Preconditioning for continuation model predictive 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.

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.

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.

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.

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).

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.

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.

Model predictive control (MPC) is a popular optimal control approach that can explicitly deal with system constraints. However, its high computational cost motivates researches on various approximation methods that make the MPC more practically viable for systems with fast dynamics and/or limited on-board computing power. Neighboring extremal (NE) is one such method that can be used to adapt to a nominal open-loop MPC sequence based on state perturbations so that it alleviates the online computational burden and extends the applicability of the MPC. In this paper, a cloud-based nonlinear MPC (NMPC) framework is considered where the NMPC is first performed on the cloud by taking advantage of its powerful computation and storage capabilities. To save the computational power, instead of solving the NMPC problem at every time step, the open-loop control sequence is downloaded for on-board implementation, then the NE is employed to systematically update the control sequence based on the deviation of the measured/estimated state from the nominal state obtained by the cloud-based NMPC. However, due to unknown disturbances and request-response communication delays between the plant and the cloud, the cloud-based NMPC may not show a reasonable performance for the plant even with the NE adaptations when the deviation is large. Therefore, we develop an event-triggered scheme based on a cost criteria to strike a balance between performance and cost. Finally, the efficacy of the proposed control synthesis is demonstrated with simulations on the application of the cart inverted pendulum.

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.

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.

The development of schemes for effectively examining unmeasured disturbances and plant-model mismatch in nonlinear model predictive control (NMPC) is an important area, which has attracted considerable attention over the past decade. In this work, we propose NMPC formulations for improving regulatory performance using nonlinear state space models identified from input-output data. The NARX-type MISO and MIMO nonlinear models identified in Srinivasarao et al.(17) are used to develop the state estimators and the predictors in the proposed NMPC formulations. We show that these models provide information about the covariances of the fast changing disturbances that are necessary for developing an extended Kalman filter (EKF) used in an NMPC scheme (EKF-NMPC). Rather than formulating an EKF to reject these disturbances, which we show has certain drawbacks, we proceed to propose an alternate prediction scheme based on the direct use of the identified noise model for future trajectory predictions. We show that the resulting NMPC formulation (DC-NMPC) is relatively easy to tune to achieve disturbance rejection as well as offset removal. The efficacy of the proposed control schemes is demonstrated using simulation studies on two benchmark control problems: (a) regulatory control of a continuously stirred tank reactor (CSTR) exhibiting input multiplicity at a singular point, and (b) servo control of a CSTR exhibiting output multiplicities. The analysis of simulation results reveals that the proposed EKF-NMPC and DC-NMPC schemes consistently generate significantly better regulatory performances, when compared to the performance of a conventional NDMC-type formulation, even in the presence of significant plant-model mismatch. In particular, the DC-NMPC performs significantly better than the EKF-NMPC formulation and can be tuned to achieve excellent regulatory performance without causing excessive input variability using parameters of a single disturbance filter. The conclusions reached through simulation studies are validated by conducting servo and regulatory control experiments on a laboratory-scale heater-mixer setup.

Thermal processes with prolonged and variable delays pose considerable difficulties due to unpredictable system dynamics and external disturbances, often resulting in diminished control effectiveness. This work presents a hybrid control strategy that synthesizes deep reinforcement learning (DRL) strategies with nonlinear model predictive control (NMPC) to improve the robust control performance of a thermal process with a long time delay. In this approach, NMPC cost functions are formulated as learning functions to achieve control objectives in terms of thermal tracking and disturbance rejection, while an actor–critic (AC) reinforcement learning agent dynamically adjusts control actions through an adaptive policy based on the exploration and exploitation of real-time data about the thermal process. Unlike conventional NMPC approaches, the proposed framework removes the need for predefined terminal cost tuning and strict constraint formulations during the control execution at runtime, which are typically required to ensure robust stability. To assess performance, a comparative study was conducted evaluating NMPC against AC-based controllers built upon policy gradient algorithms such as the deep deterministic policy gradient (DDPG) and the twin delayed deep deterministic policy gradient (TD3). The proposed method was experimentally validated using a temperature control laboratory (TCLab) testbed featuring long and varying delays. Results demonstrate that while the NMPC–AC hybrid approach maintains tracking control performance comparable to NMPC, the proposed technique acquires adaptability while tracking and further strengthens robustness in the presence of uncertainties and disturbances under dynamic system conditions. These findings highlight the benefits of integrating DRL with NMPC to enhance reliability in thermal process control and optimize resource efficiency in thermal applications.