Real-time random reference tracking nonlinear model predictive control: a case study on wind turbines.

Nonlinear Model Predictive Control approach in design of Adaptive Cruise Control with automated switching to cruise control

In this work a nonlinear model predictive control method for a floating wind turbine is presented. A reduced nonlinear model including disturbance preview of wind and waves is derived and implemented to compute optimal input trajectories for collective pitch and the generator torque. A cost functional is introduced which fulfills all desired constraints and controller goals for above rated wind conditions. The controller is tested for extreme and fatigue load cases and a significant reduction of the power and rotor speed deviations is obtained. Furthermore, ultimate tower loads and damage equivalent loads on shaft and blades are decreased. Although more detailed testing is necessary, this preliminary results show the advantages of nonlinear model predictive control for floating wind turbines.

Hydrogen is crucial for achieving SDGs by driving energy transition and combating climate change. Proton exchange membrane fuel cell technology, leveraging hydrogen, faces challenges in meeting high‐power demands. The multistack fuel cell system (MFCS) tackles this by integrating multiple substacks, yet its air supply needs meticulous control. Proportional integral derivative (PID) decoupling from single‐stack falls short of MFCS. This article proposes nonlinear model predictive control (NMPC) for optimized air flow and pressure decoupling. Modeling MFCS's air system and designing a predictive model, it is aimed to ensuring precise control of air flow and pressure in each substack. The decoupling experiments show that NMPC outperforms PID, accurately managing air flow and pressure and reducing load fluctuations. For air mass flow, NMPC cuts mean‐absolute error (MAE) by 64.56% and root‐mean‐square error (RMSE) by 81.36%. For pressure, MAE drops 81.23% and RMSE 83.59%. Comprehensive step load tests confirm NMPC's precise, dynamic regulation too, compared to PID, NMPC lowers average MAE for air mass by 20.67%, pressure by 32.22%. RMSE improvements of 31.08% and 33.23% highlight NMPC's strength. NMPC's quick response mitigates coupling issues, enhancing vehicle load adaptability.

The climate in a greenhouse is a highly nonlinear complex system that contains nonlinearity. In addition, there are dependencies between each system state. In order to simultaneously control multiple system states in greenhouse climate, this paper develops a nonlinear model predictive control (NMPC) framework for the greenhouse climate control to minimize the total control cost and the constraint violation probability. The nonlinear dynamic model of the greenhouse climate, including temperature, humidity, CO2 level, and lighting, will be first constructed. After constructing all dynamic models, historical weather data is gathered to identify the system parameters for the nonlinear greenhouse climate model. A nonlinear optimization problem can then be developed to obtain the optimal control inputs. A case study on controlling a greenhouse located in New York state is used to demonstrate the advantages of the proposed NMPC framework.

One of the most critical tasks in tractor operation is the accurate steering during field operations, e.g., accurate trajectory following during mechanical weeding or spraying, to avoid damaging the crop or planting when there is no crop yet. To automate the trajectory following problem of an autonomous tractor-trailer system and also increase its steering accuracy, a nonlinear model predictive control approach has been proposed in this paper. For the state and parameter estimation, moving horizon estimation has been chosen since it considers the state and the parameter estimation within the same problem and also constraints both on inputs and states can be incorporated. The experimental results show the accuracy and the efficiency of the proposed control scheme in which the mean values of the Euclidean error for the tractor and the trailer, respectively, are 6.44 and 3.61 cm for a straight line trajectory and 49.78 and 41.52 cm for a curved line trajectory.

Modern chemical processes need to operate around time‐varying operating conditions to optimize plant economy, in response to dynamic supply chains (e.g., time‐varying specifications of product and energy costs). As such, the process control system needs to handle a wide range of operating conditions whilst optimizing system performance and ensuring stability during transitions. This article presents a reference‐flexible nonlinear model predictive control approach using contraction based constraints. Firstly, a contraction condition that ensures convergence to any feasible state trajectories or setpoints is constructed. This condition is then imposed as a constraint on the optimization problem for model predictive control with a general (typically economic) cost function, utilizing Riemannian weighted graphs and shortest path techniques. The result is a reference flexible and fast optimal controller that can trade‐off between the rate of target trajectory convergence and economic benefit (away from the desired process objective). The proposed approach is illustrated by a simulation study on a CSTR control problem.

Dynamic modelling and nonlinear model predictive control of a Fluid Catalytic Cracking Unit

Control of machine learning models has emerged as an important paradigm for a broad range of robotics applications. In this paper, we present a sampling-based nonlinear model predictive control (NMPC) approach for control of neural network dynamics. We show its design in two parts: 1) formulating conventional optimization-based NMPC as a Bayesian state estimation problem, and 2) using particle filtering/smoothing to achieve the estimation. Through a principled sampling-based implementation, this approach can potentially make effective searches in the control action space for optimal control and also facilitate computation toward overcoming the challenges caused by neural network dynamics. We apply the proposed NMPC approach to motion planning for autonomous vehicles. The specific problem considers nonlinear unknown vehicle dynamics modeled as neural networks as well as dynamic on-road driving scenarios. The approach shows significant effectiveness in successful motion planning in case studies.

Airborne Wind Energy (AWE) systems generate energy by flying a tethered airfoil across the wind flow at a high velocity. Tethered flight is a fast, strongly nonlinear, unstable and constrained process, motivating control approaches based on fast Nonlinear Model Predictive Control (NMPC) and state estimation approaches based on Moving Horizon Estimation (MHE). Dual-Airfoil AWE systems, i.e. systems with two airfoils attached to a Y-shaped tether have been shown to be more effective than systems based on a single airfoil. This paper proposes a control scheme for a dual-airfoil AWE system based on NMPC and MHE and studies its performance in a realistic scenario based on state-of-the-art turbulence models.

In this paper, the parameterized dynamical model of the diesel engine intake and exhaust system using a data-based method, namely a Gain-Scheduled model is proposed and designed based on the data from a virtual engine test bench under normal load conditions. In the first step, the Multiple Input Multiple Output model structure is defined with five inputs and two outputs. Using the constructed model, it is possible to establish the relations between intake Manifold Pressure, Air Mass Flow, the control signals, and changes of the load. Then, the model is further used to design a Nonlinear Model Predictive Control controller, aimed at optimizing the efficiency of the combustion system in terms of the control reference value tracking with respect to emission reduction. This paper follows a model-based design approach to construct the Nonlinear Model Predictive Control objective function for the engine intake Manifold Pressure and Air Mass Flow nonlinear control problem. The proposed data-based dynamical modeling method is shown to increase the flexibility for the modeling of nonlinear plant at a low cost in computational requirements. The experimental results illustrate that the optimized nonlinear control approach significantly improve the control reference tracking performance and the exhaust emissions against the standard decentralized Single Input Single Output control in the standard production Engine Control Unit.

For omnidirectional mobile robot (OMR), it is difficult to achieve adequate tracking flexibility and accuracy with nonlinear model predictive control (NMPC) in a fixed horizon due to the dynamic change of system variables. This paper proposed a strategy of NMPC with self-turned prediction horizon, which can adapt to the change of system variables and perform a stable tracking control. Firstly, we construct a kinematics model for the Ackerman mode of the OMR. Secondly, the NMPC strategy is adapted to trajectory tracking. Moreover, a method to select the appropriate prediction of horizon is proposed by considering the influence of velocity and road curvature on the system. And the switch problem of the self-turned prediction horizon is solved through the division of the terminal region. Theoretical analysis reveals that the NMPC method is stable, and the convergence of the state errors can be guaranteed. Finally, simulation experiments on a four-wheeled OMR validate the effectiveness and superiority of the self-turned prediction horizon control method comparing with the fixed horizon NMPC method or other variable prediction horizon NMPC methods.

In this paper, an online gain scheduling algorithm for Proportional Navigation is presented, whose major components are multiple shooting condensing and a nonlinear model predictive control closed-loop scheme. The required reference open-loop solution is obtained by a multiple shooting transcription that transforms the infinite-dimensional optimal control problem to a nonlinear parameter optimization problem, the solution of which is acquired by a sequential quadratic programming routine. In optimization, multiple shooting condensing is particulary effective when the number of transcribed variables with regard to controls is smaller than the number of transcribed variables with regard to system states. This is the case for online gain scheduling applied to Proportional Navigation guidance. The closed-loop nonlinear model predictive control method provides updates on the reference open-loop solution by solving quadratic update problems resulting from a second-order Taylor expansion of the problem’s Lagrangian. Therein, the quadratic problem may be greatly reduced in size by deploying the condensing method that essentially replaces the dependency of the multiple shooting constraints with respect to the state trajectories by the corresponding control dependency. Preliminary results of a terminal endgame guidance scenario in missile interception are presented that are very promising in terms of robustness and computational cost, and therefore, show an improved online applicability.

This paper addresses the problem of cooperative object transportation for multiple Underwater Vehicle Manipulator Systems (UVMSs) in a constrained workspace involving static obstacles. We propose a Nonlinear Model Predictive Control (NMPC) approach for a team of UVMSs in order to transport an object while avoiding significant constraints and limitations such as: kinematic and representation singularities, obstacles within the workspace, joint limits and control input saturations. More precisely, by exploiting the coupled dynamics between the robots and the object, and using certain load sharing coefficients, we design a distributed NMPC for each UVMS in order to cooperatively transport the object within the workspace’s feasible region. Moreover, the control scheme adopts load sharing among the UVMSs according to their specific payload capabilities. Additionally, the feedback relies on each UVMS’s locally measurements and no explicit data is exchanged online among the robots, thus reducing the required communication bandwidth. Finally, real-time simulation results conducted in UwSim dynamic simulator running in ROS environment verify the efficiency of the theoretical finding.

Nonlinear Model Predictive Control (NMPC) algorithms are based on various nonlinear models. Recently, an on-line optimization approach for stochastic NMPC based on a Gaussian process model was proposed. A significant advantage of the Gaussian process models is that they provide information about prediction uncertainties, which would be of help in NMPC design. On the other hand, an explicit solution to the stochastic NMPC problem based on Gaussian process model would allow efficient on-line computations as well as verifiability of the implementation. This paper suggests an approximate multi-parametric Nonlinear Programming approach to explicit solution of stochastic NMPC problems for constrained nonlinear systems based on Gaussian process model. In particular, the reference tracking problem is considered. The approach builds an orthogonal search tree structure of the state space partition and consists in constructing a feasible PWL approximation to the optimal control sequence.

Nonlinear MPC for the improvement of dispersion of freeway traffic emissions