International Journal of Control Automation and Systems最新文献

In this paper, aiming at the phenomenon that the dead zone and hysteresis of three-way proportional pressure reducing valve (TPPRV) will seriously affect the control accuracy of construction machinery, a polynomial excitation current compensation controller (PECC) is designed, which is novel and easy to realize in engineering. Firstly, the mathematical model of TPPRV is established, and the dead zone and hysteresis of TPPRV are quantitatively analyzed by using the performance test platform of proportional pressure reducing valve. Secondly, the design principle of PECC is expounded, and the controller model is deduced theoretically. The proposed PECC has two main advantages. One is that the method does not need to establish the nonlinear model of dead zone and hysteresis, and the other is that the method can compensate the dead zone and hysteresis simultaneously. Finally, the compensation control performance of PECC is verified by using the performance test platform of proportional pressure reducing valve. The experimental results show that PECC can greatly reduce the adverse effects of dead zone and hysteresis on TPPRV, and has great applicability under different working conditions. Relevant research results can significantly improve the proportional control accuracy of TPPRV, which has a certain engineering value.

This paper studies the stable degree for profile of networked evolutionary games (NEGs) with disturbances by the semi-tensor product of matrices. Firstly, the algebraic formulation of an NEG with disturbances is established. Secondly, on the basis of algebraic formulation, some concepts about stable profile with disturbances are proposed, including strong k-degree stable and weak k-degree stable, and the corresponding criteria to detect the stable degree are presented. Thirdly, two algorithms for designing controls are given to make the stable profile achieves strong k-degree stable or weak k-degree stable. Finally, an example is given to illustrate the validity of the results.

This paper presents a novel method for path selection by non-expert users in robot trajectory planning using augmented reality (AR). While AR has been used in robot control tasks, current approaches often require manual waypoint specification, limiting their effectiveness for non-expert users. In contrast, our study introduces an innovative AR-based method via a head-mounted display, designed to enhance human-robot interaction by making the process of selecting robotic paths more accessible to users without specialized expertise. The proposed method utilizes the RRT-Connect algorithm to automatically generate pathways from the initial to the goal position, offering choices of 1, 3, or 5 pathways, as well as 3 and 5 pathways with AR text guidance. This guidance provides contextual instructions within the AR environment, displaying the order of pathways from the fewest to the highest number of waypoints. Our findings demonstrate that optimizing the number of AR pathways can reduce user stress and improve operational skills. Path1 exhibited the fastest performance time but had the highest number of obstacle collisions. Methods with AR text guidance showed increased performance time compared to Path1. However, Path3 and Path5 achieved the best balance between performance time and collision avoidance. Qualitative analysis indicated that AR text displays demanded more effort from users. Path3 without AR text guidance was identified as the easiest method for operating the robot. Consequently, Path3 was deemed the most beneficial among the five methods. These results highlight the novelty of our method in enhancing the design of future human-robot interaction systems, focusing on improving efficiency, safety, and user experience for non-expert users using AR interfaces.

The shaftless rim-driven thruster (RDT) has many attractive features and it would improve the fault-tolerance of RDT to select the fault-tolerant permanent magnet motor (FTPMM) as the propulsion motor. The FTPMM works under severe marine environment and subjects to uncertainty and sensorless conditions. To overcome this problem, a sensorless speed tracking control is proposed for the FTPMM with general uncertainties, e.g. perturbing or time-varying parameters, unknown nonlinearities and bounded disturbances. Based on adaptive nonlinear damping, a robust adaptive observer is constructed to estimate the FTPMM speed. The proposed scheme ensures that all the dynamic signals of the closed system are bounded and it is not needed to estimate the uncertain parameters and disturbances of the system. Furthermore, the estimation errors and the tracking errors can converge to arbitrarily small values by adjusting design parameters. The effectiveness of the proposed scheme is verified by the simulation and experiment results. Furthermore, the stability of the control system and the robustness for the parameter uncertainty and load torque disturbance can be guaranteed.

To address the problem of control performance degradation caused by flexible attachment vibrations and external disturbances during attitude maneuvering of flexible spacecraft, this paper proposes a parametric method for improving flexible spacecraft attitude maneuver control (AMC) based on a functional observer. The objective was to enhance the control accuracy and disturbance rejection. First, state expansion was carried out for flexible spacecraft systems affected by disturbances. A functional observer was designed for the system, and sufficient conditions for the existence of the observer were obtained. Furthermore, a controller was designed by using the state information of the observer, which included state feedback and feed-forward compensation. Based on the parametric solution of a class of generalized Sylvester equations (GSEs), the parametric expressions of the controller and observer were established. Finally, a numerical example of a flexible spacecraft proved the effectiveness of the design method. The method can effectively suppress flexible vibrations and external disturbances while also meeting the high-precision control requirements of the flexible spacecraft.

In this paper, we discuss a nonlinear disturbance observer-based double closed-loop control system for managing a wheeled mobile robot (WMR) and achieving trajectory tracking control. The focus of our study is on the topic of trajectory tracking control for WMR in the presence of external disturbances. Our proposed control strategy addresses the two main issues of velocity constraint and external disturbances. Specifically, we employ a kinematic tracking error model to produce the constrained virtual velocity in the outer loop of a neural-dynamic optimization-based model predictive control (MPC). Additionally, we create a nonlinear disturbance observer (NDO) based on the dynamic model to monitor external disturbances. To achieve accurate trajectory tracking for disturbances compensation, we utilize a robust controller. We confirm the stability of our proposed controller using the Lyapunov theory. Finally, two numerical simulation experiments demonstrate the effectiveness and reliability of our proposed controller.

This paper focuses on discussing the stability of time-varying switched impulsive nonlinear systems (TVSINSs). To conquer the difficulties induced by time-varying parameters, we firstly put forward comparison theorem associated with a method that doesn’t involve any Lyapunov function and is commonly utilized in positive systems to set up new stability criteria of TVSINSs under the average dwell time (ADT) switching. In comparison with recent results, the derived ones are not only in connection with state delay and impulse delay, but also possess less conservative conditions from the simulation analyses. In conclusion, the feasibility and superiority of the adopted approaches are testified by two appropriate examples.

This paper studies the problem of prescribed performance tracking control for the lower limb exoskeleton under time-varying state constraints and input saturation. A prescribed performance control method is proposed to achieve the specified tracking accuracy within a preset time. Under the framework of system transformation, the control problem with specified performance and state constraints is transformed into the boundedness problem of auxiliary variables. Meanwhile, the hyperbolic tangent function is employed to deal with the input saturation. Through rigorous theoretical analysis, it can conclude that the tracking error converges to a prescribed region near the origin within a given time and all signals of the closed-loop system are bounded. The superiority of this control scheme is verified through a practical simulation example.

This paper investigates the adaptive finite-time tracking control problem for a class of constrained pure-feedback systems with time-varying delays and unknown initial states. By designing a novel shifting function and using the state transformation, there is no need to know the initial value of states. Instead of employing barrier Lyapunov functions, the modified nonlinear state-dependent functions are constructed to solve the deferred state constraints, avoiding the feasibility conditions on virtual controllers. The effect of time-varying delays is eliminated by combining the radial basis function neural networks with a finite covering lemma, and the requirement that the derivative of time-varying delays should be less than one in Lyapunov-Krasovskii functional method is relaxed. The asymmetric saturation nonlinearity is solved by designing an auxiliary system. The system coordinate transformation is employed to solve the design difficulty brought by the nonaffine structure. Then, an adaptive finite-time tracking control scheme is developed based on the command filtered backstepping technique. The developed control scheme can not only make all states satisfy the asymmetric time-varying constraints after a predefined time, but also guarantee the finite-time tracking performance. Finally, simulation examples are given to demonstrate the effectiveness of the proposed scheme.

This study presents a LookAhead-RAdam gradient iterative algorithm to identify ExpARX models with random missing outputs. The LookAhead-RAdam gradient iterative algorithm is used to optimize the step size of each element and adjust the direction to effectively update the ExpARX model parameter estimation through the estimated outputs. Compared to the classical gradient iterative algorithm, this study improves the estimation accuracy of the missing outputs and the parameter estimation convergence rate by introducing the LookAhead algorithm and RAdam algorithm. To validate the algorithm developed, a series of bench tests were conducted with computational experiments. Finally, the effectiveness of the proposed design approach is demonstrated by a simulation example.