Xiang Wang

In the application of multimotor driving systems, achieving high control performance while reducing the occupation of communication networks holds practical significance. This article proposes an event-triggered fixed-time command filtered control strategy that not only facilitates load tracking and motor synchronization but also conserves communication resources. In tracking control design, the fixed-time control is incorporated into the backstepping procedure to accelerate convergence rate and improve tracking precision. Command filters are utilized to reduce computational complexity, and a compensation mechanism with double powers is constructed to eliminate filtering errors. In synchronization control design, a practical grouping control approach tailored for four motors is proposed, with control inputs superimposed on current commands to quickly achieve speed synchronization and reduce mechanical abrasion. Subsequently, an event-triggered mechanism is designed for the final composite control signal, which greatly conserves communication resources while maintaining desirable performance. The closed-loop system is proven to be practical, fixed-time stable, and free of Zeno behavior. Experiments conducted on a four-motor driving turntable demonstrate the effectiveness of the proposed strategy.

This brief proposes a predefined-time command filtered control strategy for multi-motor servo systems to facilitate high-performance tracking and synchronization. For load tracking, a predefined-time control approach is integrated within the backstepping framework, which accelerates the convergence rate and simplifies the adjustment of the convergence time. Command filters are utilized to obtain the derivatives of virtual control signals, and a compensation mechanism is developed to reduce filtering errors. Furthermore, a fuzzy logic system is constructed to estimate and counteract the frictional effect. For motor synchronization, a practical grouping control approach is designed for four motors, with control inputs overlaid on command signals of motor currents to rapidly realize synchronization of motor speeds. The predefined-time stability of the closed-loop system is demonstrated. The practicality and effectiveness of the proposed strategy are validated by experimental results.

In multimotor servo systems, load tracking and motor synchronization are two key control objectives, necessitating the high-performance control design accompanied by high-precision measurements of position, speed, and current. This paper presents a fixed-time command filtered control strategy based on full-state measurements to simultaneously realize position tracking and speed synchronization. In the tracking control design, command filters are introduced to approximate derivatives of virtual control signals to avoid the “explosion of complexity” problem. An improved compensation mechanism with double fractional powers is constructed to further eliminate filtering errors. A fixed-time control approach is utilized to accelerate the convergence process and improve the tracking accuracy. In the synchronization control design, a practical grouping control is proposed to cooperate with the backlash elimination method, and the control inputs are superimposed on current commands to quickly attain speed synchronization. The fixed-time convergence of both the tracking error and the synchronization error is proved through Lyapunov theory. Experiments are conducted on a four-motor servo platform, and the obtained results demonstrate the effectiveness and practicality of the proposed strategy.

In multimotor driving systems, inertia is one of the key parameters, and its variations can severely affect the control performance. This paper proposes a novel adaptive discrete-time variable-power super-twisting control (ADVSC) to address this issue. To cater to practical digital control systems, a characteristic model is built in discrete-time domain with fewer parameters and lower order, which decreases the complicacy of common dynamics models. The model parameters are acquired through online identification utilizing input and output data, which diminishes uncertainties and unknown dynamics. The ADVSC is then designed based on the characteristic model to meet practical performance requirements. Specifically, a variable-power sliding function is constructed to suit different tracking error intervals and accelerate the convergence process, and the super-twisting algorithm is further utilized to enhance robustness and meanwhile alleviate chattering. One attractive advantage of this strategy lies in that inertia variations can be reflected by time-varying parameters in the characteristic model using online identification, which endows the designed ADVSC with good adaptability. The stability of the closed-loop system is proved via the Lyapunov theory. Experiments are carried out on a multimotor servo control platform with adjustable load, and the obtained results validate the efficacy and practicality of the proposed strategy.

A dual‐motor servo mechanism is a high‐order and strong‐coupling system with unknown nonlinearity, which brings challenges to controller design to realize high‐performance tracking and synchronization. This article proposes a finite‐time command filtered control strategy to address this problem. In tracking control design, finite‐time command filters are adopted to obtain the derivatives of virtual controllers, and the “explosion of complexity” problem in backstepping is thus solved. An improved compensation system is designed to reduce filtering errors. The tracking control is developed based on the combination of finite‐time control and command filtered approach. Moreover, to deal with unknown nonlinearities and uncertainties, fast finite‐time extended state observers are developed to observe lumped disturbances for the load and motor, respectively. In synchronization control design, two opposite control actions are developed using the speed difference to force two motors to rotate synchronously. The finite‐time convergence of the tracking and synchronization errors is proved. The efficiency of the proposed control strategy is verified via experiments conducted on a dual‐motor servo turntable.

With the popularization of computerized digital control systems, the discrete-time control design has attracted increasing attention. This article presents a novel discrete-time second-order multipower terminal sliding-mode control (DSMTSC) for the position tracking of multimotor driving servo systems. Instead of the traditional dynamic model, a discrete-time characteristic model, which possesses lower order and fewer parameters, is built based on the system input–output data. In the DSMTSC design, a multipower sliding function is constructed fitting different convergence stages, which achieves faster convergence rate and higher tracking precision than conventional terminal sliding functions. Furthermore, an improved reaching law is designed to avoid the integral saturation, and the high-order sliding-mode approach is utilized to reduce the chattering. The finite-step boundedness of both the sliding functions and the tracking error is proved by rigorous theoretical analysis. Comparative experiments are conducted on a four-motor driving servo turntable, and the results verify the advantages of the DSMTSC, which are particularly suitable for practical application.

In gear transmission servo systems, backlash effect and inertia variation often generate low precision, oscillations or even affect the stability. Focusing on this issue, an adaptive terminal integral sliding mode control (ATISMC) strategy and a discrete linear extended state observer (DLESO) are proposed in this article. First, different from using traditional dynamic model, a characteristic model is established with online parameter identification to describe inertia variation. Then, a DLESO is proposed to observe and compensate the modeling error caused by parameter identification and backlash effect. An ATISMC is newly designed based on the characteristic model to suppress the uncertainties and stabilize the whole closed-loop system. Both the uniform ultimate boundedness of observation error and the practical finite-time stability of the system is proved. Finally, simulation and experimental results demonstrate that the proposed strategy can adapt to large-range inertia variation and suppress the backlash effect.

Accurate quantification of safety is essential for the design of autonomous systems. In this paper, we present a methodology to characterize the exact probabilities associated with invariance and recovery in safe control. We consider a stochastic control system where control barrier functions, gradient-based methods, and barrier certificates are used to constrain control actions and validate safety. We derive the probability distributions of the minimum and maximum barrier function values during any time interval and the first entry and exit times to and from any super level sets of the barrier function. These distributions are characterized by deterministic convection-diffusion equations, and the approach used is generalizable to other safe control methods based on barrier functions. These distributions can be used to characterize various quantities associated with invariance and recovery, such as the safety margin, the probability of entering and recovering from safe and unsafe regions, and the mean and tail distributions of failure and recovery times.

The frost heave and thawing settlement in the seasonal frozen areas can cause the track geometry change, even seriously affect the driving safety. Taking the HSR-A railway as the research object, this paper divides the change process of track surface during the period of frost heave and thawing settlement into four stages, and equates the four stages into a trapezoidal change process. Then the characteristics of these stages are summarized. Finally, based on the track geometry measurements (TGM), two different linear functions are used to represent the track surface changes before and after frost heave start respectively, and this essay proposes the start time estimations model(STEM) to estimate the start time of frost heave. STEM is applied to 10 typical frost heave positions in the HSR-A. The results show: the estimated effect is good.