Robustness bound for receding horizon finite memory control: Lyapunov–Krasovskii approach

Ahn, Choon Ki

doi:10.1080/00207179.2012.669849

Cited by 21 publications

(5 citation statements)

References 16 publications

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“…Based on the above observations, the main contributions of this paper can be summarized as follows • A FHMC model for the NCS is established by using input delay approach. Different from the previous results which only the latest sampling information is used [7] or some probability distribution is needed [12,13], the proposed FHMC model makes full use of finite available historic sampled-data and characterizes it as a chain-like input delay model for the NCS.…”

Section: Introductionmentioning

confidence: 99%

Finite Horizon Memory Control of Networked Systems Using Chain-like Lyapunov Function

Liu,

Wang,

Sun

et al. 2023

Int. J. Robot. Autom. Technol.

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This paper proposes a novel finite horizon memory control (FHMC) design framework for networked systems by using input delay approach. A chain-like input delay model is established to characterize the networked control system (NCS) under memory control architecture in order to make full use of historic sampled-data. Based on the proposed chain-like delay model, the corresponding chain-like Lyapunov-Krasovskii function, which records the historic sampling information of NCS, is well constructed for facilitating further analysis and synthesis of the proposed FHMC scheme. Both state feedback controllers and static output feedback controllers are derived by solving LMIs (Linear matrix inequalities). The proposed FHMC scheme is skilled in improving control performance of networked systems. Simulations show the effectiveness of the presented FHMC scheme.

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Section: Introductionmentioning

confidence: 99%

Finite Horizon Memory Control of Networked Systems Using Chain-like Lyapunov Function

Liu,

Wang,

Sun

et al. 2023

Int. J. Robot. Autom. Technol.

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“…Kwon et al [29,30] developed FIR filters applicable for both continuous and discrete time. In [22,[31][32][33][34][35][36][37], several robust FIR filters and a nonlinear FIR filter were proposed. In [38][39][40][41][42], a Kalman-like unbiased FIR (KUFIR) filter was proposed.…”

Section: Introductionmentioning

confidence: 99%

Arbitration algorithm of FIR filter and optical flow based on ANFIS for visual object tracking

et al. 2015

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“…This error accumulation often leads to performance degradation or divergence of the KF. In order to overcome this problem, finite impulse response (FIR) filters [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] were developed. Since FIR filters use recent finite measurements to generate state estimates, they can prevent error accumulation and have built-in bounded-input bounded-output (BIBO) stability.…”

Section: Introductionmentioning

confidence: 99%

Weighted average extended FIR filter bank to manage the horizon size in nonlinear FIR filtering

Pak¹,

Yoo²,

Lim³

et al. 2014

Int. J. Control Autom. Syst.

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In this paper, we propose a novel approach to manage the horizon size in nonlinear finite impulse response (FIR) filtering. The proposed approach is to perform state estimation through a bank of FIR filters called a weighted average extended FIR filter bank (WAEFFB). In the WAEFFB, the state estimate is obtained by weighting the average of multiple estimates from a bank of extended FIR filters that uses different horizon sizes. The horizon sizes used for the WAEFFB are adjusted constantly by maximizing the likelihood function. We show through simulations that the WAEFFB yields better results than the conventional approach that uses a constant (i.e., fixed) horizon size.

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Robustness bound for receding horizon finite memory control: Lyapunov–Krasovskii approach

Cited by 21 publications

References 16 publications

Finite Horizon Memory Control of Networked Systems Using Chain-like Lyapunov Function

Finite Horizon Memory Control of Networked Systems Using Chain-like Lyapunov Function

Arbitration algorithm of FIR filter and optical flow based on ANFIS for visual object tracking

Weighted average extended FIR filter bank to manage the horizon size in nonlinear FIR filtering

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