifies the effectiveness and progressiveness of the designed controller.…”

“…In this paper, the 7m/s high-speed elevator is taken as the simulation experimental object for numerical simulation analysis. The constant speed running phase (Peng et al, 2020;Sandilo and Horssen, 2014) and the free oscillation phase (Peng et al, 2018;Xu et al, 2022) after emergency braking of the high-speed elevator are selected for numerical simulation analysis under no-load and heavy load conditions, respectively, (the change range of the car mass in this section is defined as ½800kg, 1600kg).To further embody the superiority of the FABI-controller performance, the GA-LQR controller (Li et al, 2022) and elementary SDRE controller (Santo et al, 2016) are used as the comparison controllers. The numerical simulation results of the horizontal vibration of the car system at different stages and working conditions are shown in Figures 3-10.…”

“…3. Through MATLAB simulation of two typical stages of high-speed elevator constant speed operation and emergency braking, and comparison with the GA-LQR controller (Li et al, 2022) and elementary statedependent Ricatti equation (SDRE) controller (Santo et al, 2016), the time-frequency response characteristics of car system vibration acceleration are analyzed, which verifies the effectiveness and progressiveness of the designed controller.…”

Finite Element Modeling and Vibration Control of Plates with Active Constrained Layer Damping Treatment

“…By designing the optimal feedback controller, the objective function J is minimized to: where Q and R are, respectively, the output vector weighting matrix and the control vector weighting matrix, and is the control voltage of the system [ 38 , 39 ]. …”

Optimal Control Strategy of Construction Machinery Motor Considering Genetic Algorithm

“…For traditional PID control strategy, its main feature is that empirical formula method and mathematical method are used to determine parameters and evaluate algorithm performance. This method is easy to fall into local convergence or prematurely terminate state, leading to system oscillation and even serious distortion [17][18]. The traditional solution space cannot completely guarantee that every individual can find an optimal solution.…”