Discrete-Time High Order Neural Control

Sánchez, Edgar N.; Alanís, Alma Y.; Loukianov, Alexander G.

doi:10.1007/978-3-540-78289-6

Cited by 110 publications

(87 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…[27][28][29] Neural network computing power comes from its massive interconnection and its ability to learn and generalize. 27 According to their architecture, neural networks can be classified as follows: 10,27,28 Static neural networks. These kinds of neural networks are capable of approximating any function using a static mapping.…”

Section: Neural Networkmentioning

confidence: 99%

“…10,30 RHONNs are the result of including high-order interactions represented by triplets (y i y j y k ), quadruplets (y i y j y k y l ) and so on to the first-order Hopfield model. 10,30 The RHONN model used in this work is the seriesparallel model model, 10 which is defined aŝ…”

Section: Rhonnmentioning

confidence: 99%

“…It estimates the state of a linear system with additive state and output white noise using a recursive solution in which each update of the state is estimated from the previous estimated state and the new input data. 10,31 Kalman filter training. Kalman filter training for neural networks offers advantages such as reduction in the epoch number and number of required neurons, on-line and off-line training implementation, improvement in learning convergence and also they are more computationally efficient compared to the most used backpropagation methods.…”

Section: Neural Network Trainingmentioning

confidence: 99%

“…Kalman filter training for neural networks offers advantages such as reduction in the epoch number and number of required neurons, on-line and off-line training implementation, improvement in learning convergence and also they are more computationally efficient compared to the most used backpropagation methods. 10,31 Moreover, they have proven to be reliable and practical. 10,27 The training goal is to find the optimal weight vector which minimizes the prediction error.…”

Section: Neural Network Trainingmentioning

confidence: 99%

“…We use a model with time delay because the wireless communication with the tracked robots could induce some delays and we can modify the series-parallel model (10) to accept a state vector of a plan with time delays;…”

Section: Neural Identificationmentioning

confidence: 99%

See 4 more Smart Citations

Real-time neural identification and inverse optimal control for a tracked robot

Rios

Alanis

Lopez-Franco

et al. 2017

Advances in Mechanical Engineering

View full text Add to dashboard Cite

This work presents the implementation in real-time of a neural identifier based on a recurrent high-order neural network which is trained with an extended Kalman filter-based training algorithm and an inverse optimal control applied to a tracked robot. The recurrent high-order neural network identifier is developed without the knowledge of the plant model or its parameters; on the other hand, the inverse optimal control is designed for tracking velocity references. This article includes simulation and real-time results, both using MATLAB Ò , and also the experimental tests use a modified HD2Ò Treaded ATR Tank Robot Platform with wireless communication.

show abstract

Section: Neural Networkmentioning

confidence: 99%

Section: Rhonnmentioning

confidence: 99%

Section: Neural Network Trainingmentioning

confidence: 99%

Section: Neural Network Trainingmentioning

confidence: 99%

Section: Neural Identificationmentioning

confidence: 99%

See 3 more Smart Citations

Real-time neural identification and inverse optimal control for a tracked robot

Rios

Alanis

Lopez-Franco

et al. 2017

Advances in Mechanical Engineering

View full text Add to dashboard Cite

show abstract

Adaptive control of discrete‐time nonlinear systems by recurrent neural networks in quasi‐sliding mode like regime

Salgado

Yáñez

Camacho

et al. 2016

Adaptive Control & Signal

View full text Add to dashboard Cite

The aim of this study was to design an adaptive control strategy based on recurrent neural networks (RNNs). This neural network was designed to obtain a non-parametric approximation (identification) of discretetime uncertain nonlinear systems. A discrete-time Lyapunov candidate function was proposed to prove the convergence of the identification error. The adaptation laws to adjust the free parameters in the RNN were obtained in the same stability analysis. The control scheme used the states of the identifier, and it was developed fulfilling the necessary conditions to establish a behavior comparable with a quasi-sliding mode regime. This controller does not use the regular form of the switching function that commonly appears in the sliding mode control designs. The Lyapunov candidate function to design the controller and the identifier simultaneously requires the existence of positive definite solutions of two different matrix inequalities. As consequence, a class of separation principle was proven when the RNN-based identifier and the controller were designed by the same analysis. Simulations results were designed to show the behavior of the proposed controller solving the tracking problem for the trajectories of a direct current (DC) motor. The performance of the proposed controller was compared with the solution obtained when a classical proportional derivative controller and an adaptive first-order sliding mode controller assuming poor knowledge of the plant. In both cases, the proposed controller showed superior performance when the relation between the tracking error convergence and the energy used to reach it was evaluated.

show abstract

Identification and adaptive PID Control of a hexacopter UAV based on neural networks

Rosales

Soria

Rossomando

2018

Adaptive Control & Signal

View full text Add to dashboard Cite

In this paper, a novel adaptive PID controller for trajectory-tracking tasks is proposed. It is implemented in discrete time over a hexacopter, and it takes into consideration the unmanned aerial vehicles (UAVs) nonlinear model. The PID controller is developed following an adaptive neural technique, and its stability is verified by the Lyapunov discrete theory. Besides, the neural identification of the dynamic model of the UAV is presented to backpropagate output errors to adjust PID gains with the purpose of reducing the control errors. The validation of the proposed algorithm is performed through experimental results with a hexacopter. KEYWORDSadaptive PID, discrete stability analysis, hexacopter, identification, neural networks 74 ;33:74-91. ROSALES ET AL. 75steady-state errors, and the derivative gain is used to modify any system overshoot. However, since quadrotor is a nonlinear underactuated systems, 8 it is not always possible to use PID control directly for the quadrotor system. Consequently, some authors proposed controllers designed based only on static PID for the UAV control such as the controllers presented in other works. [9][10][11] Other studies proposed the UAV control based on PID controllers with artificial intelligence and dynamic properties, such as the controllers presented in the work of Yang et al, 12 where authors defined a single-neuron PID, where the adjusting of the weights is a function of the control errors. Besides, simulations results were displayed to validate the proposal but the stability analysis is omitted. In addition, Xu and Zhou 13 use self-tuning PID controller based on adaptive pole placement to a quadrotor by using a linearized version of the dynamic model. Another class of PID controllers had the capabilities to backpropagate control errors for a better adjust of the controller gains, some of those are described in this work. Tsakalis and Dash 14 proposed an algorithm for  ∞ for online tuning of PID gains. The estimator is very complex to implement, although is robust with respect to the excitation spectrum. The algorithm is validated by using simulations results for different transfer functions. In the work of Liu et al, 15 the design of a Gaussian potential function network PID (GPFN-PID) controller based on GPFN NN was described. The proposed controller had the functions of online training, self-training, and self-adjusting, which made control of the UAV longitudinal channel more effective. However, the results obtained were based on simulations. Babu et al 16 proposed a gradient descent-based methodology for adjusting online the gains of a PID controller based on a cost function defined to penalize errors. The controller is designed only for positioning task and a commercial quadrotor is used to experiment the controller in position task. In the work of Wang et al, 17 an intelligent PID controller based on radial basis functions (RBFs) NN was designed for longitudinal attitude control of a small UAV using a nonlinear model. An RBF NN was utilized for online updatin...

show abstract

Discrete-Time High Order Neural Control

Cited by 110 publications

References 0 publications

Real-time neural identification and inverse optimal control for a tracked robot

Real-time neural identification and inverse optimal control for a tracked robot

Adaptive control of discrete‐time nonlinear systems by recurrent neural networks in quasi‐sliding mode like regime

Identification and adaptive PID Control of a hexacopter UAV based on neural networks

Contact Info

Product

Resources

About