Model Predictive Control of Nonholonomic Chained Systems Using General Projection Neural Networks Optimization

Li, Zhijun; Xiao, Hanzhen; Yang, Chenguang; Zhao, Yiwen

doi:10.1109/tsmc.2015.2398833

Cited by 132 publications

(36 citation statements)

References 37 publications

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“…However, in order to improve the performance of the algorithm it has been developed, in particular, with regard to the avoidance of convergence to a local optimum [20,21].…”

Section: Applications Of Soft Computing Techniques To Indoor Positionmentioning

confidence: 99%

An Improved Particle Swarm Optimization-Based Feed-Forward Neural Network Combined with RFID Sensors to Indoor Localization

Wang

Shi

2017

Information

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Location-based services (LBS) have long been recognized as a significant component of the emerging information services. However, the localization cost and the performance of algorithm still need to be optimized. In the study, an improved particle swarm optimization algorithm based on a feed-forward neural network (IMPSO-FNN) combined with RFID sensors is proposed, which can achieve the best indoor positioning location and overcome the problems effectively. In IMPSO-FNN, an improved PSO algorithm (IMPSO) is developed to determine the optimal connecting weights and markedly optimize the network parameters and structural parameters for the FNN, and then an optimal location prediction model is established by the IMPSO-FNN. To avoid the interference of environmental noise for the experimental data, some preprocessing methods are used during the positioning process. The computational results for learning two continuous functions show that the proposed positioning algorithm has a faster convergence rate and higher generalization performance. The model evaluation results also verify that the proposed positioning method really is superior to other algorithms in terms of the learning ability, efficiency, and positioning accuracy.

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“…However, in order to improve the performance of the algorithm it has been developed, in particular, with regard to the avoidance of convergence to a local optimum [20,21].…”

Section: Applications Of Soft Computing Techniques To Indoor Positionmentioning

confidence: 99%

An Improved Particle Swarm Optimization-Based Feed-Forward Neural Network Combined with RFID Sensors to Indoor Localization

Wang

Shi

2017

Information

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“…In [32] [33], the control strategies based on both adaptive NN and disturbance observer were designed for large-scale systems and 3-DOF model helicopters with uncertainties, nonlinearities, and unknown external disturbance. A general projection NN was employed to handle the quadratic programming problem caused by model predictive control [34]. [35] developed an adaptive NN controller based on full-state feedback for an uncertain robot manipulator, which had full-state constraints.…”

Section: Introductionmentioning

confidence: 99%

Haptic Identification by ELM-Controlled Uncertain Manipulator

Yang

Huang

Cheng

et al. 2017

IEEE Trans. Syst. Man Cybern, Syst.

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146

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Abstract-This paper presents an extreme learning machine (ELM) based control scheme for uncertain robot manipulators to perform haptic identification. ELM is used to compensate for the unknown nonlinearity in the manipulator dynamics. The ELM enhanced controller ensures that the closed-loop controlled manipulator follows a specified reference model, in which the reference point as well as the feedforward force is adjusted after each trial for haptic identification of geometry and stiffness of an unknown object. A neural learning law is designed to ensure finite-time convergence of the neural weight learning, such that exact matching with the reference model can be achieved after the initial iteration. The usefulness of the proposed method is tested and demonstrated by extensive simulation studies.

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“…The neural networks [5] and fuzzy logic systems [6] have become the two main tools which can effectively deal with the unknown functions in the systems. In [7,8], these are studies of some actual engineering systems with uncertain parameters. In [9,10], the NN is used to approximate several random perturbations and unknown functions.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neural Networks Control Using Barrier Lyapunov Functions for DC Motor System with Time‐Varying State Constraints

2018

Complexity

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This paper proposes an adaptive neural network (NN) control approach for a direct-current (DC) system with full state constraints. To guarantee that state constraints always remain in the asymmetric time-varying constraint regions, the asymmetric time-varying Barrier Lyapunov Function (BLF) is employed to structure an adaptive NN controller. As we all know that the constant constraint is only a special case of the time-varying constraint, hence, the proposed control method is more general for dealing with constraint problem as compared with the existing works on DC systems. As far as we know, this system is the first studied situations with timevarying constraints. Using Lyapunov analysis, all signals in the closed-loop system are proved to be bounded and the constraints are not violated. In this paper, the effectiveness of the control method is demonstrated by simulation results.

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Model Predictive Control of Nonholonomic Chained Systems Using General Projection Neural Networks Optimization

Cited by 132 publications

References 37 publications

An Improved Particle Swarm Optimization-Based Feed-Forward Neural Network Combined with RFID Sensors to Indoor Localization

An Improved Particle Swarm Optimization-Based Feed-Forward Neural Network Combined with RFID Sensors to Indoor Localization

Haptic Identification by ELM-Controlled Uncertain Manipulator

Adaptive Neural Networks Control Using Barrier Lyapunov Functions for DC Motor System with Time‐Varying State Constraints

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