Inverse model control using recurrent networks

Kambhampati, C.; Craddock, R.; Tham, M.T.; Warwick, Kevin

doi:10.1016/s0378-4754(99)00116-0

Cited by 29 publications

(15 citation statements)

References 27 publications

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“…Using a discrete time RNNs causes a great dependence of the resulting models on the sampling period used in the process and no information is given about the model trajectories between the sampling instants. The sampling period used with CTRNNs, on the other hand, can be varied without the need for re-training [14,15].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear system identification for predictive control using continuous time recurrent neural networks and automatic differentiation

Al-Seyab

Cao

2008

Journal of Process Control

107

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In this paper, a continuous time recurrent neural network (CTRNN) is developed to be used in nonlinear model predictive control (NMPC) context. The neural network represented in a general nonlinear statespace form is used to predict the future dynamic behavior of the nonlinear process in real time. An efficient training algorithm for the proposed network is developed using automatic differentiation (AD) techniques. By automatically generating Taylor coefficients, the algorithm not only solves the differentiation equations of the network but also produces the sensitivity for the training problem. The same approach is also used to solve the online optimization problem in the predictive controller. The proposed neural network and the nonlinear predictive controller were tested on an evaporation case study. A good model fitting for the nonlinear plant is obtained using the new method. A comparison with other approaches shows that the new algorithm can considerably reduce network training time and improve solution accuracy. The CTRNN trained is used as an internal model in a predictive controller and results in good performance under different operating conditions.

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Section: Introductionmentioning

confidence: 99%

Nonlinear system identification for predictive control using continuous time recurrent neural networks and automatic differentiation

Al-Seyab

Cao

2008

Journal of Process Control

107

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“…If the sampling frequency is to change, the model has to be re-built. On the other hand, a continuous-time RNN can be used for any sampling frequency (Kambhampati, Craddock, Tham, & Warwick, 2000) even for continuous-time NMPC. Although a continuous-time RNN has clear advantages, it has rarely been used in NMPC.…”

Section: Introductionmentioning

confidence: 99%

Identification and predictive control of a multistage evaporator

Atuonwu

Cao

Rangaiah

et al. 2010

Control Engineering Practice

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a b s t r a c tA recurrent neural network-based nonlinear model predictive control (NMPC) scheme in parallel with PI control loops is developed for a simulation model of an industrial-scale five-stage evaporator. Inputoutput data from system identification experiments are used in training the network using the Levenberg-Marquardt algorithm with automatic differentiation. The same optimization algorithm is used in predictive control of the plant. The scheme is tested with set-point tracking and disturbance rejection problems on the plant while control performance is compared with that of PI controllers, a simplified mechanistic model-based NMPC developed in previous work and a linear model predictive controller (LMPC). Results show significant improvements in control performance by the new parallel NMPC-PI control scheme.

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“…Such method was already explored by Izumi et al [3] to be very similar to IMC approach [4] and it can also be regarded as one method of the so-called two-degrees-of-freedom (dof) design for robust control. This method is composed mainly of two parts: one is an inverse system realized by an NN to generate a feedforward control input according to a reference value or the output of a reference model and the other is a feedback Fig.…”

Section: Introductionmentioning

confidence: 99%

A Neuro-interface with fuzzy compensator for controlling nonholonomic mobile robots

Syam

Watanabe

Izumi

2007

Neural Comput Applic

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This paper describes a control method for mobile robots represented by a nonlinear dynamical system, which is subjected to an output deviation caused by drastically changed disturbances. We here propose some controllers in the framework of neuro-interface. It is assumed that a neural network (NN)-based feedforward controller is construcetd by following the concept of virtual master-slave robot, in which a virtual master robot as a feedforward controller is used to control the slave (i.e., actual) robot. The whole system of the present neuro-interface consists of an NN-based feedforward controller, a feedback PD controller and an adaptive fuzzy feedback compensator. The NN-based feedforward controller is trained offline by using a gradient method, the gains of the PD controller are to be chosen constant, and the adaptive fuzzy compensator is constructed with a simplified fuzzy reasoning. Some simulations are presented to confirm the validity of the present approach, where a nonholonomic mobile robot with two independent driving wheels is assmued to have a disturbance due to the change of mass for the robot.

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Inverse model control using recurrent networks

Cited by 29 publications

References 27 publications

Nonlinear system identification for predictive control using continuous time recurrent neural networks and automatic differentiation

Nonlinear system identification for predictive control using continuous time recurrent neural networks and automatic differentiation

Identification and predictive control of a multistage evaporator

A Neuro-interface with fuzzy compensator for controlling nonholonomic mobile robots

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