Adaptive observers have undergone sustained theoretical evolution and gained widespread industrial adoption due to their inherent capability for simultaneous parameter and state estimation in dynamic systems. While significant advancements have been made in nonlinear system identification and adaptive control, critical challenges persist in closed-loop implementations. These challenges stem primarily from input-noise correlation induced by feedback mechanisms and performance degradation caused by unknown disturbances. To solve these problems, a closed-loop robust adaptive estimation framework is proposed in this article. It employs coprime factorization to construct noise-decoupled auxiliary variables as reconstructed system inputs, thereby effectively decoupling input-noise correlations. The proposed framework integrates a disturbance estimation module into the adaptive observer while implementing optimal step size adaptation for noise suppression. This cooperative approach achieves simultaneous estimation of system dynamics and disturbances with guaranteed stability and convergence. Experimental validation demonstrates superior performance compared to existing algorithms.

This paper presents an adaptive residual generator-based co-estimation algorithm for multi-sinusoidal signals with unknown components. By modeling the multi-sinusoidal signal, a residual generator algorithm integrated with the adaptive estimation mechanism is constructed. The proposed algorithm can simultaneously estimate the amplitude, frequency, and phase of every sinusoidal component online with stability and convergence guarantees. The performance of the proposed co-estimation approach is evaluated with a simulation experiment.

This article proposes a subspace-aided distributed monitoring and control performance optimization integrated framework and the corresponding distributed monitoring and optimization approaches equivalent to centralized designs. It effectively realizes the online global control performance optimization and solves the predesigned controller parameter adjustment limitation. The main contributions of this article are as follows. First, the proposed distributed monitoring and optimization modules can cooperate to establish a subspace-aided distributed integrated framework. The framework effectively addresses the issue of separate design in monitoring and optimization, achieving modularization that facilitates the expansion and maintenance of interconnected systems. Second, the proposed subspace-aided control performance optimization approach breaks the limitations of existing methods that require predesigned controller parameter adjustments, which can achieve distributed control performance optimization while ensuring closed-loop stability of interconnected systems. Third, the proposed optimization approach can automatically adjust the iterative step size, avoiding the disadvantage of manually setting the step size in the traditional optimization algorithm. It shortens the optimization time and reduces the design difficulty. The new methodologies have been evaluated against the current techniques and validated using an interconnected dc motor system, which holds significant engineering importance.

This article proposes a projection-aided robust distributed monitoring and control optimization approach for interconnected systems with disturbances. The disturbances and state coupling between subsystems are a challenge in achieving accurate distributed process monitoring using data-driven techniques. To address the problems, a distributed adaptive residual generator uses the average consensus algorithm to perform data fusion on the subsystem residual generator to implement disturbance decoupling process monitoring. The key to implementing this process is to use input and output data disturbance in the perturbed orthogonal complementary space to drive the adaptive residual generator. Then, using the projection technique, the residual signal in the disturbance space drives the distributed learning of plug-and-play (PnP) controller parameters. The average consensus algorithm ensures that the subsystem PnP controller parameter gradient consistency converges to the centralized design. The feasibility and effectiveness of the proposed approach are verified and demonstrated through a simulation.

Aiming at the problem of colored noise in the signal, this article proposes a subspace frequency estimation approach under colored noise with application to fault diagnosis of motor rolling bearings. First, a nonlinear discrete-time system is described to generate colored noise. An extended I/O model with parameters of a nonlinear discrete-time system is given by the subspace method. Then, the gap metric-aided system order determination approach is developed for extended observability matrix identification. Then, the data-driven diagnostic observer parameter identification approach and the fast approximate power iterative subspace method are adopted to realize online monitoring for frequency change detection. Eventually, a data-driven design scheme of residual generator is proposed for the implementation of fault detection. The effectiveness of the proposed methods is verified for fault diagnosis performance through numerical simulations and the experimental measurements from the dynamic motor rolling bearing experiment rig.

The intelligent manufacturing system is a complex, large-scale, interconnected system composed of many intelligent agents, and there may be physical or information space couplings between the agents. A distributed monitoring system and optimization control method are proposed to ensure the system completes its tasks safely and efficiently. The distributed monitoring system based on the average consensus algorithm is equivalent to the centralized design method, in which the submonitoring system only requires local and neighbor subsystem information. The advantage of this design is that it uses local and interactive information to achieve global diagnosis. In addition, sending data from all subsystems to a central computing node is challenging to implement in large-scale manufacturing systems. Based on the centralized plug-and-play (PnP) optimization control method, an average consensus algorithm distributed manufacturing system PnP optimization control method is proposed. Its advantage is that it uses local information and interactive information to achieve global control optimization. On this basis, an integrated architecture for distributed fault detection and optimization control is developed. The simulation results verify the feasibility and effectiveness of proposed method.

Coverage control describes the optimal deployment problem of multi-agent system with communication sensors, aiming to drive the multi-agent system reach the optimal deployment location. To accomplish coverage tasks, agents are required to communicate with each other. Thus, the connectivity of multi-agent system is a fundamental requirement in case of agent disconnection and disappearance. In this paper, we propose a distributed coverage control algorithm with connectivity optimization. According to the designed cost function, the controller is divided into two parts: coverage controller and connectivity optimization controller, and the method of gradient descent is used to minimize the cost function to get the controller. Finally, the simulation serves to show the effectiveness of the algorithm.

Three-tank system is an important benchmark in industrial process. However, so far, the research on the three-tank system is mainly limited to simulation studies, and the use of virtual simulators. In this article, a real-time three-tank system setup is used for practical investigations. Differing with the virtual simulator of a three-tank system, the setup can enable the setting of different types of faults (such as cloggings and leaks in the system, sensor faults, and actuator faults) through manual manipulation, users can choose the combinations of different valves and knobs in the setup, which is helpful to evaluate and compare methods for process monitoring and control. The relevant codes or modules can be applied directly that are developed in the MATLAB/Simulink environment. On this setup, two methods are used to verify the effectiveness in this study. One method is to solve the problem of process monitoring and fault detection; due to the fluctuation of the liquid level caused by flow, the input/output (I/O) data are preferred to be decomposed to different subspaces, which aims to identify the data-driven stable kernel representation. Moreover, the original controller of the three-tank system cannot match the system accurately, and therefore, needs to be modified. To solve the problem, a plug-and-play process control method (the other method used in this article) is applied, which adds a stable Youla parameterization matrix on the basis of the original controller. All controllers that internally stabilize the control loop improves the performance of the system without changing the original controller of the three-tank system. The experimental results of the two methods indicate that the proposed approach has strong practicality.

This paper presents the study on the data-driven process monitoring system design for the dynamic processes with periodic rectangular wave disturbance. The basic idea of the proposed methods are to identify the stable kernel representation (SKR) of the dynamic process by projecting the process data into the row subspace of the periodic rectangular wave disturbance. With the help of the projection, the kernel subspace of the system can be further determined. Based on the identified data-driven SKR, fault detection are developed. The performance and effectiveness of the proposed scheme is verified and demonstrated through the numerical study on randomly generated systems.

This paper focuses on the design of diagnostic observer based residual generator (DORG) for fault detection purposes. The property of the existing Multiple- Input-Single-Output (MISO) DORG is firstly discussed, followed by a parity vector based solution. Then, a novel Multiple- Input- Multiple-Output (MIMO) DORG is proposed through rigorous mathematical derivations. Compared with existing approaches, the proposed approach and algorithms retain the correlation information in the output variables, and reduce the offline design complexity and the online implementation efforts. Simulation studies on a numerical example show that the proposed approach has better fault detection performance than the MISO DORG based approach.