Data-Driven Tracking Control for a Class of Unknown Nonlinear Time-Varying Systems Using Improved PID Neural Network and Cohen-Coon Approach

Hao, Jun; Zhang, Guoshan

doi:10.1109/ddcls52934.2021.9455699

Cited by 9 publications

(6 citation statements)

References 19 publications

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“…The expression randn in equation ( 8) represents a random number with normal distribution. x s ve x ss expressions are explained mathematically as seen in equation (9). x m ve x c are formulated as in equation (10).…”

Section: ) Updating Solutionsmentioning

confidence: 99%

“…The Ziegler Nichols approach, one of the formula-based methods extensively used in PID design, and the Cohen-Coon [9] method were utilized in the design of the PID controller used in the drinking water filtering system, and their performances were compared [10]. For fractional-order PID (FOPID), a novel Ziegler Nichols autotuning approach with smaller overshoot, improved durability and steady-state response, and shorter sitting time was developed [11].…”

Section: Introductionmentioning

confidence: 99%

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Determination of Different Types of Controller Parameters Using Metaheuristic Optimization Algorithms for Buck Converter Systems

Isen

2022

IEEE Access

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The steady-state operation with low error and the fast dynamic response in transient of the DC-DC converter circuits depend on the controller design. The performance of the controller used in DC-DC converters, which vary the level of DC voltage depends on the controller coefficients. Although classical methods are often used to determine these coefficients in controller design, various modern optimization methods have been recently used. In this study, the DC-DC buck converter control simulation is performed with FOPID, PID and TID controllers. Aquila Optimizer, African Vultures Optimization Algorithm and Hunger Games Search optimization algorithms are used to determine the coefficients of these controllers in the literature. However, Fitness-Distance Balance Based Runge Kutta is employed first time for PID controller in buck converter in this study. The performance indices integral absolute error, integral square error, integral time absolute error, and integral time squared error are employed to assess the outcomes. When the results obtained are examined, the FOPID controller gives the best results in the control of the buck converter. These results are obtained by using the coefficients determined by the Fitness-Distance Balance Based Runge Kutta (FDBRUN) optimization algorithm. It has better performance than the other algorithms.INDEX TERMS Fractional-order PID controller, buck converter, optimization techniques

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Section: ) Updating Solutionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Determination of Different Types of Controller Parameters Using Metaheuristic Optimization Algorithms for Buck Converter Systems

Isen

2022

IEEE Access

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“…However, the adjusted gains value with this approach gives significant overshoot with step input and tracking error with sinus input [9] [10]. Cohen Coon is another classical method found in the literature for adjusting PID gains [11] [12]. These two conventional techniques lead to limited performances, especially with nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Optimal Tuning PID Controller Gains from Ziegler-Nichols Approach for an Electrohydraulic Servo System

Mintsa,

Eny,

Senouveau

et al. 2023

JERR

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The Proportional-Integral-Derivative (PID) controller is widely used to control industrial systems due to its ease of implementation, flexibility and well-known theory. The Ziegler-Nichols method is the primary method of adjusting this gains controller. Unfortunately, this method generates limited performances, especially on nonlinear systems. This paper shows the optimization of the gains of the PID controller from the values of the gains obtained by the ZN method. To do this, the Matlab Response Optimization tool is used to control the angular position of an electrohydraulic servo system. The initial conditions of this optimization process are the gain values adjusted by the ZN method. The numerical results obtained after a few iterations show a reduction of approximately in the tracking error for a sinusoidal input. Unfortunately, the performance improvement is not achieved for the step signal input because only the sine wave was used as the signal reference requirement for the optimization procedure.

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“…Radacm et al [ 18 ] proposed a novel DDC control method that combines VRFT and AAC for linear ORM tracking, and the control learning scheme is model-free with respect to the process model. Liu et al [ 19 ] developed an event-based data-driven model-free adaptive controller design algorithm, and an aperiodic neural network weight update law is introduced to estimate the controller parameters in this method; Hao et al [ 20 ] developed a data-driven tracking control method by employing an improved PID neural network and Cohen–Coon approach for a class nonlinear time-varying systems, and the stability of the closed loop system based on the proposed method is proven via the Lyapunov stability theory. Rodrigo et al [ 21 ] proposed an auto-tune PID-like controller with neural networks to help the underwater vehicle adaptively switch driving mode when encountering ocean currents, and experimental results show that the underwater vehicle can achieve a smaller position tracking error based on the proposed method.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Full-Form Model-Free Adaptive Controller for SISO Discrete-Time Nonlinear Systems

Yang

Chen

2022

Entropy

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This study focuses on the full-form model-free adaptive controller (FFMFAC) for SISO discrete-time nonlinear systems, and proposes enhanced FFMFAC. The proposed technique design incorporates long short-term memory neural networks (LSTMs) and fuzzy neural networks (FNNs). To be more precise, LSTMs are utilized to adjust vital parameters of the FFMFAC online. Additionally, due to the high nonlinear approximation capabilities of FNNs, pseudo gradient (PG) values of the controller are estimated online. EFFMFAC is characterized by utilizing the measured I/O data for the online training of all introduced neural networks and does not involve offline training and specific models of the controlled system. Finally, the rationality and superiority are verified by two simulations and a supporting ablation analysis. Five individual performance indices are given, and the experimental findings show that EFFMFAC outperforms all other methods. Especially compared with the FFMFAC, EFFMFAC reduces the RMSE by 21.69% and 11.21%, respectively, proving it to be applicable for SISO discrete-time nonlinear systems.

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Data-Driven Tracking Control for a Class of Unknown Nonlinear Time-Varying Systems Using Improved PID Neural Network and Cohen-Coon Approach

Cited by 9 publications

References 19 publications

Determination of Different Types of Controller Parameters Using Metaheuristic Optimization Algorithms for Buck Converter Systems

Determination of Different Types of Controller Parameters Using Metaheuristic Optimization Algorithms for Buck Converter Systems

Optimal Tuning PID Controller Gains from Ziegler-Nichols Approach for an Electrohydraulic Servo System

An Enhanced Full-Form Model-Free Adaptive Controller for SISO Discrete-Time Nonlinear Systems

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