Different‐factor compact‐form model‐free adaptive control with neural networks for MIMO nonlinear systems

Abstract

In this paper, a different-factor structure-based compact-form model-free adaptive control method with neural networks (DF-CFMFAC-NN) is proposed for a class of general multiple-input and multiple-output (MIMO) nonlinear systems. Its novelty lies in that it is a pure data-driven control method using merely input/output data without any model information involved. Moreover, by virtue of the different-factor structure, it addresses the problem in the prototype CFMFAC that mainly deals with a class of MIMO systems with similar channel characteristics under a fixed topology. Aiming at different characteristics between control channels, widely existing in MIMO systems, DF-CFMFAC-NN with learning parameters shows powerful tracking ability and flexible design for each control channel, and learning parameters are auto-tuned by back propagation neural networks (BPNNs) online based on their self-learning and self-adapting properties. In the tuning process, a one-step-ahead partial derivative is directly derived by the dynamic linearization technique utilized in DF-CFMFAC, which greatly improves the prediction accuracy of parameters.The convergence of tracking error and the stability of the tuning process are guaranteed by rigorous theoretical analysis. A coal mill system is provided to demonstrate its effectiveness and practicability, demonstrating it to be a promising control method for MIMO nonlinear systems.

Model-Free Adaptive Control of Hydrometallurgy Cascade Gold Leaching Process with Input Constraints

“…y u (29) where u(k) ∈ R m and y(k) ∈ R, respectively, represent the input and output of the system at time k; n u and n y are the orders of the input and output of the system, respectively;…”

“…The learning parameters are auto-tuned by back propagation neural networks. This control algorithm has been successfully applied to coal mill system . Combining a sampled-data parameter estimator to estimate the unknown partial derivatives and a sampled-data observer to estimate the residual nonlinear uncertainty, an observer-based SMFAC scheme has been proposed for continuous-time nonlinear nonaffine systems with input constraints.…”

“…This control algorithm has been successfully applied to coal mill system. 29 Combining a sampled-data parameter estimator to estimate the unknown partial derivatives and a sampled-data observer to estimate the residual nonlinear uncertainty, an observer-based SMFAC scheme has been proposed for continuous-time nonlinear nonaffine systems with input constraints. The constraint on the input rate is also considered in the control law as the transition condition.…”

Model‐free predictive control for a class of asymmetric nonlinear systems

“…For the car‐like mobile robot systems, the literature [18] proposes a data‐driven generalized predictive control method based on interval type‐2 T‐S fuzzy neural network, which does not rely on the mathematical model of the mobile robot but only on the historical data of its operation. Literature [19] proposes a model‐free adaptive control method based on compact neural network with different factor structures for a kind of general MIMO systems using data‐driven control technology. In order to suppress the influence of system parameter changes, a nonlinear model‐based predictive control strategy for constrained systems based on an adaptive neural network predictor is proposed in literature [20].…”