Fuzzy–neural Model Predictive Control of Multivariable Processes

Petrov, Michail; Ahmed, Sevil; Ichtev, Alexandar; Taneva, Albena

doi:10.5772/16828

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…The state-space model is preferred over the transfer function, since it drastically reduces the computation required during the optimization procedure. 39…”

Section: Intelligent Position Control Of the 3-ppss Parallel Manipulatormentioning

confidence: 99%

Dynamic modeling, system identification and comparative study of various control strategies for a spatial parallel manipulator

Thomas

George

Sreedharan

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part I:

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The significant challenges seen with the mathematical modeling and control of spatial parallel manipulators are its difficulty in the kinematic formulation and the inability to real-time control. The analytical approaches for the determination of the kinematic solutions are computationally expensive. This is due to the passive joints, solvability issues with non-linear equations, and inherent kinematic constraints within the manipulator architecture. Therefore, this article concentrates on an artificial neural network–based system identification approach to resolve the complexities of mathematical formulations. Moreover, the low computation time with neural networks adds up to its advantage of real-time control. Besides, this article compares the performance of a constant gain proportional–integral–derivative (PID), variable gain proportional–integral–derivative, model predictive controller, and a cascade controller with combined variable proportional–integral–derivative and model predictive controller for real-time tracking of the end-effector. The control strategies are simulated on the Simulink model of a 6-degree-of-freedom 3-PPSS (P—prismatic; S—spherical) parallel manipulator. The simulation and real-time experiments performed on the fabricated manipulator prototype indicate that the proposed cascade controller with position and velocity compensation is an appropriate method for accurate tracking along the desired path. Also, training the network using the experimentally generated data set incorporates the mechanical joint approximations and link deformities present in the fabricated model into the predicted results. In addition, this article showcases the application of Euler–Lagrangian formalism on the 3-PPSS parallel manipulator for its dynamic model incorporating the system constraints. The Lagrangian multipliers include the influence of the constraint forces acting on the manipulator platform. For completeness, the analytical model results have been verified using ADAMS for a pre-defined end-effector trajectory.

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“…The state-space model is preferred over the transfer function, since it drastically reduces the computation required during the optimization procedure. 39…”

Section: Intelligent Position Control Of the 3-ppss Parallel Manipulatormentioning

confidence: 99%

Dynamic modeling, system identification and comparative study of various control strategies for a spatial parallel manipulator

Thomas

George

Sreedharan

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…An approach for predictive control, based on State-Space Takagi-Sugeno model is discussed in [15]. As well as, in our previous works we discuss a hybrid approach to describe a Hammerstein FuzzyNeural State-Space model [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Distributed fuzzy-neural state-space predictive control

Todorov

Terziyska

Doukovska

2015

2015 20th International Conference on Process Control (PC)

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This paper describes the development of nonlinear state-space predictive controller based on distributed fuzzyneural model. The presented approach assumes a state-space representation in order to obtain more compact form of the model, without statement of a great number of parameters needed to represent nonlinear relations. To increase the flexibility of the network, a set of fuzzy inferences is used to estimate the current system states, as well as to construct a simple predictor needed to update the future system behavior along the defined horizons. At each sampling period an optimization task performing Quadratic Programming minimization assuming the imposed constraints on the system parameters is solved. The performance of the proposed controller is assessed by simulation experiments in modeling and control of nonlinear systems with complicated dynamics.

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“…They seem to be ideally suited to deal with MIMO plants. Thus, we can find some works on dynamic decoupling with the use of MPC [20]- [28]. Most of them have been created for specific TITO nonlinear plants [21], [23], [25]- [27].…”

Section: Introductionmentioning

confidence: 99%

Effective Use of MPC for Dynamic Decoupling of MIMO Systems

Dworak¹,

Goyal²,

Aggarwal³

et al. 2019

ElAEE

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The article analyses possible ways of using predictive controllers to perform control tasks and dynamic decoupling for dynamic systems with Multiple Inputs and Multiple Outputs (MIMO). The results of experiments on the selected reference plant are presented, showing the effectiveness of individual decoupling methods. These are also compared to those obtained in typical control systems with Proportional-Integral-Derivative (PID) controllers. Recommendations are made on how to tune model predictive controllers (MPC) for their effective use for MIMO plants.

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Fuzzy–neural Model Predictive Control of Multivariable Processes

Cited by 3 publications

References 8 publications

Dynamic modeling, system identification and comparative study of various control strategies for a spatial parallel manipulator

Dynamic modeling, system identification and comparative study of various control strategies for a spatial parallel manipulator

Distributed fuzzy-neural state-space predictive control

Effective Use of MPC for Dynamic Decoupling of MIMO Systems

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