A Novel Trajectory Planning Method for Automated Vehicles Under Parameter Decision Framework

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 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.

The rapid acceleration of urbanization and the surge in car ownership necessitate efficient automatic parking solutions in constricted spaces to address the escalating urban parking issue. To optimize space utilization, enhance traffic efficiency, and mitigate accident risks, a method is proposed for smooth, comfortable, and adaptable automatic parking trajectory planning. This study initially employs a hybrid A* algorithm to generate a preliminary path, then fits the velocity and acceleration based on a cubic polynomial. The kinematic constraints of the vehicle and obstacle avoidance constraints are then meticulously defined, and a coupled nonlinear model predictive control (NMPC) method is employed to optimize the trajectory. Compared to the hybrid A* algorithm, the optimized trajectory demonstrates superior space utilization and improved smoothness. The experimental results indicate that the proposed method performs effectively in automated parking tasks in confined spaces, suggesting promising applications and broad prospects for future.

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.

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).

In order to obtain the high‐quality fast response control for helicopter/turboshaft engine with variable rotor speed, a nonlinear model predictive control (NMPC) method based on piecewise objective function is proposed. The piecewise objective function takes into account both the control index of power turbine speed and the rotor dynamics of engine output shaft, which optimizes the transient overshoot and sag of the power turbine speed caused by torques mismatch during variable rotor speed from the root. The results show that compared with conventional MPC method with single objective function only involving the control index of power turbine speed, the novel NMPC method proposed can reduce the overshoot or sag of the power turbine speed by more than 30% during variable rotor speed, which possesses superior control effect of turboshaft engine without losing real‐time performance.

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

This paper presents a nonlinear model predictive control (NMPC) method with adaptive neuro-modelling for redundant robotic manipulators. Using the NMPC, the end-effector of the robot tracks a predefined geometry path in the Cartesian space without colliding with obstacles in the workspace and at the same time avoiding singular configurations of the robot. Furthermore, using the neural network for the model prediction, no knowledge about system parameters is necessary; hence, yielding robustness against changes in parameters of the system. Numerical results for a 4DOF redundant spatial manipulator actuated by DC servomotors shows effectiveness of the proposed method.

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 brief presents a nonlinear model predictive control (NMPC) method for the trajectory tracking problem of an autonomous underwater vehicle (AUV). By augmenting the desired output trajectory to a reference dynamical system, the tracking task can fit into the standard NMPC framework, which effectively avoids possible numerical difficulties in the following fast NMPC implementation. To relieve the conflict between short sampling period and high demand of online calculation, Ohtsuka's continuation/generalized minimal residual (C/GMRES) algorithm is investigated. In order to handle the realistic constraints on the AUV thrusters, we incorporate the log barrier functions into the cost function and modify the C/GMRES algorithm. Several different reference trajectories are tested using the identified dynamic model of the Saab SeaEye Falcon open-frame ROV/AUV, which demonstrate the effectiveness and efficiency of the proposed fast algorithm for the AUV tracking control.

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.

Distributed implementation of nonlinear model predictive control for AUV trajectory tracking

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 focuses on a lateral disturbance rejection control issue for the robust path following of the autonomous ground vehicle (AGV) with the constrained active front steering (AFS) and torque vectoring control (TVC) under extreme conditions. First, to guarantee the robustness of closed-loop system, a discrete switched disturbance rejection (DSDR) controller with hierarchical structure is presented to reject the lateral disturbances which are generated from a nominal steering. Second, in the supervisor layer of the DSDR controller, to enhance lateral stability of the AGV, a lateral stabilization strategy is developed to tackle the interference effects between the AFS and the TVC based on the region boundary conditions. Third, in the TVC layer of the DSDR controller, to ease the computational burden and ensure the accuracy of the yaw moment control, a nonlinear reduced-order model predictive control method is proposed to deal with the nonlinearity of the tire dynamics. Both the software-in-the-loop and hardware-in-the-loop tests are performed to verify the effectiveness and practicability of the proposed method.

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.