an adaptive input-output linearization method for general nonlinear systems is developed without using states of the system. another key feature of this structure is the fact that, it does not need model of the system. in this scheme, neurolinearizer has few weights, so it is practical in adaptive situations.  online training of neurolinearizer is compared to model predictive recurrent training...

The availability of substantial computational capability in industrial environments has rendered the traditional S-factor prediction of cook time o control DP inadequate, compared to what can be achieved with more sophisticated predictive models. A fundamental model, in simplified form, ith adjustable parameters, was developed and its accuracy to predict DP based on given operating conditions w...

There is a strong push within the mining sector to automate equipment such as large excavators. A challenging problem is the control of motion on high inertia degrees of freedom where the actuators are constrained in the power they can deliver to and extract from the system and the machine’s underlying control system sits between the automation system and the actuators. The swing motion of an e...

The predictive cruise control (PCC) is a promising method to optimize energy consumption of vehicles, especially the heavy-duty vehicles (HDV). Due limited sensing range and computational capabilities available on-board, conventional PCC system can only obtain sub-optimal speed trajectory based on shorter prediction horizon. recently emerging information communication technologies such as vehic...