Transactions of the Institute of Measurement and Control

Bi, Hui; Zou, Tao; Wu, Lihua

doi: 10.1177/01423312241242845pmid: N/A

This paper introduces an adaptive neural network compensatory control approach designed for a 2-degree-of-freedom (2-DOF) helicopter system facing challenges such as input backlash and state constraints. The proposed methodology leverages a radial basis function (RBF) neural network to effectively approximate system uncertainties, mitigating the impact of nonlinear dynamics on control performance. To address the presence of nonlinear input backlash, a compensation technique is introduced to enhance the smoothness of input signals. In addition, for enhanced system safety, a barrier Lyapunov function is integrated to impose restrictions on position and velocity states, resulting in constrained control. Through a rigorous analysis using the Lyapunov direct method, this paper demonstrates the effectiveness of the proposed approach in achieving bounded stability of the system. The validation of the approach is further established through the presentation of simulation and experimental results, showcasing its effectiveness and feasibility in real-world applications.

Fiuzy, Mohammad; Shamaghdari, Saeed

doi: 10.1177/01423312241233622pmid: N/A

This paper investigates a type of robust observer-based tracking control for the well-known inverted pendulum as an uncertain fractional-order system with two-norm bounded uncertainties subject to input saturation along with external disturbance that focuses on the case of a fractional-order α such that 0<α<1. The main goal is to design a robust model following tracking control servomechanism by the side of stability analysis. This paper addresses the well-known quadratic Lyapunov function in addition to the Gronwall–Bellman lemma and the sector condition of the saturation function stability synthesis. This paper proposes an outstanding strategy to achieve the best model following robust observer in the cast of output-feedback control subject to input saturation in the framework of an uncertain fractional-order system based on the linear matrix inequality (LMI) solution. The LMI procedure can be used to achieve static output-feedback tracking control and observer gain. Several valuable theorems support this approach, and various simulation scenarios are available to showcase its effectiveness.

Lu, Yang; Pang, Guochen; Liu, Yang; Zhang, Ancai; Qiu, Jianlong; Cao, Jinde

doi: 10.1177/01423312241239233pmid: N/A

This paper deals with the problem of event-triggered control (ETC) for vertical take-off and landing (VTOL) aircraft systems under actuator saturation. By introducing the VTOL system with saturated actuators, a composite quadratic Lyapunov function (CQLF) approach is first proposed to describe the problem of the event triggering mechanism. The proposed transfer mechanism not only further saves communication resources, but also further suppresses the conservatism of the attraction domain estimation. Then, sufficient conditions are established via linear matrix inequality (LMI) by incorporating new event triggering mechanism condition and prescribed passive performance metrics under the closed-loop system that is asymptotically stable and strictly passive, respectively. Finally, the method proposed in this paper can effectively reduce the number of triggers and expand the domain of attraction. The validity and feasibility of the obtained results are demonstrated by two example simulations.

Liang, Jianguo; Duan, Yujie; Wen, Xinyu; Zhao, Yinan; Gao, Haifeng; Zhao, Xiaodong; Emmanuel, Uwayezu

doi: 10.1177/01423312241239113pmid: N/A

Constant tension control is essential for excellent winding quality. However, the system’s nonlinearity and external disturbance make it challenging to guarantee tension control accuracy with conventional control methods. Thus, a self-coupling fractional-order proportional–integral–derivative (SC-FOPID) control scheme combined with a disturbance observer is proposed to enhance the system’s anti-vibration performance. The fractional-order dynamic model of the unwinding roller and swing rod is established by analyzing the tension mechanism. Based on deliberate analysis and calculation, the vibration shock signal can be decomposed into periodic sinusoidal disturbance and bounded noise approximately. As such, an output-based anti-vibration method using a fractional-order model can be realized, where a back recursive disturbance observer is designed to estimate the periodic component. Simultaneously, the bounded noise exhibited in vibration can be attenuated by the SC-FOPID controller. The stability is guaranteed using the Lyapunov theorem, and the simulation results show the proposed method’s effectiveness in improving the tension control performance.

Wang, Bingsen; Dong, Jiuxiang

doi: 10.1177/01423312241239716pmid: N/A

A technology based on Kalman filtering method combined with multi-channel gain training reinforcement learning for uncalibrated camera visual servo tasks is proposed in this paper. First, a dynamic system with state variables formed from the elements of the image Jacobian matrix is constructed to describe the mapping relationship between two-dimensional images and three-dimensional poses. Kalman filter is used to estimate the state variables of the constructed system online. Next, the Jacobian matrix estimation and depth determination strategy gradient (DDPG) methods are combined to jointly train multi-channel gains by setting a reasonable segmented reward and punishment mechanism. Through training, a more effective gain decision can be obtained. The robustness of Kalman filtering to interference to a certain extent reduces the precise dependence of reinforcement learning models, thereby achieving higher robustness in intelligent visual servo control. Finally, the effectiveness and advantages of the Kalman-DDPG method have been demonstrated through simulation comparison and six-degree-of-freedom (DOF) uncalibrated manipulator physical experiments.

Niu, Shuqi; Zhang, Zhao; Zhou, Hongyan; Chen, Xue-Bo

doi: 10.1177/01423312241239229pmid: N/A

Power load forecasting is an important part of modern smart grid operation management. Accurate forecasting guides the efficient and stable operation of the power system. In this paper, a fuzzy C-means clustering algorithm and an improved locally weighted linear regression model are proposed for short-term power load forecasting. First, the fuzzy C-means clustering algorithm is used to cluster the power load. Make the power consumption behavior of load data of the same category similar and use the power consumption load data of the same category as the training sample. Then, to solve the problem of large calculation and insufficient fitting of the locally weighted linear regression model, the k-nearest neighbor range constraint is introduced into the model for daily load forecasting. Finally, the effectiveness of the method is verified by a simulation example. Experimental results show that this method can effectively improve the accuracy and generalization ability of power load forecasting compared with other methods.

Chen, Peng; Cheng, Yun; Yuan, Yinlong; Hua, Liang

doi: 10.1177/01423312241242514pmid: N/A

In the textile industry, yarn tension control is directly related to product quality and production efficiency. Addressing the nonlinearities, uncertainties, and external disturbances encountered by tension control systems, this work initially establishes a mathematical model to describe the dynamic characteristics of yarn tension. Building upon this foundation, a yarn tension sliding mode control strategy based on an extended state observer (ESO) is proposed. This strategy employs the ESO to estimate and compensate for system uncertainties and disturbances. Subsequently, a sliding mode controller is designed for the compensated system, utilizing hyperbolic tangent functions for the reaching law, thereby enhancing the dynamic performance of the yarn tension control system. The stability of the controller is analyzed using Lyapunov theory. Recognizing the complexity of controller parameter tuning, an improved grey wolf optimizer (GWO) is introduced for further adjustment and optimization of the controller parameters. Finally, comparative simulations demonstrate that the designed controller maintains rapid response and high-precision dynamic characteristics even in the presence of external disturbances and noise. This underscores the promising application prospects of the proposed method in practical systems.

Liu, Yuhang; Wei, Chen; Duan, Haibin; Yuan, Wanmai

doi: 10.1177/01423312241239484pmid: N/A

An improved active disturbance rejection control (ADRC) algorithm is proposed in this paper to enhance the heading control capabilities of unmanned surface vehicles (USVs) under wind and wave disturbances. The algorithm introduces two enhancements: parameter tuning and fitting, alongside the optimization of the nonlinear function in the ADRC algorithm. First, the parameter tuning employs an improved pigeon-inspired optimization (PIO) algorithm, which encompasses two strategies: the adaptive strategy and the wandering strategy. Parameter fitting ensures discretely optimized value transition into a continuous state, allowing dynamic parameter adjustments. Second, the optimization of the nonlinear function uses the D-value fitting method. Overall, the improved ADRC algorithm significantly enhances response speed to heading control commands for USVs, fortifying their resistance against wind and wave disturbances. Our proposed algorithm provides a new approach to achieve precise USV heading control.

Hernandez-Gonzalez, Miguel; Basin, Michael

doi: 10.1177/01423312241247092pmid: N/A

This paper proposes a predefined-time convergent stabilization controller for a class of nonlinear systems. First, a controller is introduced for linear systems where the convergent time is selected in advance, independent of initial conditions, and can be assigned as a parameter of a control input. The corresponding result is then obtained for a multidimensional system consisting of n nonlinear differential equations. The block control technique is employed to consequently stabilize each state component by a virtual control until the real control appears. Finally, the presented result is applied to a multivariable nonlinear system to design a predefined-time convergent robust controller. To show effectiveness of the proposed convergent controller, numerical simulations have been carried out for a Van-der-Pol system and a 2-degree-of-freedom (2-DOF) helicopter model.

Thilagamani, Velusamy; Sakthivel, Rathinasamy; Satheesh, Thangavel; Mohammadzadeh, Ardashir; Sasirekha, Rathinasamy

doi: 10.1177/01423312241245457pmid: N/A

This study explores the problem of finite-time resilient control for periodic piecewise polynomial time-varying systems in the face of parameter uncertainties, time-varying state delays and external disturbances. Particularly, the considered system is characterized by dividing the fundamental period of periodic systems into numerous subintervals, each of which can be expressed by using matrix polynomial functions. The foremost intention of this work is to lay out a resilient controller such that the resulting closed-loop system is finite-time bounded and satisfies a mixed H∞ and passivity performance index. Furthermore, by constructing a periodic piecewise time-varying Lyapunov–Krasovskii functional, a delay-dependent sufficient condition is established in line with Wiritinger’s inequality and matrix polynomial lemma to guarantee the needed outcomes of the system under study. Following this, the gain matrix of the devised controller can be calculated by solving the established constraints. As a final step, we conclude with a numerical example that validates the potential and importance of the theoretical discoveries and the developed control scheme.