Adaptive Tracking Control of State Constraint Systems Based on Differential Neural Networks: A Barrier Lyapunov Function Approach

Fuentes-Aguilar, Rita Q.; Chaírez, Isaac

doi:10.1109/tnnls.2020.2966914

Cited by 48 publications

(21 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The process of applying Lyapunov-based stability confirms that identification error has an upper ultimate bound [21,22]. The suggested Lyapunov function has a quadratic form that depends on identification error and SDNN weights.…”

Section: Formulation Of Spiking-differential-neural-network-based Modelmentioning

confidence: 84%

Differential Neural Network-Based Nonparametric Identification of Eye Response to Enforced Head Motion

et al. 2022

Self Cite

View full text Add to dashboard Cite

Dynamic motion simulators cannot provide the same stimulation of sensory systems as real motion. Hence, only a subset of human senses should be targeted. For simulators providing vestibular stimulus, an automatic bodily function of vestibular–ocular reflex (VOR) can objectively measure how accurate motion simulation is. This requires a model of ocular response to enforced accelerations, an attempt to create which is shown in this paper. The proposed model corresponds to a single-layer spiking differential neural network with its activation functions are based on the dynamic Izhikevich model of neuron dynamics. An experiment is proposed to collect training data corresponding to controlled accelerated motions that produce VOR, measured using an eye-tracking system. The effectiveness of the proposed identification is demonstrated by comparing its performance with a traditional sigmoidal identifier. The proposed model based on dynamic representations of activation functions produces a more accurate approximation of foveal motion as the estimation of mean square error confirms.

show abstract

Section: Formulation Of Spiking-differential-neural-network-based Modelmentioning

confidence: 84%

Differential Neural Network-Based Nonparametric Identification of Eye Response to Enforced Head Motion

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…Instead, the machine learning-based control method uses a software method (e.g., an approximation algorithm) to simulate the control signal. Particularly, as neural networks (NNs) have become a very popular machine learning technique for control tasks due to their strong ability of function approximation [5]- [11], the NN-based controllers (NNCs) have been used in PEC. The NNCs use a well-trained NN to generate control signals to the PCS.…”

Section: Introductionmentioning

confidence: 99%

Resource-Aware Distributed Differential Evolution for Training Expensive Neural-Network-Based Controller in Power Electronic Circuit

Liu

Zhan

Zhang

2022

IEEE Trans. Neural Netw. Learning Syst.

View full text Add to dashboard Cite

The neural-network (NN)-based control method is a new emerging promising technique for controller design in a power electronic circuit (PEC). However, the optimization of NN-based controllers (NNCs) has significant challenges in two aspects. The first challenge is that the search space of the NNC optimization problem is such complex that the global optimization ability of the existing algorithms still needs to be improved. The second challenge is that the training process of the NNC parameters is very computationally expensive and requires a long execution time. Thus, in this article, we develop a powerful evolutionary computation-based algorithm to find a high-quality solution and reduce computational time. First, the differential evolution (DE) algorithm is adopted because it is a powerful global optimizer in solving a complex optimization problem. This can help to overcome the premature convergence in local optima to train the NNC parameters well. Second, to reduce the computational time, the DE is extended to distribute DE (DDE) by dispatching all the individuals to different distributed computing resources for parallel computing. Moreover, a resource-aware strategy (RAS) is designed to further efficiently utilize the resources by adaptively dispatching individuals to resources according to the real-time performance of the resources, which can simultaneously concern the computing ability and load state of each resource. Experimental results show that, compared with some other typical evolutionary algorithms, the proposed algorithm can get significantly better solutions within a shorter computational time.

show abstract

“…Firstly, the state identification error is usually used to design the learning law for the existed neural identifier [ 20 – 22 ], which may affect the accuracy and convergence speed of the entire control loop owing to the inherent parameter drift problem. Secondly, most of the existed indirect adaptive control methods [ 23 , 24 ] rely on the well-known linear separation principal to design the identifier and controller separately, which may affect the closed-loop stability when confronting the uncertain system dynamics. In this paper, we propose a new PNN-based indirect adaptive tracking control method for model free nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Parametric Neural Network-Based Model Free Adaptive Tracking Control Method and Its Application to AFS/DYC System

Zhi-jun

Lü

Zhou

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

This paper deals with adaptive nonlinear identification and trajectory tracking problem for model free nonlinear systems via parametric neural network (PNN). Firstly, a more effective PNN identifier is developed to obtain the unknown system dynamics, where a parameter error driven updating law is synthesized to ensure good identification performance in terms of accuracy and rapidity. Then, an adaptive tracking controller consisting of a feedback control term to compensate the identified nonlinearity and a sliding model control term to deal with the modeling error is established. The Lyapunov approach is synthesized to ensure the convergence characteristics of the overall closed-loop system composed of the PNN identifier and the adaptive tracking controller. Simulation results for an AFS/DYC system are presented to confirm the validity of the proposed approach.

show abstract

Adaptive Tracking Control of State Constraint Systems Based on Differential Neural Networks: A Barrier Lyapunov Function Approach

Cited by 48 publications

References 28 publications

Differential Neural Network-Based Nonparametric Identification of Eye Response to Enforced Head Motion

Differential Neural Network-Based Nonparametric Identification of Eye Response to Enforced Head Motion

Resource-Aware Distributed Differential Evolution for Training Expensive Neural-Network-Based Controller in Power Electronic Circuit

Parametric Neural Network-Based Model Free Adaptive Tracking Control Method and Its Application to AFS/DYC System

Contact Info

Product

Resources

About