Tracking Control of Adaptive MIMO Nonlinear Systems Using Fuzzy Logic Control and Extreme Learning Machine
Abdunaser M. Abdusamad, Mohamed Aburakhis · 2023
This paper presents two different control techniques for tracking an output reference signal to any unknown parameter vector. The two techniques are implemented to perform adaptive control approach for MIMO nonlinear systems. The first technique is fuzzy logic control (FLC). In FLC the appropriate approximation can be established based on the number of membership functions. The designed FLC-based controller will make the system outputs follow the reference input signals. The second technique is called an extreme learning machine (ELM). The ELM is a newly developed learning algorithm for generalized single-hidden-layer neural networks, where the output weights are calculated computationally and the hidden node parameters are produced at random. This work focuses on decreasing the control energy in conjunction with reducing the overall MSE, which makes the output tracks the reference input smoothly.