Nonlinear model predictive control of 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.

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.

Designing non-linear model predictive control (NMPC) laws for controlling multiple autonomous surface vessels in arbitrary formations in environments containing obstacles are reported in this paper. Two leader-follower decentralized geometrical control schemes that are required for defining a unique two-dimensional formation are considered. A three-degree-of-freedom dynamic model of surface vessels has been used for the controller design. It is assumed that the surface vessels are under-actuated and obstacles are present in the vessels' work environment. The real-time optimization abilities of the NMPC method has been used to improve the response of the unactuated DOF of the vessels and to directly incorporate the local obstacle avoidance into the formation control eliminating the need for an external local obstacle avoidance algorithm. This leads to more effective obstacle avoidance decisions based on the dynamics of the vehicle. The effectiveness the developed control law, even in the presence of model uncertainty and external disturbances are demonstrated via computer simulations.

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.

The article studies the trajectory tracking control problem of underactuated marine vehicles via the nonlinear model predictive control (NMPC) strategy, where practical control and state constraints present. It is a well-known challenging issue that the conventional NMPC is not applicable for underactuated marine vehicles, due to the fact that there does not exist a local static continuous state-feedback controller to stabilize the underactuated dynamics. To resolve this issue, this article proposes to construct an auxiliary time-varying tracking controller to aid terminal constraint design in the NMPC framework, where the time-varying tracking controller borrows the ideas from Lyapunov's direct method and backstepping approach. Based on this, a novel NMPC algorithm is designed to ensure trajectory tracking control of underactuated marine vehicles. Furthermore, a systematic parameter design approach is developed. Under the designed parameters, we show that the tracking error system is input-to-state stable (ISS). Finally, the effectiveness of the designed algorithm is verified by thorough simulation and hardware experiments.

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

Mobile medical robots have been widely used in various structured scenarios, such as hospital drug delivery, public area disinfection, and medical examinations. Considering the challenge of environment modeling and controller design, how to achieve the information from the human demonstration in a structured environment directly arouse our interests. Learning skills is a powerful way that can reduce the complexity of algorithm in searching space. This is especially true when naturally acquiring new skills, as mobile medical robot must learn from the interaction with a human being or the environment with limited programming effort. In this article, a learning scheme with nonlinear model predictive control (NMPC) is proposed for mobile robot path tracking. The learning-by-imitation system consists of two levels of hierarchy: in the first level, a multivirtual spring-dampers system is presented for imitation of the mobile robot's trajectories; and in the second level, the NMPC method is used in the motion control system. The NMPC strategy utilizes a varying-parameter one-layer projection neural network to solve an online quadratic programming optimization via iteration over a limited receding horizon. The proposed algorithm is evaluated on a mobile medical robot with an emulated trajectory in simulation and three scenarios used in the experiment.

In this paper a hierarchical model predictive control framework for the energy management of a hybrid electric vehicle is extended by replacing the supervisory linear model predictive controller with a nonlinear model predictive controller. The nonlinear system dynamics of a parallel hybrid electric vehicle are incorporated into a nonlinear optimal control problem which is solved at each time instance. The discretization of the continuous time system dynamics is realized via direct multiple shooting. The low-level linear model predictive controller considers the actuator dynamics for optimal tracking of the driver's torque demand while the high-level controller considers the slow dynamics of the vehicle and the battery. The proposed strategy is validated in simulation with the worldwide harmonized light vehicles test cycle and compared to a hierarchical model predictive control framework with linearized system dynamics.

This paper presents two nonlinear model predictive control (MPC) methods for the integrated propulsion and cabin-cooling management of electric vehicles. An air-conditioning (AC) model, which has previously been validated on a real system, is used to accomplish system-level optimization. To investigate the optimal solution for the integrated optimal control problem (OCP), we first build an MPC, referred to as a joint MPC, in which the goal is to minimize battery energy consumption while maintaining cabin-cooling comfort. Second, we divide the integrated OCP into two small-scale problems and devise a co-optimization MPC (co-MPC), where speed planning on hilly roads and cabin-cooling management with propulsion power information are addressed successively. Our proposed MPC methods are then validated through two case studies. The results show that both the joint MPC and co-MPC can produce significant energy benefits while maintaining driving and thermal comfort. Compared to regular constant-speed cruise control that is equipped with a proportion integral (PI)-based AC controller, the benefits to the battery energy earned by the joint MPC and co-MPC range from 2.09% to 2.72%. Furthermore, compared with the joint MPC, the co-MPC method can achieve comparable performance in energy consumption and temperature regulation but with reduced computation time.

In this paper, application of a modified nonlinear model predictive control (NMPC) method with simplified optimization problem has been studied. The NMPC scheme used here is based on Wiener model which requires only little more effort in development than a standard linear step-response model, yet offers superior characterization of systems with highly nonlinear gains. In this work, Wiener model has been incorporated into model predictive control scheme in a way that effectively removes the nonlinearity from the control problem. This scheme is based on identification of inverse nonlinear part of the Wiener model. The presented method results in an optimization problem with the implementation and computation levels of difficulty that are nearly equivalent to the case where the plant and constraints are linear. A standard quadratic programming (QP) algorithm is enough to solve the optimization problem. PH neutralization, as a highly nonlinear plant, is used in simulations to demonstrate the efficiency of the presented method.

This paper focuses on the application of a nonlinear model predictive control (MPC) method to coal-based integrated gasification combined cycle (IGCC) plants with water gas shift membrane reactors (WGS-MR) for pre-combustion capture of CO2. A systems-level nonlinear model of the integrated IGCC-MR process is introduced. The simulation results for a steady-state design used as a starting point for the control studies are presented. For such studies, a centralized nonlinear MPC strategy using a collocation-based algorithm is formulated to control power generation according to the demand. This strategy is successfully implemented to address scenarios that consider power load transitions (setpoint tracking) and variability in coal/slurry feed composition (disturbance rejection). The closed-loop simulation results show that power control is attained without violating process constraints related to target specifications and regulations imposed in stream temperatures and purities, including the carbon capture goal of 90% recommended by the U.S. Department of Energy (DOE).

Preconditioning for continuation model predictive control

To improve transportation efficiency, an adaptive speed control method is proposed for ship formation control when a ship formation enters a port with tidal elevation variations. The nonlinear model predictive control (NMPC) method and leader‒follower structure are utilised for the formation keeping and trajectory tracking tasks. The proposed method establishes a ship manoeuvring model and a dynamic speed constraint model for adaptive speed control. A safe distance model is constructed to maintain a safe distance between ship formation members. The proposed safe distance model utilises a Serret‒Frenet (S‒F) coordinate system to describe the positions of ship formation members. Simulation experiments are applied to the North Channel of the Yangtze River. The experimental results indicate that the maximum actual draught accounts for 101.4% of the maximum safe draught without speed constraints. The draft ratio decreases to 99.2% after the adaptive speed control method is applied. This method can be utilised to effectively control ship formation navigation considering variations in tidal elevation.

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.