Nonlinear MPC Algorithm Research Articles

Cooperative control for platoon generation of vehicle-to-vehicle networks: a hierarchical nonlinear MPC algorithm

Cooperative control for platoon generation of vehicle-to-vehicle networks: a hierarchical nonlinear MPC algorithm

Long-Horizon Nonlinear Model Predictive Control of Modular Multilevel Converters

Modular Multilevel Converters (MMCs) are a topology that can scale several voltage levels to obtain higher efficiency and lower harmonics than most voltage-source converters. MMCs are very attractive for renewable energy applications and fast charging stations for electric vehicles, where they can improve performance and reduce costs. However, due to the complex architecture and the large number of submodules, the current control of modular multilevel converters is a challenging task. The standard solution in practice relies on hierarchical decoupling and single-input-single-output control loops, which are limited in performance. Linearization-based model predictive control was already proposed for current control in MMCs, as it can optimize transient response and better handle constraints. In this paper, we show that the validity of linear MMC models significantly limits the prediction horizon length, and we propose a nonlinear MPC (NMPC) solution for current control in MMCs to solve this issue. With NMPC, we can employ long prediction horizons up to 100 compared to a horizon of 10, which is the limit for the prediction range of a linear MMC model. Additionally, we propose an alternative MMC prediction model and corresponding cost function, which enables directly controlling the circulating current and improves the capacitor voltages’ behavior. Using the state-of-the-art in sequential quadratic programming for NMPC, we show that the developed NMPC algorithm can meet the real-time constraints of MMCs. A performance comparison with a time-varying linearization-based MPC for an MMC topology used in ultra-fast charging stations for electric vehicles illustrates the benefits of the developed approach.

Central Non-Linear Model-Based Predictive Vehicle Dynamics Control

Considering automated driving, vehicle dynamics control systems are also a crucial aspect. Vehicle dynamics control systems serve as an important influence factor on safety and ride comfort. By reducing the driver’s responsibility through partially or fully automated driving functions, the occupants’ perception of safety and ride comfort changes. Both aspects are focused even more and have to be enhanced. In general, research on vehicle dynamics control systems is a field that has already been well researched. With regard to the mentioned aspects, however, a central control structure features sufficient potential by exploiting synergies. Furthermore, a predictive mode of operation can contribute to achieve these objectives, since the vehicle can act in a predictive manner instead of merely reacting. Consequently, this contribution presents a central predictive control system by means of a non-linear model-based predictive control algorithm. In this context, roll, self-steering and pitch behavior are considered as control objectives. The active roll stabilization demonstrates an excellent control quality with a root mean squared error of 7.6953×10−3 rad averaged over both validation maneuvers. Compared to a vehicle utilizing a conventional control approach combined with a skyhook damping, pitching movements are reduced by 19.75%. Furthermore, an understeering behavior is maintained, which corresponds to the self-steering behavior of the passive vehicle. In general, the central predictive control, thus, increases both ride comfort and safety in a holistic way.

Constrained predictive control of a SISO nonlinear system based on third-order S-PARAFAC Volterra models

This paper is concerned with the identification and nonlinear predictive control approach for a nonlinear process based on a third-order reduced complexity, discrete-time Volterra model called the third-order S-PARAFAC Volterra model. The proposed model is given using the PARAFAC tensor decomposition that provides a parametric reduction compared with the conventional Volterra model. In addition, the symmetry property of the Volterra kernels allows us to further reduce the complexity of the model. These properties allow synthesizing a nonlinear model-based predictive control (NMBPC). Then we construct the general form of a new predictor and we propose an optimization algorithm formulated as a quadratic programming (QP) algorithm under linear and nonlinear constraints. The performance of the proposed third-order S-PARAFAC Volterra model and the developed NMBPC algorithm are illustrated on a numerical simulation and validated on a benchmark such as a continuous stirred-tank reactor system.

Real-Time Control of a Kite-Model using an Auto-Generated Nonlinear MPC Algorithm

AbstractAirborne wind energy systems like power-generating kites promise to become a sustainable and safe alternative for today's fossil fuel based energy production. Controlling these systems is still a challenge due to the fast and highly nonlinear dynamics. A real-world prototype, which has been build at the K.U. Leuven, consists of a carousel which drives an airplane being attached at one of its arms. In the first part of this paper, we propose a nonlinear grey box model which is developed for controlling this kite carousel. In the second part, we present a code generation tool for nonlinear real-time MPC algorithms which exports plain C-code tailored to particular model dynamics. Numerical closed-loop simulations of the kite carousel show that auto-generated code allows to solve the resulting dynamic optimization problems within less than 900 microseconds.

Gas-lift Optimization and Control with Nonlinear MPC

More than 70% of the oil production in Brazil employs gas-lift as the artificial lift method. An effort is being done by some operators to complete new gas-lift wells with down hole pressure gages. This paper proposes a Non-Linear MPC algorithm to control a group of wells receiving gas from a common Gas-Lift Manifold. The objective is to maximize an economic function while minimizing the oscillations of the pressures at the manifold and at the bottom of the wells.

Product property and production rate control of styrene polymerization

Abstract A multivariable multi-rate nonlinear model predictive control (NMPC) strategy is applied to styrene polymerization. The NMPC algorithm incorporates a multi-rate Extended Kalman Filter (EKF) to handle state variable and parameter estimation. A fundamental model is developed for the styrene polymerization CSTR, and control of polymer properties such as number average molecular weight (NAMW) and polydispersity is considered. These properties characterize the final polymer distribution and are strong indicators of the polymer qualities of interest. Production rate control is also demonstrated. Temperature measurements are available frequently while laboratory measurements of concentration and molecular weight distribution are available infrequently with substantial time delays between sampling and analysis. Observability analysis of the augmented system provides guidelines for the design of the augmented disturbance model for use in estimation using the multi-rate EKF. The observability analysis links measurement sets and corresponding observable disturbance models, and shows that measurements of moments of the polymer distribution are essential for good estimation and control. The CSTR is operated at an open-loop unstable steady state. Control simulations are performed under conditions of plant-model structural mismatch and in the presence of parameter uncertainty and disturbances, and the proposed multi-rate NMPC algorithm is shown to provide superior performance compared to linear multi-rate and nonlinear single-rate MPC algorithms. The major contributions of this work are the development of the multi-rate estimator and the measurement design study based on the observability analysis.

Nonlinear model-based predictive control of control nonaffine systems

This paper proposes a new nonlinear model-based predictive control (NLMPC) algorithm, designed to handle control nonaffine systems (nonlinear in the manipulated variable), which is based on a reinterpretation of the prediction equation as a Taylor series expansion. They key feature of this algorithm lies in the use of a process output prediction that accounts for changes in process dynamics as a function of the operating point as well as of the magnitude of the process input change. From this algorithm, a suboptimal NLMPC algorithm is derived to improve computational efficiency for real-time applications. The performance improvement of the new algorithm is illustrated by comparing it with a standard nonlinear model-based predictive control algorithm using three examples. The first two examples are simulations of simple static nonlinear systems, and the third example is a simulation of a semibatch acrylonitrile-butadiene (NBR) emulsion polymerization process.