This paper describes structured neural models and a computationally efficient (suboptimal) nonlinear Model Predictive Control (MPC) algorithm based on such models. The structured neural model has the ability to make future predictions of the process without being used recursively. Thanks to the nature of the model, the prediction error is not propagated. This is particularly important in the ca...

The continuously increasing demands in terms of performance, environmental compatibility and safety motivate the growing interest for optimal control in automotive systems. In practice, however, these methods are seldom used, one of the reasons being the nonlinear nature of the plant which makes the computation more difficult. Several nonlinear optimal control methods have been tested for autom...

We present two new empirical models of radiation belt electron flux at geostationary orbit. GOES-15 measurements 0.8 MeV electrons were used to train a Nonlinear Autoregressive with Exogenous input (NARX) neural network for both modeling values and an upper boundary condition scaling factor (BF). The model utilizes feedback delay 2 time steps (i.e., 5 min steps) the most efficient number hidden...

Abstract The electronic skin described in the article comprises screen-printed graphene-based sensors, intended to be used for robotic applications. precise mathematical model allowing touch pressure estimation is required during its calibration. describes recurrent neural network calibration, which parameters are not homogeneous, and force characteristics have visible hysteretic behaviour. pre...

New magnitude bounds of the frequency response functions for the Nonlinear AutoRegressive model with eXogenous input (NARX) are investigated by exploiting the symmetry of the nth-order generalized frequency response function (GFRF) in its n frequency variables. The new magnitude bound of the nth-order symmetric GFRF is frequency-dependent, and is a polynomial function of the magnitude of the fi...

Chapter Outline 8.1. Classification of SolarForecasting Methods 172 8.2. Deterministic and Stochastic Forecasting Approaches 177 8.2.1. A Critical Appraisal of Physically-Based Forecasting Approaches 177 8.2.2. Satellite Forecasts 178 8.2.3. Sky-Imager Forecasts 179 8.2.4. Data Inputs to Stochastic-Learning Approaches 179 8.2.5. Section Summary 181 8.3. Metrics for Evaluation of Solar-Forecasti...

The properties of an adaptive control system based on a general class of non-linear models are analyzed. The class of models contains NARX models represented by feedforward neural networks with sigmoidal non-linearity, some radial basis-function expansions, local model networks, and some fuzzy systems. We apply a simple adaptive feedback linearizing controller. The analysis takes into account m...