MIMO (Multiple Input Multiple Output) is a key technology underpinning fourth generation or 4G networks. This technology allows 4G networks to increase throughput. However, the dynamics of the MIMO system are not unde...MIMO (Multiple Input Multiple Output) is a key technology underpinning fourth generation or 4G networks. This technology allows 4G networks to increase throughput. However, the dynamics of the MIMO system are not under control due to the many uncertainties that destabilize the system. This work is therefore very relevant in the sense that an observer can be used to monitor the dynamics of such a system. This work presents a neuro-adaptive observer based on a radial basis function neural network for generic non-linear MIMO systems. Unlike most neuro-adaptive observers, the proposed observer uses a neural network that is non-linear in its parameters. It can therefore be applied to systems with high degrees of nonlinearity without any a priori knowledge of the system dynamics. Indeed, in addition to the fact that neural networks are very good nonlinear approximators, their adaptive behavior makes them powerful tools for observing the state without any a priori knowledge of the dynamics of the system. The learning rule of the neural network is an approach based on the modified backpropagation algorithm: A term has been added to guarantee the robustness of the observer. The proposed approach is not limited by a strong assumption. The stability of the neuro-adaptive observer is demonstrated by the direct Lyapunov method. Simulation results are presented in the context of MIMO signal transmission applied in LTE, to demonstrate the performance of our observer.展开更多
文摘MIMO (Multiple Input Multiple Output) is a key technology underpinning fourth generation or 4G networks. This technology allows 4G networks to increase throughput. However, the dynamics of the MIMO system are not under control due to the many uncertainties that destabilize the system. This work is therefore very relevant in the sense that an observer can be used to monitor the dynamics of such a system. This work presents a neuro-adaptive observer based on a radial basis function neural network for generic non-linear MIMO systems. Unlike most neuro-adaptive observers, the proposed observer uses a neural network that is non-linear in its parameters. It can therefore be applied to systems with high degrees of nonlinearity without any a priori knowledge of the system dynamics. Indeed, in addition to the fact that neural networks are very good nonlinear approximators, their adaptive behavior makes them powerful tools for observing the state without any a priori knowledge of the dynamics of the system. The learning rule of the neural network is an approach based on the modified backpropagation algorithm: A term has been added to guarantee the robustness of the observer. The proposed approach is not limited by a strong assumption. The stability of the neuro-adaptive observer is demonstrated by the direct Lyapunov method. Simulation results are presented in the context of MIMO signal transmission applied in LTE, to demonstrate the performance of our observer.
