Design of Optimal UKF State Observer–Controller for Stochastic Dynamical Systems

Rana, Rohit; Agarwal, Vijyant; Gaur, Prerna; Parthasarathy, Harish

doi:10.1109/tia.2020.3048647

Cited by 8 publications

(3 citation statements)

References 29 publications

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“…Because of its flexible mechanical structure, high positional resolution and abundant compatible software packages, it has become the first choice for secondary development and has been widely adopted as a robot manipulator in many researches. (Rana et al , 2020; Shen and Pan, 2019). To name a few, a teleoperation system using two PHANToM Omni robotic devices to control the Da Vinci surgical robot for surgical procedures has been demonstrated in Leporini et al (2020), in which the PHANToM Omni robotic device was believed to provide accurate operating instructions to the Da Vinci surgical robot and timely force feedback to the surgeon.…”

Section: Methodsmentioning

confidence: 99%

Event-triggered aperiodic intermittent sliding-mode control for master–slave bilateral teleoperation robotic systems

Gao

2022

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Purpose The purpose of this paper is to propose a novel event-triggered aperiodic intermittent sliding-mode control (ETAI-SMC) algorithm for master–slave bilateral teleoperation robotic systems to further save communication resources while maintaining synchronization precision. Design/methodology/approach By using the Lyapunov theory, a new event-triggered aperiodic intermittent sliding-mode controller is designed to synchronize master–slave robots in a discontinuous method. Unlike traditional periodic time-triggered continuous control strategy, a new ETAI condition is discussed for less communication pressure. Then, the exponential reaching law is adopted to accelerate sliding-mode variables convergence, which has a significant effect on synchronization performance. In addition, the authors use quantizers to make their algorithm have obvious progress in saving communication resources. Findings The proposed control algorithm performance is validated by an experiment developed on a practical bilateral teleoperation system with two PHANToM Omni robotic devices. As a result, the synchronization error is limited within a small range and the control frequency is evidently reduced. Compared with a conventional control algorithm, the experimental results illustrate that the proposed control algorithm is more sensitive to system states changes and it can further save communication resources while guaranteeing the system synchronization accuracy, which is more practical for real bilateral teleoperation robotic systems. Originality/value A novel ETAI-SMC for bilateral teleoperation robotic systems is proposed to find a balance between reducing the control frequency and synchronization control precision. Combining the traditional sliding-mode control algorithm with the periodic intermittent control strategy and the event-triggered control strategy has produced obvious effect on our control performance. The proposed ETAI-SMC algorithm helps the controller be more sensitive to system states changes, which makes it possible to achieve precise control with lower control frequency. Moreover, we design an environment contact force feedback algorithm for operators to improve the perception of the slave robot working environment. In addition, quantizers and the exponential convergence law are adopted to help the proposed algorithm perform better in saving communication resources and improving synchronization precision.

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Section: Methodsmentioning

confidence: 99%

Event-triggered aperiodic intermittent sliding-mode control for master–slave bilateral teleoperation robotic systems

Gao

2022

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“…Complexing of filtering algorithms with fuzzy logic algorithms [13] or control algorithms [14] to compensate for random disturbances. 5.…”

mentioning

confidence: 99%

“…However, at the same time, the major difficulty of using it is the impossibility of approximating the variations of the observer's parameters with white Gaussian noise, as shown in Reference [35] and allowed us to obtain simple recurrent dependences of the estimation error on the variations of the filter parameters there. In the vast majority of practical applications, a random change in the observer's parameters over time occurs rather slowly [10][11][12][13][14][15][16], which makes it impossible to use the previously obtained ratios and requires the derivation of fundamentally new dependencies of estimation errors on variations in the observer's parameters. In this regard, the problem solution was further considered on the basis of joint integration of the two approaches mentioned above, which are fundamentally different from each other-the use of irregular accurate observations of the state vector of the object and the mathematical apparatus for the study of disturbed multidimensional linear systems.…”

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confidence: 99%

Kalman Filter Adaptation to Disturbances of the Observer’s Parameters

et al. 2021

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Currently, one of the most effective algorithms for state estimation of stochastic systems is a Kalman filter. This filter provides an optimal root-mean-square error in state vector estimation only when the parameters of the dynamic system and its observer are precisely known. In real conditions, the observer’s parameters are often inaccurately known; moreover, they change randomly over time. This in turn leads to the divergence of the Kalman estimation process. The problem is currently being solved in a variety of ways. They include the use of interval observers, the use of an extended Kalman filter, the introduction of an additional evaluating observer by nonlinear programming methods, robust scaling of the observer’s transmission coefficient, etc. At the same time, it should be borne in mind that, firstly, all of the above ways are focused on application in specific technical systems and complexes, and secondly, they fundamentally do not allow estimating errors in determining the parameters of the observer themselves in order to compensate them for further improving the accuracy and stability of the filtration process of the state vector. To solve this problem, this paper proposes the use of accurate observations that are irregularly received in a complex measuring system (for example, navigation) for adaptive evaluation of the observer’s true parameters of the stochastic system state vector. The development of the proposed algorithm is based on the analytical dependence of the Kalman estimate variation on the observer’s parameters disturbances obtained using the mathematical apparatus for the study of perturbed multidimensional dynamical systems. The developed algorithm for observer’s parameters adaptive estimation makes it possible to significantly increase the accuracy and stability of the stochastic estimation process as a whole in the time intervals between accurate observations, which is illustrated by the corresponding numerical example.

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Estimation of robot states with poisson process based on EKF approximate of Kushner filter: a completely coordinate free Lie group approach

et al. 2021

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Design of Optimal UKF State Observer–Controller for Stochastic Dynamical Systems

Cited by 8 publications

References 29 publications

Event-triggered aperiodic intermittent sliding-mode control for master–slave bilateral teleoperation robotic systems

Event-triggered aperiodic intermittent sliding-mode control for master–slave bilateral teleoperation robotic systems

Kalman Filter Adaptation to Disturbances of the Observer’s Parameters

Estimation of robot states with poisson process based on EKF approximate of Kushner filter: a completely coordinate free Lie group approach

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