Nonlinear Model Predictive Control Methodology Research Articles

Set Membership identification for NMPC complexity reduction

A Set Membership (SM) approach is proposed to reduce the computational burden of Nonlinear Model Predictive Control (NMPC) algorithms. In particular, a SM identification method is applied to derive an approximation and tight bounds of the NMPC control law, using a set of its values computed offline. These quantities are used online to reduce the dimension and the volume of the search domain of the NMPC optimization algorithm, and to perform a warm start, allowing a significant shortening of the computational time. The developed NMPC methodology is tested in simulation, considering an obstacle avoidance application in a realistic autonomous vehicle scenario. The obtained results demonstrate the effectiveness of the proposed approach in terms of computation time, without affecting the solution quality.

Stabilization of gas-lift oil wells by a nonlinear model predictive control scheme based on adaptive neural network models

Producing oil from gas-lift wells are often faced with severe producing oscillatory flow regimes. A major source of the oscillations is recognized as casing–heading instability which is caused by dynamic interaction between injection gas and multiphase fluid. This phenomenon poses strict production-related challenges in terms of lower average production and strain on downstream equipment. In this paper, an effective solution is proposed based on integration of an online interpretation dynamic model and a nonlinear model predictive control (NMPC) scheme. The paper uses adaptive growing and pruning radial basis function (GAP-RBF) neural networks (NNs) to recursively capture the essential dynamics of casing–heading instability in a nonlinear model structure. Extended Kalman filter (EKF) and unscented Kalman filter (UKF) are comparatively investigated to adaptively train modified GAP-RBF NNs. NMPC methodology is developed on the basis of the identified nonlinear NN model for real-time stabilization of casing–heading instability in an oil reservoir equipped with a gas-lift production well. A set of test studies has been conducted to explore the superior performance of the proposed adaptive NMPC controller under different scenarios for an oil reservoir simulated in ECLIPSE and linked to a complementary gas-lifted oil well simulated in programming environment.

A novel adaptive fuzzy predictive control for hybrid systems with mixed inputs

This paper proposes a new adaptive nonlinear model predictive control (NMPC) methodology for a class of hybrid systems with mixed inputs. For this purpose, an online fuzzy identification approach is presented to recursively estimate an evolving Takagi–Sugeno (eTS) model for the hybrid systems based on a potential clustering scheme. A receding horizon adaptive NMPC is then devised on the basis of the online identified eTS fuzzy model. The nonlinear MPC optimization problem is solved by a genetic algorithm (GA). Diverse sets of test scenarios have been conducted to comparatively demonstrate the robust performance of the proposed adaptive NMPC methodology on the challenging start-up operation of a hybrid continuous stirred tank reactor (CSTR) benchmark problem.

Design of Robust Predictive Control Laws Using Set Membership Identified Models

AbstractThis paper investigates the robust design of nonlinear model predictive control (NMPC) laws that employ approximated models, derived directly from process input‐output data. In particular, a nonlinear set membership (NSM) identification technique is used to obtain a system model and a bound of the related uncertainty. The latter is used to carry out a robust control design, via a min‐max formulation of the optimal control problem underlying the NMPC methodology. A numerical example with a nonlinear oscillator shows the effectiveness of the proposed approach.

A New Weighted Graph-Based Partitioning Algorithm for Decentralized Nonlinear Model Predictive Control of Large-Scale Systems

ABSTRACT 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 algorithm is then developed to partition systems into some sub-graphs based on the weighted graph. A decentralized nonlinear model predictive control (NMPC) methodology is then formulated for the sub-systems. The overall NMPC design methodology is finally evaluated on a process plant benchmark, consisting of two continuous stirred tank reactors (CSTRs) and a flash separator with a recycle path. A set of tracking and regulatory tests is comparatively conducted exploring the successful performance of the proposed algorithm in the context of the decentralized NMPC methodology with respect to an alternative centralized NMPC control scheme.

Robust design of predictive controllers using Set Membership identified models

AbstractThis paper investigates the robust design of Nonlinear Model Predictive Control (NMPC) laws that employ approximated models, derived directly from process input-output data. In particular, a Nonlinear Set Membership (NSM) identification technique is used to derive a system model and a bound of the related uncertainty. The latter is used to carry out a robust control design via a min-max formulation of the optimal control problem underlying the NMPC methodology. A numerical example with a nonlinear oscillator shows the effectiveness of the proposed approach.

Real Time Optimal Guidance of Low‐Thrust Spacecraft: An Application of Nonlinear Model Predictive Control

Real time optimal guidance is considered for a class of low thrust spacecraft. In particular, nonlinear model predictive control (NMPC) is utilized for computing the optimal control actions required to transfer a spacecraft from a low Earth orbit to a mission orbit. The NMPC methodology presented is able to cope with unmodeled disturbances. The dynamics of the transfer are modeled using a set of modified equinoctial elements because they do not exhibit singularities for zero inclination and zero eccentricity. The idea behind NMPC is the repeated solution of optimal control problems; at each time step, a new control action is computed. The optimal control problem is solved using a direct method-fully discretizing the equations of motion. The large scale nonlinear program resulting from the discretization procedure is solved using IPOPT--a primal-dual interior point algorithm. Stability and robustness characteristics of the NMPC algorithm are reviewed. A numerical example is presented that encourages further development of the proposed methodology: the transfer from low-Earth orbit to a molniya orbit.