Herein we provide a framework for this type of virtual program and describe our experience.Ridge extraction is an effective tacholess order tracking technique for the fault detection of bearings under time-varying speed conditions. Cost function ridge detection (CFRD) is the most widely used ridge detection method. However, improper bandwidth selection and unreasonable cost function construction significantly restrict the performance of the CFRD. To address the two shortcomings of the CFRD, an improved CFRD (ICFRD) method is firstly proposed in this paper. The ICFRD integrates an adaptive search bandwidth determination technique that varies the search region with signal signatures, as well as a novel cost function that comprehensively considers the trade-off between ridge amplitude and smoothness. An iterative characteristic ridge extraction (ICRE) strategy is then presented based on the ICFRD to extract multiple characteristic ridges in a time-frequency plane automatically. The average frequency ratios between the extracted characteristic ridges are calculated to estimate bearing fault characteristic orders and therefore detect bearing faults. The performance of the proposed method was tested using simulated signals and experimental vibration signals collected from a machinery test rig. Results show that the ICRE outperforms the conventional CFRD in terms of detecting bearing faults under variable speed conditions. The average relative errors between the extracted instantaneous frequencies and the theoretical ones of the ICRE are 0.85%, 2.11% and 0.63% for inner race fault, outer race fault, and healthy bearing vibration signal, respectively. These values are much smaller than the results of using the CFRD.This paper addresses the robust stochastic finite-time and fixed-time chaos synchronization of two permanent magnet synchronous motors (PMSMs) in noise environment. The novel adaptive finite-time and fixed-time control schemes are implemented, respectively, which can not only ensure that the stochastic chaos synchronization of PMSMs can be achieved in a fast rate, but also determine the control gains successfully(not necessary to set them in advance). The sufficient conditions are derived in the light of the stochastic finite-time and fixed-time stability theories, where the upper bound of synchronization time can be estimated. Furthermore, the stochastic fixed-time synchronization can get rid of the dependence of initial conditions in PMSMs, which overcomes the critical deficiency of stochastic finite-time synchronization of PMSMs. Finally, simulation results demonstrate the validity of proposed theoretical analysis with comparisons.High energy efficiency and tracking accuracy are both vital for hydraulic lifting servo systems. However, the appropriate hardware configuration and its corresponding nonlinear controller is always a problem to be solved. To address this multi-objective task, an integrated energy-saving and position tracking controller is developed. Specifically, to reduce the substantial energy dissipation, a system configuration referring to the structure of the three-position-six-way valve is proposed. To ensure accurate tracking performance, a hybrid observer-based output feedback controller is developed. https://www.selleckchem.com/products/bpv-hopic.html By doing so, the function of flow regeneration and the property of flow matching are achieved, and the tracking accuracy is guaranteed by using only position signal. To validate the effectiveness of the proposed method, a common Lyapunov function is used to prove the stability of the multi-model system, and case studies are conducted to demonstrate the system performance.The low-speed high-torque permanent magnet synchronous motor (PMSM) drive system is a kind of typical nonlinear, strong-coupling and easy-parameter perturbation electromechanical coupling system. The control system is uncertainties and subject to unknown external interferences as well. In this paper, a disturbance rejection control method combining robust speed controller and load observer is proposed for low-speed high-torque PMSM. The robust speed controller combines the composite nonlinear feedback (CNF) which has advantage in improving the transient responsive performance and the integral sliding mode (ISM) advancing in improving system robustness. Subsequently, the effects of unknown external interferences are avoided by using a sliding mode observer (SMO), in which the chattering is reduced by introducing fuzzy control, and the observation is used for feed-forward compensation. The proposed robust speed controller solves the contradiction between the rapidity and overshoot of the traditional control method, and combines the load observer to compensate the influence of the load mutations and wide range of the load changes on the control system. Finally, the numerical simulation and experiments demonstrate that the proposed speed control method is able to achieve good transient performance in inhibiting system overshoot and reducing stable state error. Additionally, it successfully suppresses the influence of load disturbances and mutations, and shows the proposed method has better robustness.This paper investigates the problem of using multiple microsatellites to control the attitude of a target spacecraft losing control ability. Considering external disturbance and unknown system dynamics, a data-driven robust control method based on game theory is proposed. Firstly, the attitude takeover control of the target using multiple microsatellites is modeled as a robust differential game among disturbance and multiple microsatellites, in which microsatellites can obtain the worst-case control policies. Subsequently, policy iteration algorithm is put forward to acquire the robust Nash equilibrium control policies of microsatellites with known dynamics, which is a basis of data-driven algorithm. Then, by employing off-policy integral reinforcement learning, a data-driven online controller without information about system dynamics is developed to get the feedback gain matrices of microsatellites by learning robust Nash equilibrium solution from online input-state data. To validate the effectiveness of the proposed control method, numerical simulations are provided.