Observer-Based Adaptive Sliding Mode Control of NPC Converters: An RBF Neural Network Approach

Yin, Yunfei; Liu, Jianxing; Sánchez, Juan Antonio Pastor; Wu, Ligang; Vázquez, Sergio; Leon, Jose I.; Franquelo, Leopoldo G.

doi:10.1109/tpel.2018.2853093

Cited by 139 publications

(57 citation statements)

References 40 publications

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“…It can accurately estimate any smooth nonlinear functions, and it has good local approximation performance. Based on RBFNN in [30], the function γ(t) can be depicted as…”

Section: Rbfnn Preliminariesmentioning

confidence: 99%

“…Construct the Lyapunov function as V = 1 2 σ 2 . The derivatives of σ and V can be derived as (29) and (30).…”

Section: Power Tracking Loopmentioning

confidence: 99%

“…RBFNN is used in [29] to help to solve the problem of detecting and locating circular holes in conducting plates. In [30], RBFNN is applied for NPC to estimate the unknown functions including parameter uncertainties in the power tracking loop, and the stability is approved. In [31], to approximate the equations of stochastic nonlinear systems, RBFNN is used to estimate the state variables.…”

Section: Introductionmentioning

confidence: 99%

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Integral Sliding-Mode Control-Based Direct Power Control for Three-Level NPC Converters

et al. 2020

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Three-level neutral-point-clamped (NPC) converter is widely used in energy conversion systems due to its good properties for high-power systems presenting output waveforms with reduced harmonic distortion. To obtain better system performance, an integral sliding-mode control (ISMC)-based direct power control (DPC) strategy is proposed for NPC converters. The controller achieves three objectives. First, an extended state observer (ESO)-based ISMC strategy, to enforce the active and reactive power to their reference values, is applied in the power tracking loop. ESO is used to reduce the influence of parameter uncertainties. Next, in the voltage regulation loop, a radial basis function neural network (RBFNN)-based adaptive ISMC strategy is applied to regulate the DC-link voltage. RBFNN is used to estimate the load variation, which is considered as a disturbance, to improve the system disturbance rejection ability. An adaptive law is used in the controller to reduce the chattering of reference active power which can reduce the current harmonic distortion. Finally, a proportional-integral (PI) control strategy is applied in the voltage balancing loop to achieve voltage balance between two DC-link capacitors. Experimental results show the effectiveness and superiority of the proposed control strategy for the NPC power converter compared with PI-based DPC strategy.

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“…It can accurately estimate any smooth nonlinear functions, and it has good local approximation performance. Based on RBFNN in [30], the function γ(t) can be depicted as…”

Section: Rbfnn Preliminariesmentioning

confidence: 99%

“…Construct the Lyapunov function as V = 1 2 σ 2 . The derivatives of σ and V can be derived as (29) and (30).…”

Section: Power Tracking Loopmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Integral Sliding-Mode Control-Based Direct Power Control for Three-Level NPC Converters

et al. 2020

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“…To mitigate these issues, numerous seminal solutions have been proposed in various literature. These approaches are commonly based on a variety of advanced nonlinear controllers, including sliding mode control (SMC) [5][6][7][8], model predictive control [9][10][11], neural networks control [12], ect., bearing in mind the distinct features of nonlinear algorithms [13]. In this work, the focus is the SMC method owing to its superior ability to deal with nonlinear problems [14,15].…”

Section: Introductionmentioning

confidence: 99%

A High-Gain Observer-Based Adaptive Super-Twisting Algorithm for DC-Link Voltage Control of NPC Converters

et al. 2020

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Acting as an interface between the grid and many energy systems, the active front-end (AFE) has been widely used in a large variety of industrial applications. In this paper, in order to ensure the fast dynamic performance and good disturbance rejection ability of the AFE, a high-gain observer (HGO) plus adaptive super-twisting algorithm (STA) for the three-level neutral-point-clamped (NPC) converter is proposed. Comparing with the conventional PI control strategy, the proposed controller implements the adaptive STA in the voltage regulator to provide a faster transient response. The gains of the adaptive STA keep varying according to the rules being reduced in steady state but increasing in transient conditions. Therefore, the chattering phenomenon is mitigated and the dynamic response is guaranteed. Additionally, to undermine the impact of external disturbances on the dc-link voltage, a high-efficiency HGO is designed in the voltage regulation loop to reject it. Experimental results based on a three-level NPC prototype are given and compared with the conventional PI method to validate the fast dynamic performance and high disturbance rejection ability of the proposed approach.

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“…Most of the nonlinear techniques operate under strict condition. One such technique is sliding mode control in which it transforms relatively higher order systems into lower order systems. This greatly eases the controller design by analyzing the stability of the lower order systems.…”

Section: Introductionmentioning

confidence: 99%

Real‐time implementation of Chebyshev neural adaptive controller for boost converter

Govindharaj

Mariappan

2020

Int Trans Electr Energ Syst

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Summary An indirect approach is employed to track the desired output voltage of the boost converter by controlling the inductor current in the proposed adaptive backstepping Chebyshev neural network controller, because of the non‐minimum phase nature of the converter. The computational complexity of the neural network is avoided by the use of Chebyshev polynomials as the basis function. The online weight update of the Chebyshev neural network is designed for the closed‐loop system based on the Lyapunov stability analysis to obtain an asymptotically stable system. In the proposed work, the required duty cycle is obtained by a novel method of solving the quadratic equation of the control function instead of the first derivative of the duty cycle to get the desired output voltage from the Lyapunov control function. Detailed analyses of simulations are carried out for a wide range of variations in the set point and load, and the results are compared with that of backstepping and PID controllers. The proposed controller exhibits superior performance than other controllers for the uncertainties caused by disturbances. To ensure its suitability in real time, a prototype is designed for the proposed controller, and the obtained results are compared with that of backstepping and PID controllers. Investigation of experimental results confirms the adaptability of the proposed control scheme as it exhibits accurate and fast response irrespective of disturbances acting upon it.

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Observer-Based Adaptive Sliding Mode Control of NPC Converters: An RBF Neural Network Approach

Cited by 139 publications

References 40 publications

Integral Sliding-Mode Control-Based Direct Power Control for Three-Level NPC Converters

Integral Sliding-Mode Control-Based Direct Power Control for Three-Level NPC Converters

A High-Gain Observer-Based Adaptive Super-Twisting Algorithm for DC-Link Voltage Control of NPC Converters

Real‐time implementation of Chebyshev neural adaptive controller for boost converter

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