IET Control Theory & Applications
Volume 15, Issue 15
https://ietresearch.onlinelibrary.wiley.com/toc/17518652/2021/15/15
A new fault tolerant control scheme for non-linear systems by T-S fuzzy model approach
Wu, Yue; Wang, Yang; Li, Tieshan; Yang, Zhixin
https://doi.org/10.1049/cth2.12081
This paper is concerned with the problem of fault tolerant control for non-linear systems with sensor faults based on the T-S fuzzy model approach. First, a fault estimation (FE) observer with L∞ technique is designed to estimate the fault. Second, by using the fault estimation information, which are provided by the fault estimation observer, a novel fault tolerant control scheme with a fault compensation mechanism in the premise part of the fuzzy controller is presented to compensate the effect of the fault and stabilize the system. Different from the existing methods, where the fault compensation mechanism is only established in the consequent part of the controller, in the presented method, the fault compensation mechanism is established in the consequent part as well as the premise part of the fuzzy controller. Then, by using the fault compensation mechanism, the bound of the deviation between the normalized membership functions of the fuzzy model and those of the controller can be reduced such that the fault tolerant control design can be relaxed. In the end, an example is given to illustrate the validity of the developed fault tolerant control scheme.
Multi-actuator control with modal switching and different disturbance for scanning imaging motion compensation
Gao, Yang; Xu, Rui; Wang, Yutang; Tian, Dapeng
https://doi.org/10.1049/cth2.12168
Scanning imaging can be used to obtain wide field-of-view, high-resolution remote-sensing images with a telescope lens. However, the motion between the image and the sensor causes blurring during scanning, which reduces the image quality. In this paper, a motion compensation method based on multi-actuator control with modal switching is proposed. In this method, the tracking controller realizes scanning and compensation; the cooperative controller reduces the relative motion that causes motion blur, and modal switching solves the travel limitation of the linear actuator. Further, to improve the control performance, an adaptive sliding mode controller is proposed and combined with a disturbance observer. The stability theorems for multi-actuator and modal switching are studied. Experimental results show that the accuracy, robustness, and frame rate of the proposed method improved compared with those of traditional methods. Compared with the non-cooperative structure using a PD controller, the root mean square error is reduced by 76.61%.
Parametric identification of output error model for sampled systems with integer-type time delay subject to load disturbance with unknown dynamics
Pandey, Saurabh; Liu, Tao; Wang, Qing-Guo
https://doi.org/10.1049/cth2.12170
In this paper, a bias-eliminated output error (OE) model identification method is proposed for single-input-single-output sampled systems with integer-type time delay subject to load disturbance with unknown dynamics. By viewing the load disturbance response as a time-variant parameter, an iterative least-squares identification algorithm is established to estimate the rational model parameters together with an integer-type delay parameter, while the disturbance response could be simultaneously estimated. To overcome the adverse effect of stochastic noise involved with output measurement, an auxiliary model is constructed to predict the noise-free system response. Moreover, a set of adaptive forgetting factors is introduced to expedite the convergence rate of model parameter estimation and the tracking performance of load disturbance response, respectively. In addition, a monotonically rising profile for evaluating the delay parameter is proposed for implementing the above iterative least-squares algorithm, in order to avoid the occurrence of multiple local minima of the loss function for model fitting. The asymptotic convergence on estimating the rational model parameters together with the delay parameter is analysed with a proof. An illustrative example is given to demonstrate the effectiveness and advantage of the proposed method.
Delayed output feedback based leader–follower and leaderless consensus control of uncertain multiagent systems
Soni, Sandeep Kumar; Xiong, Xiaogang; Sachan, Ankit; Kamal, Shyam; Ghosh, Sandip
https://doi.org/10.1049/cth2.12171
This paper proposes a distributed artificially delayed output feedback-based leader?follower and leaderless consensus control for the uncertain general linear multiagent systems (MASs) with an arbitrary relative degree. Based on the measured relative output and their delayed information with respect to neighbours, two distributed controllers are designed, which relaxes the requirement of all relative states information of agents. Through the Lyapunov?Krasovskii functional, linear matrix inequalities (LMIs) are formulated, and these LMIs are feasible for arbitrary small delays selected by the user. Delay-dependent sufficient conditions are provided to guarantee the asymptotic convergence of nominal error systems and ultimately uniform boundedness in the case of a perturbed error system, using the input to state stability (ISS) theory. Finally, the efficacy of the proposed method is illustrated through numerical examples.
An adaptive sliding-mode resilient control strategy in smart grid under mixed attacks
Li, Jian; Yang, De-Fu; Gao, Yan-Chao; Huang, Xin
https://doi.org/10.1049/cth2.12172
This paper is concerned with security problems for cyber-physical systems (CPSs) under dynamic load altering attacks (DLAA) and false data injection attacks (FDIA). The smart grid, as a typical CPS system, is taken as an example in this paper. Since the communication channel is vulnerable to FDIA and DLAA, the stability of the smart grid may be influenced. For enhancing resilience and stability of smart grids, first of all, the power system model including both DLAA and FDIA is introduced. Second, an adaptive sliding mode controller is proposed. The controller can ensure the reliable operation of the power system in the case of unknown attack information by using the adaptive mechanism online estimating the upper bound of attack signal to automatically eliminate the effect of mixed attacks. Finally, a power system with three generators and six buses is taken as an illustrative example, and simulation and experiment results obtained by using MATLAB and Hardware-in-time platform built by Sartsim verify the effectiveness of the proposed resilient defense strategy.
Distributed trust-based unscented Kalman filter for non-linear state estimation under cyber-attacks: The application of manoeuvring target tracking over wireless sensor networks
Adeli, Mahdieh; Hajatipour, Majid; Yazdanpanah, Mohammad Javad; Shafieirad, Mohsen; Hashemi-Dezaki, Hamed
https://doi.org/10.1049/cth2.12173
This paper is concerned with secure state estimation of non-linear systems under malicious cyber-attacks. The application of target tracking over a wireless sensor network is investigated. The existence of rotational manoeuvre in the target movement introduces non-linear behaviour in the dynamic model of the system. Moreover, in wireless sensor networks under cyber-attacks, erroneous information is spread in the whole network by imperilling some nodes and consequently their neighbours. Thus, they can deteriorate the performance of tracking. Despite the development of target tracking techniques in wireless sensor networks, the problem of rotational manoeuvring target tracking under cyber-attacks is still challenging. To deal with the model non-linearity due to target rotational manoeuvres, an unscented Kalman filter is employed to estimate the target state variables consisting of the position and velocity. A diffusion-based distributed unscented Kalman filtering combined with a trust-based scheme is applied to ensure robustness against the cyber-attacks in manoeuvring target tracking applications over a wireless sensor network with secured nodes. Simulation results demonstrate the effectiveness of the proposed strategy in terms of tracking accuracy, while random attacks, false data injection attacks, and replay attacks are considered.
An analysis of hot-started ADMM for linear MPC
Toyoda, Mitsuru; Tanaka, Mirai
https://doi.org/10.1049/cth2.12174
A convergence analysis of the alternating direction method of multipliers (ADMM) for linear model predictive control (MPC) problems with regularization terms is addressed here. Compared with conventional results, this paper focuses on the dynamical structure of the ADMM and derives the linear convergence of the state variables of the ADMM dynamics.In on-line optimization problems associated with MPC problems, because the computation time is a significant issue, a hot-start technique in which an initial point of the algorithm is set as the convergence point for the previous optimization problem is widely employed to improve the convergence property. Here, by utilizing the proposed convergence analysis framework, the effectiveness of the hot-start is explored, and the upper bounds of the number of iterations to guarantee the required accuracy of an obtained iterative solution are deduced. The proposed hot-start framework derives a criterion for choosing the penalty parameter of the ADMM in MPC problems and facilitates effective iteration, which is validated in a numerical example.
Covariance regulation based invariant Kalman filtering for attitude estimation on matrix Lie groups
Wang, Jiaolong; Li, Minzhe
https://doi.org/10.1049/cth2.12179
For matrix Lie groups attitude estimation problems with the trouble of unknown/inaccurate process noise covariance, by elaborating the proportion based covariance regulation scheme, this work proposes a novel version of adaptive invariant Kalman filter (AIKF). Invariant Kalman filter (IKF) takes into account the group geometry and can give better results than Euclidean Kalman filters, but it still heavily depends on the accuracy of noise statistics parameters. To ease this constraint, IKF's covariance propagation step is removed and a proportional regulation scheme is elaborated for the proposed AIKF: the feedback of posterior sequence is introduced to construct a closed-loop structure of covariance propagation, and then a proportional regulator is employed to amplify the feedback and accelerate the convergence of covariance calibration. As the main benefit, implementation of new AIKF does not require the accurate knowledge of noise statistics, which is also the main advantage over IKF. The mathematical derivation of proposed covariance regulation scheme is presented and the numerical simulations of the Lie groups attitude estimation problem are used to certify the filtering performance of the new approach.
