Abstract
In this study, a novel Multivariable Adaptive Neural Network Controller (MANNC) is developed for coupled model-free n-input n-output systems. The learning algorithm of the proposed controller does not rely on the model of a system and uses only the history of the system inputs and outputs. The system is considered as a ‘black box’ with no pre-knowledge of its internal structure. By online monitoring and possessing the system inputs and outputs, the parameters of the controller are adjusted. Using the accumulated gradient of the system error along with the Lyapunov stability analysis, the weights’ adjustment convergence of the controller can be observed, and an optimal training number of the controller can be selected. The Lyapunov stability of the system is checked during the entire weight training process to enable the controller to handle any possible nonlinearities of the system. The effectiveness of the MANNC in controlling nonlinear square multiple-input multiple-output (MIMO) systems is demonstrated via three simulation studies covering the cases of a time-invariant nonlinear MIMO system, a time-variant nonlinear MIMO system, and a hybrid MIMO system, respectively. In each case, the performance of the MANNC is compared with that of a properly selected existing counterpart. Simulation results demonstrate that the proposed MANNC is capable of controlling various types of square MIMO systems with much improved performance over its existing counterpart. The unique properties of the MANNC will make it a suitable candidate for many industrial applications.
Highlights
Over the past few years, there has been a significant improvement in controllingMultiple-Input Multiple-Output (MIMO) systems using adaptive control methods [1]
This controller is a combination of several Single-Input SingleOutput (SISO) controllers cascaded together and cannot be further developed for coupled multiple-input multiple-output (MIMO) systems
This neuron accumulates the outputs of the hidden layer and forms the control command applied to the lth output of the n × n MIMO system
Summary
Over the past few years, there has been a significant improvement in controlling. Multiple-Input Multiple-Output (MIMO) systems using adaptive control methods [1]. Even at the circumstances where an exact model of a MIMO system could be identified, the controller designed for the predicted model of the system may still be subjected to conditional variations both internal and external to the system [8] Due to these practical problems associated with model-based approaches, many existing adaptive control schemes are seen to be impractical or limited in controlling real industrial MIMO plants. In [45], a neural network controller and its associated learning rules are proposed, which can be successfully applied to Single-Input MultiOutput (SIMO) plants This controller is a combination of several SISO controllers cascaded together and cannot be further developed for coupled MIMO systems. In order to overcome the above-listed deficiencies, in this study, a novel model-free Multivariable Adaptive Neural Network Controller (MANNC) is proposed for controlling coupled MIMO systems in which the following are true. There are 3n2 weights in the output layer that are associated with the hidden-layer neurons and decide the impact of each neuron of the hidden layer on the generation of the inputs applied to the MIMO system
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