Model-free adaptive MIMO control algorithm application in polishing robot

Gao, Binbin; Cao, Rongmin; Hou, Zhongsheng; Zhou, Huixing

doi:10.1109/ddcls.2017.8068058

Cited by 12 publications

(5 citation statements)

References 6 publications

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“…Dynamic linearization methods for MFAC can be divided into three categories: compact-form dynamic linearization (CFDL), partial-form dynamic linearization (PFDL), and full-form dynamic linearization (FFDL) [19][20][21]. Among these three methods, the model structure of the CFDL method is relatively simple.…”

Section: Introductionmentioning

confidence: 99%

Parameter Self-Tuning of SISO Compact-Form Model-Free Adaptive Controller Based on Long Short-Term Memory Neural Network

Yang

Chen

2020

IEEE Access

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Model-free adaptive controller (MFAC) is a novel data-driven control methodology that relies only on input/output (I/O) measurement data instead of classic mathematical models of actual controlled plants. The single-input single-output (SISO) compact-form MFAC (SISO-CFMFAC) is a promising method for controlling SISO nonlinear time-varying systems. The parameters in SISO-CFMFAC must be carefully tuned before use, as inappropriate parameters may lead to poor control performance. However, up to now, parameter tuning has been a time-consuming and laborious task. In this paper, a new approach called SISO-CFMFAC-LSTM is proposed for parameter self-tuning of SISO-CFMFAC based on long short-term memory (LSTM) neural network. To evaluate the performance of the proposed methodology, qualitative and quantitative comparisons with other existing control algorithms are carried out. Six individual performance indices, namely, the root mean square error (RMSE), the integral absolute error (IAE), the integral time-weighted absolute error (ITAE), the integral absolute variation of the control signal (IAVU), the maximum overshoot (MO), and the imprecise control ratio (ICR), are introduced for quantitative comparison. The experimental results demonstrate that the proposed SISO-CFMFAC-LSTM achieves the best performance in all indices, indicating that it is an effective control method for SISO nonlinear time-varying systems.

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Section: Introductionmentioning

confidence: 99%

Parameter Self-Tuning of SISO Compact-Form Model-Free Adaptive Controller Based on Long Short-Term Memory Neural Network

Yang

Chen

2020

IEEE Access

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“…However, the establishment of leaching process model is heavy and not universal. Once the working environment or production conditions change, the model needs to be updated, which limits the implementation of model-based controllers. , Besides, the model-based control strategy not only has greater computational complexity but also has great engineering application limitations . The MFAC control algorithm based on CFDL has the advantages of simple structure, good algorithm stability, and easy to realize controller design of the actual process.…”

Section: Introductionmentioning

confidence: 99%

“… 36 , 37 Besides, the model-based control strategy not only has greater computational complexity but also has great engineering application limitations. 38 The MFAC control algorithm based on CFDL has the advantages of simple structure, good algorithm stability, and easy to realize controller design of the actual process. Therefore, this paper will study the MFAC control strategy based on CFDL and design the controller according to the characteristics of the leaching process.…”

Section: Introductionmentioning

confidence: 99%

Model-Free Adaptive Control of Hydrometallurgy Cascade Gold Leaching Process with Input Constraints

et al. 2023

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Hydrometallurgy technology can directly deal with low grade and complex materials, improve the comprehensive utilization rate of resources, and effectively adapt to the demand of low carbon and cleaner production. A series of cascade continuous stirred tank reactors are usually applied in the gold leaching industrial process. The equations of leaching process mechanism model are mainly composed of gold conservation, cyanide ion conservation, and kinetic reaction rate equations. The derivation of the theoretical model involves many unknown parameters and some ideal assumptions, which leads to difficulty and imprecision in establishing the accurate mechanism model of the leaching process. Imprecise mechanism models limit the application of model-based control algorithms in the leaching process. Due to the constraints and limitations of the input variables in the cascade leaching process, a novel model-free adaptive control algorithm based on compact form dynamic linearization with integration (ICFDL-MFAC) control factor is first constructed. The constraints between input variables is realized by setting the initial value of the input to the pseudo-gradient and the weight of the integral coefficient. The proposed pure data-driven ICFDL-MFAC algorithm has anti-integral saturation ability and can achieve faster control rate and higher control precision. This control strategy can effectively improve the utilization efficiency of sodium cyanide and reduce environmental pollution. The consistent stability of the proposed control algorithm is also analyzed and proved. Compared with the existing model-free control algorithms, the merit and practicability of the control algorithm are verified by the practical leaching industrial process test. The proposed model-free control strategy has advantages of strong adaptive ability, robustness, and practicability. The MFAC algorithm can also be easily applied to control the multi-input multi-output of other industrial processes.

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“…Various control methods have been proposed for the control of MIMO systems, such as decentralized control, adaptive control, and robust control. However, these methods have limitations, such as complexity and lack of generality [1][2][3][4][5][6][7][8]. Universal approximators, such as artificial neural networks and fuzzy logic systems, are extensively used for MIMO systems control by approximating the underlying nonlinear dynamics and relationships between inputs and outputs [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Modeling of Mimo Systems With Universal Controller

Mohammed A. Ali,

Vodichev

2023

ELTECS

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Abstract. The control of multi-input multi-output (MIMO) systems poses significant challenges due to the intricate dynamics and interactions between multiple input and output channels. This paper presents a simulation study on MIMO systems control using the universal control technique. The proposed method offers a versatile framework for regulating MIMO systems with varying dynamics, utilizing a single control algorithm. The underlying concept of the universal control law is to devise a set of principles or strategies that can be universally applied to any system, regardless of its specific characteristics or parameters. To achieve this, the control algorithm is designed based on the derivatives balance of the system's input-output dynamics, granting the controller adaptability to both single-input single-output (SISO) and MIMO systems. Numerical examples are provided to demonstrate the effectiveness of the proposed method, involving a MIMO system comprising four inputs in a two-coupled distillation column process and two inputs in a twin rotor setup. The challenges associated with distillation column control, such as nonlinearity, disturbances, and interaction between multiple inputs and outputs, are considered. Twin rotor systems, consisting of two interconnected rotors, pose unique control difficulties, primarily arising from the coupling between the two rotors and their nonlinear behavior. The simulation results show the capability of the proposed universal controller to achieve precise and robust control over the MIMO system. Keywords: MIMO, Control, Simulation, Distillation Column Process, Twin Rotor.

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Model-free adaptive MIMO control algorithm application in polishing robot

Cited by 12 publications

References 6 publications

Parameter Self-Tuning of SISO Compact-Form Model-Free Adaptive Controller Based on Long Short-Term Memory Neural Network

Parameter Self-Tuning of SISO Compact-Form Model-Free Adaptive Controller Based on Long Short-Term Memory Neural Network

Model-Free Adaptive Control of Hydrometallurgy Cascade Gold Leaching Process with Input Constraints

Modeling of Mimo Systems With Universal Controller

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