Performance-based data-driven model-free adaptive sliding mode control for a class of discrete-time nonlinear processes

Liu, Dong; Yang, Guang-Hong

doi:10.1016/j.jprocont.2018.06.006

Cited by 34 publications

(28 citation statements)

References 35 publications

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“…Thirdly, the MFAC method is easy to implement with high robustness. Fourth, under some actual assumptions, the CFDL-based MFAC scheme can guarantee the monotone convergence and the stability of bounded input and bounded output of the tracking error of the closed-loop system, which is an important characteristic that is different from other data-driven control methods [28].…”

Section: Model-free Adaptive Controlmentioning

confidence: 99%

“…Then, we realize the adaptive control of the nonlinear system. The PPD parameter can only be estimated by using the I/O measurement data of the controlled object [28]. There are three specific forms of dynamic linearization methods, including compact-format dynamic linearization (CFDL), partial format dynamic linearization (PFDL) and full-format dynamic linearization (FFDL).…”

Section: Model-free Adaptive Controlmentioning

confidence: 99%

“…28 show the control performances of MFAC and MFAC based on ADRC. The results are reported inTables 9 and 10.1.Figures 25 and 26are the heading change from 20 degrees to 60 degrees.…”

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confidence: 99%

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Fuzzy Optimized MFAC Based on ADRC in AUV Heading Control

Yin

et al. 2019

Electronics

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The control issue of Autonomous Underwater Vehicles (AUV) is very challenging since the precise mathematical model of AUV is hard to establish due to its strong coupling and time-varying features. Meanwhile, AUV movement is easily interfered with by ocean currents and waves, causing anti-interference performance of traditional Proportional-Integral-Derivative (PID) control to be unsatisfactory. Aiming to solve those problems, an algorithm of fuzzy optimized model-free adaptive control (MFAC) based on auto-disturbance rejection control (ADRC) was proposed and used in AUV heading control. The MFAC is used to overcome the difficulty with establishing a precise mathematical model, and the ADRC is introduced to handle the interference of currents and waves. In this paper, MFAC and ADRC are combined. First, the MFAC is performed based only on the I/O data of the controlled object, which is simple to implement with low calculation complexity and strong robustness. Then, a tracking differentiator (TD) is employed to track the input signal to overcome the antinomy of rapidity and hypertonicity in MFAC. After that, an extended-state observer (ESO) is added to control the variables of MFAC to estimate all the disturbances, which can greatly improve the anti-interference ability of the system. Due to the complexity and diversity of the marine environment, a fuzzy optimized MFAC based on ADRC is proposed to improve the adaptability of AUV to the marine environment. Simulations and experiments were carried out to verify the control effect of this algorithm in complex sea conditions.

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Section: Model-free Adaptive Controlmentioning

confidence: 99%

Section: Model-free Adaptive Controlmentioning

confidence: 99%

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Fuzzy Optimized MFAC Based on ADRC in AUV Heading Control

Yin

et al. 2019

Electronics

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“…Recently, several papers [12][13][14][15][16][17][18][19][20][21][22][23][24] have reported on model-free adaptive control (MFAC), interactive learning control (ILC), repetitive learning control (RLC), reinforcement learning (RL), and so on. The consensus tracking problems of MASs were researched in [12] by the MFAC approach, where both the time invariable and varying desired trajectories tracking are archived.…”

Section: Introductionmentioning

confidence: 99%

“…It should be pointed out that NNs-based methods need training processes and external testing signals for controller design, which are not convenient. Meanwhile, there are some interesting adaptive schemes in [19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Distributed Model-Free Bipartite Consensus Tracking for Unknown Heterogeneous Multi-Agent Systems with Switching Topology

Zhao

2020

Sensors

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This paper proposes a distributed model-free adaptive bipartite consensus tracking (DMFABCT) scheme. The proposed scheme is independent of a precise mathematical model, but can achieve both bipartite time-invariant and time-varying trajectory tracking for unknown dynamic discrete-time heterogeneous multi-agent systems (MASs) with switching topology and coopetition networks. The main innovation of this algorithm is to estimate an equivalent dynamic linearization data model by the pseudo partial derivative (PPD) approach, where only the input–output (I/O) data of each agent is required, and the cooperative interactions among agents are investigated. The rigorous proof of the convergent property is given for DMFABCT, which reveals that the trajectories error can be reduced. Finally, three simulations results show that the novel DMFABCT scheme is effective and robust for unknown heterogeneous discrete-time MASs with switching topologies to complete bipartite consensus tracking tasks.

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Adaptive Sliding Mode Control with Prescribed Performance

2023

Connected Vehicular Systems

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Performance-based data-driven model-free adaptive sliding mode control for a class of discrete-time nonlinear processes

Cited by 34 publications

References 35 publications

Fuzzy Optimized MFAC Based on ADRC in AUV Heading Control

Fuzzy Optimized MFAC Based on ADRC in AUV Heading Control

Distributed Model-Free Bipartite Consensus Tracking for Unknown Heterogeneous Multi-Agent Systems with Switching Topology

Adaptive Sliding Mode Control with Prescribed Performance

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