Transactions of the Institute of Measurement and Control

Hu, Zhentao; Tian, Liuyang; Hou, Wei; Yang, Linlin

doi: 10.1177/01423312231157120pmid: N/A

To improve the accuracy of multiple target tracking in the clutter environment, a new joint probabilistic data association (JPDA) algorithm based on variational Bayesian adaptive moment estimation is proposed. First, considering the existence of measurements, the posterior distribution of the target state in JPDA is composed of two parts of probability weighting, that is, the posterior distribution of the target state that the real measurement exists in the association gate and the posterior distribution of the target state that the real measurement does not exist in the association gate. By combining the conjugate properties of the prior and posterior distributions, the prior distributions of the target state in the two cases are classified to provide more accurate a priori information to filter, so as to improve the accuracy of data association. Second, considering the coupling effect between state estimation and data association process, combined with variational Bayesian inference, the problem of minimizing Kullback–Leibler divergence is transformed into the problem of maximizing the evidence lower bound, thereby effectively measuring the distance between the posterior distribution of target state estimation and the real posterior distribution, so as to improve the accuracy of data association again from the perspective of optimizing nonlinear filter. Finally, the adaptive momentum estimation strategy is introduced to iteratively solve the variable distribution that meets the maximization of the evidence lower bound, and the optimization of the posterior distribution of the target state is completed. Theoretical derivation and simulation experiments are conducted to verify the feasibility and effectiveness of the algorithm.

Wang, Shiqi; Li, Hui; Li, Hua; Shi, Huiyuan; Sun, Qiubai; Li, Ping

doi: 10.1177/01423312231190194pmid: N/A

A robust dynamic output feedback predictive control approach is developed for a discrete system with time-varying delays, unknown external disturbances, and unmeasurable states. First, the discrete system is transformed into an incremental state deviation model. Based on this model, a novel tracking deviation feedback model is established by extending the output tracking error. Then, a robust predictive control law, possessing more degrees of freedom, is designed. The closed-loop model is further given in conjunction with the feedback model. Second, by using the linear matrix inequality (LMI) method, relaxation technique, and variable transformation method, a less conservative stability condition is given in LMI form, which allows the controller to tolerate a greater range of time-varying delays. The gains of the control law are acquired by solving the stability condition, and the control performance can be significantly enhanced. Finally, by utilizing the TTS20 water tank as a simulation case, the viability and effectiveness of the proposed method are demonstrated.

Lian, Lian; Tian, Zhongda

doi: 10.1177/01423312231190965pmid: N/A

The problem of unknown input observer design for discrete-time nonlinear generalized Markov jump systems is studied. First, like a normal system, the whole nonlinear system is transformed into a local linear system, and then a large number of linear system theories can be applied to solve related problems. Second, in the observer design of general discrete-time Markov jump systems, only the unknown input in the state equation is usually considered. In this paper, the unknown input is considered in both the state equation and the output equation. The state estimation error system is derived by defining the error. The non-uniform Lyapunov functional is selected to stabilize the estimation error system using the Lyapunov theory. The sufficient conditions for the stability of the system are obtained and transformed into the feasibility problem of linear matrix inequality. The problem of unknown input observer design for discrete-time nonlinear generalized Markov jump systems is solved using MATLAB software. Finally, a numerical example of two rules and two modes is used to verify the effectiveness and feasibility of the proposed unknown input observer.

Zhang, Ruicheng; Li, Pengfei; Liang, Weizheng

doi: 10.1177/01423312231189810pmid: N/A

In order to address the problem that the main drive system of rolling mill is easily affected by the impact of biting steel, and considering the nonlinear friction damping and the external perturbations of the main drive system of rolling mill during the rolling process, a fault model of the main drive system of rolling mill is established, and a fault diagnosis and fault tolerance control method of the main drive system of rolling mill based on the nonlinear sliding-mode observer is proposed. In order to suppress the influence of external perturbations on fault diagnosis, a nonlinear sliding-mode observer is constructed for fault diagnosis and fault reconfiguration of the system, and the robustness of the observer to fault reconfiguration is improved by using the sliding-mode control rate v, and the stability of the designed nonlinear sliding-mode observer is proved using Lyapunov’s stability theorem. In order to ensure that the system can operate normally even after a fault occurs, a reference model is designed, and a new controller is redesigned for fault-tolerant control of the system by adding a fault compensation term to the original control scheme using fault estimation information. Through the simulation study of the main drive system of stand F4 of 2030 mm cold rolling mill, it is verified that the observer can accurately track the system state with an angular velocity error of 2.45% and detect and estimate the main drive system failure of rolling mill with an estimation error of no more than 0.04% after a fault occurs; the fault-tolerant control of the main drive system of rolling mill is carried out by using the fault information to restore the system to its normal state, and the angular velocity error is 1.89%.

Hu, Liyao; Duan, Guangren

doi: 10.1177/01423312231183610pmid: N/A

This paper considers the adaptive guaranteed cost tracking (AGCT) control problems for two classes of high-order nonlinear systems with unknown parameters. A new local smooth nonlinear function (LSNF) is introduced first, which provides an important mathematical tool for our controller design. Then, based on the LSNF and the high-order fully actuated (HOFA) system approaches, the AGCT controller is designed for HOFA system with unknown parameter, which guarantees that all of the states of the closed-loop HOFA system are globally bounded, and the tracking error is asymptotically convergent. Moreover, the upper bound of cost function (UBCF) characterizing the tracking performance can be arbitrarily preseted, and is completely independent of the system initial value and the unknown parameter, which significantly improves the tracking performance, and is difficult to achieve by using existing guaranteed cost control (GCC). Furthermore, an extra result, the AGCT controller for a class of strict-feedback systems with high-order form and unknown parameters, is obtained in this paper, which also guarantees that the system tracking error is globally asymptotically convergent with the arbitrarily preseted UBCF characterizing the tracking performance. Three simulation examples, including an inverted pendulum, are presented to show the effect and the superiority of the proposed method.

Yin, Ming; Tian, Jiayi; Zhu, Dan; Wang, Yibo; Jiang, Jijiao

doi: 10.1177/01423312231195365pmid: N/A

Process monitoring technology can help make the right decisions in manufacturing, but the complexity and scale of modern process industry processes render process monitoring difficult. Existing data-driven process monitoring methods utilize abundant monitoring data that are accumulated in industrial processes, but nonlinearity, high coupling, noise effects, and other problems continuously appear in process industry monitoring data. This study proposes a process monitoring method based on variational autoencoder and long short-term memory techniques. The method reconstructs the monitoring data by learning their distribution and time series characteristics under the controlled state, and then it monitors the state of the manufacturing process in real time by calculating the statistics. Evaluation is conducted using the Tennessee Eastman process case verification and experimental comparison method. Then, the proposed method is compared with the centralized process via principal component analysis and kernel principal component analysis. The results show that the proposed method can more significantly improve the effect of fault detection in distributed system process monitoring compared with the traditional method, and it has a better process monitoring effect.

Segheri, Mohamed; Boudjemaa, Fares; Nemra, Abdelkrim; Bibi, Youssouf

doi: 10.1177/01423312231189380pmid: N/A

Most nonlinear dynamic systems are characterized by uncertainties in models and parameters. Deterministic models cannot account for these uncertainties; therefore, model-based control using such models cannot provide the required performance. It is crucial to establish a practical concept of model-free control as a powerful alternative to model-based control. This paper develops a model-free adaptive backstepping control (MFABC) based on type 2 fuzzy Petri nets for a class of uncertain nonlinear systems. To provide valuable robustness to the MFABC structure, we have exploited the universal approximation property of type 2 fuzzy Petri nets to approximate the different nonlinear functions of the uncertain nonlinear system. The parameter adaptive laws are designed by the Lyapunov function; the stability and error convergence can be guaranteed. The simulation tests show that the proposed MFABC can provide good performance and high accuracy compared with the backstepping control. Moreover, the stability of this control scheme is affirmed.

Huong, Dinh Cong

doi: 10.1177/01423312231195932pmid: N/A

This paper addresses the problem of event-triggered robust state and fault simultaneous estimation for nonlinear time-delay systems subject to actuator and sensor unknown disturbances. Based on a fault decomposition technique and some basic mathematical transformations, we obtain an augmented system where the state vector consists of the variable of the original system and the fault. Then a novel event-triggered state observer for the augmented system is proposed to robustly estimate the variable of the original system and the fault. We next established a sufficient condition for the existence of such an observer. We translated it into a linear matrix inequality (LMI), which can be effectively solved using the MATLAB LMI Control Toolbox. Finally, an illustrative example is applied to test the proposed method.

Yang, Yushi; Li, Meng; Chen, Yong

doi: 10.1177/01423312231196642pmid: N/A

This paper investigates the problem of tracking target control of a nonlinear system in the context of cyber-physical systems for connected autonomous vehicles with external unknown disturbances. A new robust tracking strategy via backstepping sliding-mode control is proposed. First, a connected nonlinear vehicle dynamical model with disturbances is constructed. Then, a disturbance observer is presented to approximate the unknown disturbances when the derivative of the disturbance is bounded. This paper has proved that the error of the observation can converge to zero in finite time. Third, a tracking control method is designed which combines the backstepping method with the sliding-mode method. According to the method, the estimated values of interference are used as a priori knowledge. Furthermore, the stability of the designed control strategy is demonstrated through using the Lyapunov theory. Finally, simulation experiments are presented to demonstrate the feasibility of the proposed approaches.

Lei, Meizhen; Wu, Xianqing; Zhao, Yijiang; Li, Fang

doi: 10.1177/01423312231196945pmid: N/A

In this paper, a disturbance-observer–based control approach is developed for overhead crane systems. Different from existing control strategies, the issues consisting of the output feedback, input saturation, double-pendulum dynamics, and uncertain disturbances are taken into consideration here. In particular, a disturbance observer is designed first, which can exactly estimate uncertain disturbances. Next, to enhance the performance of the controller, a virtual position signal is constructed and a corresponding Lyapunov function is introduced. Then, based on the provided Lyapunov function and the designed disturbance observer, a composite control approach is developed for overhead crane systems with double-pendulum dynamics and the convergence of the system states is proved via rigorous theoretical analysis. Finally, the effectiveness and robustness of the proposed control approach are verified by simulation tests.