Passive robust control for uncertain Hamiltonian systems by using operator theory

Bu, Ni; Zhang, Yuyi; Li, Xiaoyong; Chen, Wei; Jiang, Chenfanfu

doi:10.1049/cit2.12142

Cited by 7 publications

(3 citation statements)

References 35 publications

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“…Furthermore, Bu et al considered a sufficient condition of ensuring the robust stability in Reference 31, and the robust tolerant tracking control for the two‐joint manipulator in Reference 32 wherein the perfect tracking was hold for the uncertain nonlinear systems with external disturbance. The Hamilton system is very important, along with the Newtonian system and the Lagrange system, therefore, the passive robust control for uncertain Hamiltonian systems was discussed in Reference 33, wherein the Hamiltonian systems was controlled to be robust and passive by using RRCF method.…”

Section: Introductionmentioning

confidence: 99%

Robust tracking control for uncertain micro‐hand actuator with Prandtl‐Ishlinskii hysteresis

Zhang

2023

Intl J Robust & Nonlinear

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This article is concerned with the robust tracking control for an uncertain micro‐hand actuator with Prandtl‐Ishlinskii hysteresis (PI hysteresis) by robust right coprime factorization (RRCF). Firstly, the PI hysteresis is modeled into the rubber micro‐hand actuator and a new hysteretic micro‐hand system is obtained. Secondly, by compensating the hysteresis part into the isomorphic subspace, the effect of hysteresis on micro‐hand system is transferred and the operator‐based robust controllers are designed to ensure that the robust stability of the whole system. Thirdly, the RRCF and terminal sliding mode control are combined for tracking control, and the nonsingular terminal sliding mode surface with integral terms is designed, which shorten the convergence time and improve the tracking performance. Finally, the effectiveness of the method is tested by the simulation of the micro‐hand system.

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Section: Introductionmentioning

confidence: 99%

Robust tracking control for uncertain micro‐hand actuator with Prandtl‐Ishlinskii hysteresis

Zhang

2023

Intl J Robust & Nonlinear

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“…Different from the model based control techniques [1][2][3][4][5][6], as an intelligent control scheme, iterative learning control (ILC) is usually utilised to impel the output of repetitive system to track a reference trajectory gradually along the iteration direction [7][8][9][10]. By employing the tracking information of preceding iterations, ILC adjusts the control input of subsequent iterations to make the tracking error reduce.…”

Section: Introductionmentioning

confidence: 99%

Iteration dependent interval based open‐closed‐loop iterative learning control for time varying systems with vector relative degree

Wei

Wang

et al. 2023

CAAI Trans on Intel Tech

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For linear time varying (LTV) multiple input multiple output (MIMO) systems with vector relative degree, an open‐closed‐loop iterative learning control (ILC) strategy is developed in this article, where the time interval of operation is iteration dependent. To compensate the missing tracking signal caused by iteration dependent interval, the feedback control is introduced in ILC design. As the tracking signal of many continuous iterations is lost in a certain interval, the feedback control part can employ the tracking signal of current iteration for compensation. Under the assumption that the initial state vibrates around the desired initial state uniformly in mathematical expectation sense, the expectation of ILC tracking error can converge to zero as the number of iteration tends to infinity. Under the circumstance that the initial state varies around the desired initial state with a bound, as the number of iteration tends to infinity, the expectation of ILC tracking error can be driven to a bounded range, whose upper bound is proportional to the fluctuation. It is revealed that the convergence condition is dependent on the feedforward control gains, while the feedback control can accelerate convergence speed by selecting appropriate feedback control gains. As a special case, the controlled system with integrated high relative degree is also addressed by proposing a simplified iteration dependent interval based open‐closed‐loop ILC method. Finally, the effectiveness of the developed iteration dependent interval based open‐closed‐loop ILC is illustrated by a simulation example with two cases on initial state.

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“…The fourth paper derives a parameter optimisation strategy of control systems for uncertain WPT systems using the modified genetic algorithm [4]. The fifth paper proposes a design scheme to guarantee the uncertain non‐linear systems to be robustly stable while the equivalent non‐linear systems is passive [5]. The sixth paper designs a humanoid sliding model neural network controller based on the human gait [6].…”

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confidence: 99%

Guest Editorial: Special issue on recent developments in advanced mechatronics systems

Meng

Deng

2022

CAAI Trans on Intel Tech

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This Special Issue will accept contributions describing innovative research and developments in 'Advanced Mechatronic Systems', and will cover a wide range of disciplines, including robotics, automation and control systems, and new energy systems. It particularly welcomes those emerging methodologies and techniques which bridge theoretical studies and applications in advanced mechatronic systems. Novel quantitative engineering and science studies may be considered as well.Topics of interest include, but are not limited to:� AI, big data, intelligent computation, and their applications. � Signal and image processing and pattern recognition in mechatronic systems. � New energy systems, simulation and optimisation. � Intelligent fault diagnosis and tolerance of mechatronic systems.

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Passive robust control for uncertain Hamiltonian systems by using operator theory

Cited by 7 publications

References 35 publications

Robust tracking control for uncertain micro‐hand actuator with Prandtl‐Ishlinskii hysteresis

Robust tracking control for uncertain micro‐hand actuator with Prandtl‐Ishlinskii hysteresis

Iteration dependent interval based open‐closed‐loop iterative learning control for time varying systems with vector relative degree

Guest Editorial: Special issue on recent developments in advanced mechatronics systems

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