Indirect hierarchical FCMAC control for the ball and plate system

Moreno-Armendáriz, Marco A.; Pérez-Olvera, César A.; Rodríguez, Floriberto Ortiz; Rubío, Elsa

doi:10.1016/j.neucom.2010.03.023

Cited by 38 publications

(18 citation statements)

References 21 publications

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“…This system is a dynamic system with two inputs and two outputs. To carry out the simulations a discrete time linear system is obtained taking as equilibrium point the origin for all the states and inputs and a sampling time T m = 0.05 seconds, see details in [14], [15]. This system satisfies Assumption (1).…”

Section: Lemma 1: If Problemmentioning

confidence: 99%

MPC for Tracking Periodic References

Limón

Pereira

Peña

et al. 2016

IEEE Trans. Automat. Contr.

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Abstract-In this paper a new model predictive controller for tracking arbitrary periodic references is presented. The proposed controller is based on a single layer that unites dynamic trajectory planning and control. A design procedure to guarantee that the closed loop system converges asymptotically to the optimal admissible periodic trajectory while guaranteeing constraint satisfaction is provided. In addition, the constraints of the optimization problem solved by the controller do not depend on the reference, allowing for sudden changes in the reference without loosing feasibility. The properties of the proposed controller are demonstrated with a simulation example of a ball and plate system.

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Section: Lemma 1: If Problemmentioning

confidence: 99%

MPC for Tracking Periodic References

Limón

Pereira

Peña

et al. 2016

IEEE Trans. Automat. Contr.

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“…Jacobian identification information where the incremental change of the output weights Δw 1 (k) and the hidden layer weights Δw 0 (k) of the neural network model are given as follows [13,[16][17][18][19][20][21][22][23][24]:…”

Section: Neural Network Indirect Adaptive Controlmentioning

confidence: 99%

“…However, in [13], a performance comparison of neural network training approaches in indirect adaptive control is proposed. In [16], an indirect hierarchical FCMAC control is proposed for the ball and plate system.…”

Section: Introductionmentioning

confidence: 99%

Neural Adaptive PID and Neural Indirect Adaptive Control Switch Controller for Nonlinear MIMO Systems

Slama

Errachdi

Benrejeb

2019

Mathematical Problems in Engineering

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This paper proposes an adaptive switch controller (ASC) design for the nonlinear multi-input multi-output system (MIMO). In fact, the proposed method is an online switch between the neural network adaptive PID (APID) controller and the neural network indirect adaptive controller (IAC). According to the design of the neural network IAC scheme, the adaptation law has been developed by the gradient descent (GD) method. However, the adaptive PID controller is built based on the neural network combining the PID control and explicit neural structure. The strategy of training consists of online tuning of the neural controller weights using the backpropagation algorithm to select the suitable combination of PID gains such that the error between the reference signal and the actual system output converges to zero. The stability and tracking performance of the neural network ASC, the neural network APID, and the neural network IAC are analyzed and evaluated by the Lyapunov function. Then, the controller results are compared between APID, IAC, and ASC, in this paper, applying to a nonlinear system. From simulations, the proposed adaptive switch controller has better effects both on response time and on tracking performance with smallest MSE.

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“…The controller is developed in a constructive form and a rigorous stability analysis is also provided. In Moreno-Armendáriz et al [ 8 ], an indirect adaptive control using hierarchical fuzzy CMAC neuronal network for the ball and plate system was introduced. The proposed controller was validated by means of numerical simulations and experiments.…”

Section: Introductionmentioning

confidence: 99%

“…Experimental evidence that neural networks are efficient in the control of a mechanical systems has been provided in [ 8 , 15 ], for example. However, existing literature reveals a gap in the experimental evaluation of new controllers since most of the published works only consider numerical simulations to assess the performance of the proposed controllers.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neural Network Motion Control of Manipulators with Experimental Evaluations

Puga-Guzmán

Moreno–Valenzuela

Santibáñez

2014

The Scientific World Journal

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A nonlinear proportional-derivative controller plus adaptive neuronal network compensation is proposed. With the aim of estimating the desired torque, a two-layer neural network is used. Then, adaptation laws for the neural network weights are derived. Asymptotic convergence of the position and velocity tracking errors is proven, while the neural network weights are shown to be uniformly bounded. The proposed scheme has been experimentally validated in real time. These experimental evaluations were carried in two different mechanical systems: a horizontal two degrees-of-freedom robot and a vertical one degree-of-freedom arm which is affected by the gravitational force. In each one of the two experimental set-ups, the proposed scheme was implemented without and with adaptive neural network compensation. Experimental results confirmed the tracking accuracy of the proposed adaptive neural network-based controller.

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Indirect hierarchical FCMAC control for the ball and plate system

Cited by 38 publications

References 21 publications

MPC for Tracking Periodic References

MPC for Tracking Periodic References

Neural Adaptive PID and Neural Indirect Adaptive Control Switch Controller for Nonlinear MIMO Systems

Adaptive Neural Network Motion Control of Manipulators with Experimental Evaluations

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