This paper presents a predictivecontrol strategy based on nonlinear model predictive control (NMPC) for a supercavitating vehicle system with high coupling nonlinearity and model uncertainty. By considering the cavit...
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This paper presents a predictivecontrol strategy based on nonlinear model predictive control (NMPC) for a supercavitating vehicle system with high coupling nonlinearity and model uncertainty. By considering the cavity memory effect and introducing a cavity axis correction model, a nonlinear time -varying dynamic model of the vehicle is established to derive the time -varying tail efficiency and nonlinear planing force models. Based on the model, a predictivecontroller based on the nonlinear characteristics of the vehicle is designed. The controller can solve a finite horizon optical control problem (FHOCP) online for the updated system state in each system iteration to find the optimal solution at a given time. In addition, the controller introduces a Lyapunov terminal cost function and terminal constraint for the system state to reduce the system's conservativeness, which can improve the control performance of the system. The simulation results verify that the controller exhibits good stability and robustness against external interference and parameter perturbation.
This paper presents a new flight control framework for tiltrotor multirotor uncrewed aerial vehicles (MRUAVs). Tiltrotor designs offer full actuation but introduce complexity in control allocation due to actuator redu...
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This paper presents a new flight control framework for tiltrotor multirotor uncrewed aerial vehicles (MRUAVs). Tiltrotor designs offer full actuation but introduce complexity in control allocation due to actuator redundancy. We propose a new approach where the allocator is tightly coupled with the controller, ensuring that the control signals generated by the controller are feasible within the vehicle actuation space. We leverage nonlinear model predictive control (NMPC) to implement the above framework, providing feasible control signals and optimizing performance. This unified control structure simultaneously manages both position and attitude, which eliminates the need for cascaded position and attitude control loops. Extensive numerical experiments demonstrate that our approach significantly outperforms conventional techniques that are based on Linear Quadratic Regulator (LQR) and Sliding Mode control (SMC), especially in high-acceleration trajectories and disturbance rejection scenarios, making the proposed approach a viable option for enhanced control precision and robustness, particularly in challenging missions.
This paper proposes a methodology for safely planning the motion of a robot manipulator sharing its workspace with a human operator. The motion of the robot is continuously re-planned via nonlinearmodelpredictive co...
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This paper proposes a methodology for safely planning the motion of a robot manipulator sharing its workspace with a human operator. The motion of the robot is continuously re-planned via nonlinear model predictive control (NMPC), imposing the so-called speed and separation monitoring (SSM) condition to guarantee human safety. Contrary to previous works in the field, the NMPC algorithm is designed with an ellipsoidal terminal constraint, to enlarge the domain of attraction compared to the casein which a point terminal constraint was imposed. This is a very important aspect in real-world applications, allowing the robot to plan its motion from initial configurations that are relatively far from the goal point. Theoretical results are proved on recursive feasibility and closed-loop stability for both cases of NMPC with point and set terminal constraints, under the simplifying assumption of a static human. The effectiveness of the proposed approach is verified via numerical evaluation of the domain of attraction and with experiments on a UR5 manipulator.
To mitigate severe fluctuations in engine power turbine speed caused by changes in coaxial rotors, propellers, and aero-surfaces during the mode transition in coaxial high-speed helicopter (CHH), this paper presents a...
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To mitigate severe fluctuations in engine power turbine speed caused by changes in coaxial rotors, propellers, and aero-surfaces during the mode transition in coaxial high-speed helicopter (CHH), this paper presents a nonlinear model predictive control (NMPC) method for the CHH power system based on an integrated onboard model. Firstly, a digital simulation framework is deployed, incorporating a CHH onboard model based on a T-S fuzzy model and an onboard composite model of variable speed turboshaft engine based on a stacked Long ShortTerm Memory-State Variable model (LSTM-SVM). Subsequently, a nonlinear model predictive control method is devised for the CHH power system. By integrating flight prediction data from the integrated onboard model, an optimized objective function is formulated, taking into account both speed control objectives and the dynamic response characteristics of the turboshaft engine's output shaft. Through rolling optimization and feedback correction methods, real-time optimized control parameters for the turboshaft engine are obtained, ensuring rapid responsiveness in the engine control system. Simulation results demonstrate that the developed integrated onboard model accurately represents the variations in performance parameters during high-speed helicopter flight. Additionally, the nonlinear model predictive control law effectively tracks the variable speed reference commands of the power turbine, maintaining a maximum power turbine speed fluctuation of under 0.46%, thereby significantly enhancing both the engine's response and control quality while preserving computational real-time performance.
Active and semi-active suspensions for passenger cars traditionally enhance comfort through body control, and vehicle handling by reducing the tyre load variations induced by road irregularities. Active suspensions ca...
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Active and semi-active suspensions for passenger cars traditionally enhance comfort through body control, and vehicle handling by reducing the tyre load variations induced by road irregularities. Active suspensions can also be designed to track a desired yaw rate profile through the control of the lateral load transfer distribution between the front and rear axles. This paper considers an integrated system including semi-active and active suspension actuation to control the yaw, roll, pitch and heave dynamics excited by the driving actions. To this purpose, two novel real-time-capable implicit nonlinear model predictive control (NMPC) formulations, excluding and including cost function weight adaptation, are proposed and compared with the passive vehicle, and the controlled vehicle with two combinations of skyhook and active roll control, the first based on a pseudoinverse decoupling transformation for obtaining the damping force contributions, and the second using an inverse formulation. The algorithms are assessed through an experimentally validated simulation model, along manoeuvres corresponding to sub-limit and limit handling operation, to analyse the trade-off between body motion reduction and cornering response enhancement. The results show that the adaptable NMPC configuration provides the best performance in all scenarios, also for significant variations of the main vehicle and tyre parameters.
This paper studies a multi-hydraulic system (MHS) synchronization control algorithm. Firstly, a general nonlinear asymmetric MHS state space entirety model is established and subsequently the model form is simplified ...
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This paper studies a multi-hydraulic system (MHS) synchronization control algorithm. Firstly, a general nonlinear asymmetric MHS state space entirety model is established and subsequently the model form is simplified by nonlinear feedback linearization. Secondly, an entirety model-type solution is proposed, integrating a nonlinear model predictive control (NMPC) algorithm with a cross-coupling control (CCC) algorithm. Furthermore, a novel disturbance compensator based on the system's inverse model is introduced to effectively handle disturbances, encompassing unmodeled errors and noise. The proposed innovative controller, known as nonlinear model predictive control-cross-coupling control with deep neural network feedforward (NMPC-CCCDNNF), is designed to minimize synchronization errors and counteract the impact of disturbances. The stability of the control system is rigorously demonstrated. Finally, simulation results underscore the efficacy of the NMPCCCC-DNNF controller, showcasing a remarkable 60.8% reduction in synchronization root mean square error (RMSE) compared to other controllers, reaching up to 91.1% in various simulations. These results affirm the superior control performance achieved by the NMPC-CCC-DNNF controller.
The self-balancing wheelchair is characterized by its small size, flexibility, and strong ground adaptability. To expand the control range of the pitch angle and enhance the stability of the self-balancing wheelchair,...
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The self-balancing wheelchair is characterized by its small size, flexibility, and strong ground adaptability. To expand the control range of the pitch angle and enhance the stability of the self-balancing wheelchair, this paper presents a nonlinear model predictive control (NMPC) method for challenges in online motion planning and control. We apply the NMPC formulation with the nonlinear state equation as a constraint that captures the accurate kinematics and dynamics of the wheelchair. We utilize direct transcription to discretize the formulation and convert it into a nonlinear programming problem. We establish a control loop that includes a PD controller to apply NMPC to a wheelchair. We conduct simulations under different state commands and external forces and demonstrate that the proposed method can achieve fast convergence speeds, strong robustness, and high stability motions under extreme pitch angles.
Direct internal reforming solid oxide fuel cells (DIR-SOFCs) are economically viable devices for power generation, while their reliability requires further improvement. Advanced process control can effectively facilit...
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Direct internal reforming solid oxide fuel cells (DIR-SOFCs) are economically viable devices for power generation, while their reliability requires further improvement. Advanced process control can effectively facilitate the long-term operation of SOFCs with high efficiency and safety. DIR-SOFCs exhibit complex coupling effect and a high degree of nonlinearity, making it worthwhile to embed high -quality dynamic models including spatial distributions into nonlinear model predictive control (NMPC). A two -layer control architecture aiming for rapid load tracking and thermal management with high economic efficiency is designed for planar DIRSOFCs, containing a set point optimizer and an NMPC controller based on the distributed parameter model expressed by partial differential-algebraic equations (PDAE). PDAE-constrained dynamic optimization problems are solved in the NMPC controller to seek optimal control strategies, considering safety constraints about the maximum temperature gradient and physical constraints of actuators. The high-fidelity one-dimensional PDAE model is validated through steady and dynamic simulations, and the optimal steady-state distributions are analyzed under three power demands. Behaviors of the closed -loop system under power demand step-up and step-down scenarios are investigated to demonstrate the effectiveness of the designed control scheme. The proposed NMPC controller can efficiently realize rapid load tracking, maintain good thermal management, and reduce the transition time, with minor steady-state errors and acceptable real-time performance.
Dividing wall columns (DWCs) are practical, effective, and promising among distillation process intensification technologies. nonlinear model predictive control (NMPC) schemes are developed in this study to control th...
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Dividing wall columns (DWCs) are practical, effective, and promising among distillation process intensification technologies. nonlinear model predictive control (NMPC) schemes are developed in this study to control the three-product DWCs. As these systems are intensely interactive and highly nonlinear, NMPC may be more suitable than the traditional PI control. The model is established based on Python and Pyomo platforms. As the original mathematical model of the column section is ill-posed, index reduction is used to avoid a high-index differential-algebraic equation (DAE) system. The well-posed index-1 system after index reduction is employed for the steady-state simulation and dynamic control in this study. Case studies with three DWC configurations to separate the mixture of ethanol (A), n-propanol (B), and n-butanol (C) show that the NMPC performs very well with small maximum deviations and short settling times. This demonstrates that the NMPC is a feasible and very effective scheme to control three-product DWCs.
modelpredictivecontrol (MPC) without guaranteed stability is typically employed for trajectory tracking of autonomous trucks (ATs). However, in certain scenarios, the tracking error may fail to converge. To address ...
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modelpredictivecontrol (MPC) without guaranteed stability is typically employed for trajectory tracking of autonomous trucks (ATs). However, in certain scenarios, the tracking error may fail to converge. To address this, the optimization control problem can be designed by incorporating terminal ingredients, i.e., the terminal control gain, terminal constraint, and terminal cost function. In this paper, we propose a nonlinear model predictive control (NMPC) scheme with terminal ingredients for trajectory tracking of ATs in the presence of the coupled longitudinal and lateral dynamics. The trajectory tracking problem exhibits asymptotic convergence, and the optimization control problem (OCP) ensures recursive feasibility. The complexity of the proposed controller is similar to that of a standard NMPC without terminal ingredients. Additionally, we introduce an efficient Newton-type method with a look-up table (NTLT) to solve the OCP. Co-simulations in Matlab/Simulink and TruckSim validate the effectiveness of the proposed NMPC scheme and the NTLT across various scenarios.
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