The growing interest in model predictive control for nonlinear systems, also called NMPC, is motivated by the fact that today’s processes need to be operated under tighter performance specifications to guarantee profitable and environmentally safe production. One of the remaining essential problems for NMPC is the high on-line computational load. At each sampling instant, a nonlinear optimal co...

Batch processes play a significant role in the production of most modern highvalue added products. The paper illustrates the benefits of nonlinear model predictive control (NMPC) for the setpoint tracking control of an industrial batch polymerization reactor. Real-time feasibility of the on-line optimization problem from the NMPC is achieved using an efficient multiple shooting algorithm. A rea...

The current flexibility of the energy market requires operating steam turbines that have challenging operation requirements such as variable conditions and higher number startups. This article proposes an advanced control system based on Nonlinear Model Predictive Control (NMPC) technique, which allows to speed up start-up increase produced while maintaining rotor stress a constraint variable. ...

Air separation units (ASU) pose a classic problem for nonlinear system control. This paper proposes a framework that integrates nonlinear model predictive control (NMPC) and moving horizon estimation (MHE). We prove that the proposed method achieves offset free regulatory behavior, even in the presence of plant-model mismatches. If the plant uncertainty structure is known, the proposed framewor...

Combining multiple neural networks appears to be a very promising approach for improving neural network generalization since it is very difficult, if not impossible, to develop a perfect single neural network. Therefore in this paper, a nonlinear model predictive control (NMPC) strategy using multiple neural networks is proposed. Instead of using a single neural network as a model, multiple neu...

Nonlinear Model Predictive Control (NMPC) has gained wide attention through the application of dynamic optimization. However, this approach is susceptible to computational delay, especially if the optimization problem cannot be solved within one sampling time. In this paper we propose an advanced-multi-step NMPC (amsNMPC) method based on nonlinear programming (NLP) and NLP sensitivity. This met...

This paper proposes a grouping algorithm for partitioning large-scale nonlinear dynamical systems based on graph theory. The algorithm incorporates a novel scheme to quantify the strengths of graph edges, representing the degree of couplings among the system variables via sensitivity functions. This leads to a weighted graph topology with different weights on the obtained graph edges. An algori...

A nonlinear model predictive control (NMPC) algorithm was developed to dose the chemotherapeutic tamoxifen to mice bearing breast cancer xenografts. A novel saturating rate cell-cycle model (SCM) was developed to capture unperturbed tumor growth dynamics, and a bilinear tumor kill term was included in the G-phase to account for the cycle-specific nature of tamoxifen and its active metabolite. D...

Model-based predictive control (MPC) is one of the most efficient techniques that is widely used in industrial applications. In such controllers, increasing the prediction horizon results in better selection of the optimal control signal sequence. On the other hand, increasing the prediction horizon increase the computational time of the optimization process which make it impossible to be imple...

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...