Adaptive output feedback control with cerebellar model articulation controller-based adaptive PFC and feedforward input

Otakara, Nozomu; Akaike, Kota; Kunimatsu, Sadaaki; Mizumoto, Ikuro

doi:10.1080/18824889.2022.2035925

Cited by 3 publications

(4 citation statements)

References 10 publications

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“…Given the mth-order MIMO uncertain nonlinear systems in (1). The modified MWCMAC control system is calculated in (8), in which u MWCMAC is given in (14) and the online learning laws of the MWCMAC are shown in ( 21)- (26), and the robust compensation controller is chosen as in (30). Then, the robust stability…”

Section: Stability Analysismentioning

confidence: 99%

“…The CMAC is a memory network classified as a perception-like memory network that is not fully connected to the reception fields [7]. The CMAC has been used in many nonlinear systems and has demonstrated its simplicity, generalization, and fast learning ability [8,9]. A conventional CMAC has certain drawbacks since it uses binary functions with locally constant reception.…”

Section: Introductionmentioning

confidence: 99%

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Wavelet CMAC for MIMO uncertain nonlinear systems using a modified social ski‐driver algorithm

Lin

et al. 2022

Asian Journal of Control

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This study uses a Mexican hat wavelet membership function for a cerebellar model articulation controller (CMAC) to develop a more efficient adaptive controller for multiple input multiple output (MIMO) uncertain nonlinear systems. The main controller is called the adaptive Mexican hat wavelet CMAC (MWCMAC), and an auxiliary controller is used to remove the residual error. For the MWCMAC, the online learning laws are derived from the gradient descent method. In addition, the learning rate values are very important and have a great impact on the performance of the control system; however, they are difficult to choose accurately. Therefore, a modified social ski driver (SSD) algorithm is proposed to find optimal learning rates for the control parameters. Finally, a magnetic ball levitation system and a nine‐link biped robot are used to illustrate the effectiveness of the proposed SSD‐based MWCMAC control system. The comparisons with other existing control algorithms have shown the superiority of the proposed control system.

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Section: Stability Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wavelet CMAC for MIMO uncertain nonlinear systems using a modified social ski‐driver algorithm

Lin

et al. 2022

Asian Journal of Control

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“…In this context, a method was proposed to preliminarily (offline) design an appropriate PFC, without modeling a target system, by using an arbitrary input/output dataset {u, y} acquired experimentally or in another way. 5 With this method, system nonlinearity can be handled by using the cerebellar model arithmetic controller (CMAC: Cerebellar Model Articulation Controller), known as a technique of machine learning. 6 In so doing, however, a target system has to be fragmented into several linearizable regions, while proper fragmentation requires certain experience and prior information.…”

Section: Introductionmentioning

confidence: 99%

“…For example, when aiming to design a model‐based PFC, multiple nominal models at operating points are necessary, which requires much cost and time for many experiments at every operating point. In this context, a method was proposed to preliminarily (offline) design an appropriate PFC, without modeling a target system, by using an arbitrary input/output dataset { u , y } acquired experimentally or in another way 5 . With this method, system nonlinearity can be handled by using the cerebellar model arithmetic controller (CMAC: Cerebellar Model Articulation Controller), known as a technique of machine learning 6 .…”

Section: Introductionmentioning

confidence: 99%

Adaptive output feedback control system design for nonlinear systems via neural networks

Li,

Otakara,

Kato

et al. 2024

Elect Comm in Japan

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Adaptive output feedback control based on output feedback exponential passivity (OFEP) has a simple structure and strong robustness in regard to disturbances and system uncertainties. However, it is difficult for most nonlinear systems to satisfy the conditions of OFEP. Thus, the introduction of a suitable parallel feedforward compensator (PFC) to construct an OFEP‐augmented system with the controlled system has been used, but the control output cannot achieve perfect tracking because of the output of the introduced PFC. As a solution, introducing a feedforward (FF) input to build a 2 degree of freedom (2‐DOF) is a simple and effective way to solve this problem. In this paper, we propose the design schemes for suitable PFC and FF input of nonlinear systems via the use of neural networks (NN), respectively. Besides, to cope with possibly present input disturbances, we also provide a method to achieve disturbance compensation based on NN to reduce their interference. Finally, the effectiveness of all proposed design schemes is confirmed through numerical simulations.

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ニューラルネットワークを用いた非線形系に対する適応出力フィードバック制御系設計

Li,

Otakara,

Kato

et al. 2024

IEEJ Trans.EIS

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Adaptive output feedback control with cerebellar model articulation controller-based adaptive PFC and feedforward input

Cited by 3 publications

References 10 publications

Wavelet CMAC for MIMO uncertain nonlinear systems using a modified social ski‐driver algorithm

Wavelet CMAC for MIMO uncertain nonlinear systems using a modified social ski‐driver algorithm

Adaptive output feedback control system design for nonlinear systems via neural networks

ニューラルネットワークを用いた非線形系に対する適応出力フィードバック制御系設計

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