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A nonlinear model predictive controller design for residual current compensation inverters in rapid earth fault current limiters to mitigate powerline bushfires

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2022年第PartB期142卷

作者： Fernando, W. S. P. Mahmud, M. A. Islam, S. N. Hosseinzadeh, N. Deakin Univ Sch Engn Waurn Ponds Vic 3216 Australia Northumbria Univ Newcastle Fac Engn & Environm Newcastle Upon Tyne NE1 8ST England

This paper presents a nonlinear model predictive control scheme for residual current compensation inverters in rapid earth fault current limiters to inject appropriate current to the neutral so that the fault current is compensated in distribution networks. The proposed nonlinear model predictive controller is designed to limit the fault current within a level that powerline bushfires do not ignite due to single line-to-ground faults on power distribution systems in bushfire prone areas. The nonlinear model predictive controller is designed for a T-type residual current compensation inverter in rapid earth fault current limiters based on its dynamical model where the control objective is to inject current for compensating the fault current. The control law for the residual current compensation inverter is obtained by solving an optimal control problem while using the concept of the receding horizon control scheme. The performance of the controller is evaluated through standard software-based computer and processor-in-loop simulations where results are benchmarked against backstepping and sliding mode controllers in terms of compensating both faulty phase voltage and fault current.

关键词： Residual current compensation inverters Fault current Rapid earth fault current limiters Powerline bushfires nonlinear model predictive controller Faulty phase voltage

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Wind Compensation in Trajectory Tracking of a Fixed Wing UAV Using a nonlinear model predictive controller based on the Particle Swarm Optimization 7

Wind Compensation in Trajectory Tracking of a Fixed Wing UAV...

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7th International Conference on Robotics and Mechatronics (ICRoM)

作者： Nobahari, Hadi Sharifi, Alireza Sharif Univ Technol Dept Aerosp Engn Tehran Iran

ISBN: (数字)9781728166049

ISBN: (纸本)9781728166049

In this study, a nonlinear model predictive controller based on the particle swarm optimization is used for trajectory tracking of a fixed wing UAV and its performance is investigated in the presence of wind compensation and without it. For this purpose, the components of the wind velocity and the UAV states are estimated using a nonlinear filter and then compensated in the proposed controller architecture. Finally, stability of the proposed controller is proved and analyzed. The results show that performance of the UAV flight is effectively improved when the components of the wind velocity are compensated within the controller.

关键词： component Heuristic controller nonlinear model predictive controller Particle Swarm Optimization Unmanned Aerial Vehicle Wind Estimation

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Design and Testing of a nonlinear model predictive controller for Ride Height Control of Automotive Semi-active Air Suspension Systems

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IEEE ACCESS 2018年 6卷 63777-63793页

作者： Ma, Xinbo Wong, Pak Kin Zhao, Jing Zhong, Jian-Hua Ying, Huang Xu, Xing Univ Macau Dept Electromech Engn Taipa 999078 Macau Peoples R China Fuzhou Univ Sch Mech & Automat Engn Fuzhou 350108 Fujian Peoples R China Jiangsu Univ Sch Automot & Traff Engn Zhenjiang 212013 Peoples R China

With the rapid growth of the automotive technology, electronically controlled air suspension has been widely used to improve ride comfort and handling stability of the vehicle by actively modulating the suspension stiffness, vehicle height, and posture. Ride height control (RHC) is the main function of the semi-active air suspension, and it is achieved by conducting air charging and discharging of the air spring, which plays a critical role in improving the vehicle dynamic performance. In addition, the unevenness distribution with payloads at the four wheels, the different dynamic characteristics of the front and rear air suspensions, and the undesired roll and pitch angles make adjustment effect of the vehicle ride height hard to be well addressed. Therefore, an effective control strategy is very essential, which is not only for RHC but also for keeping the vehicle posture, as well as the vehicle dynamic performance. Considering that the full car dynamics is a highly nonlinear model, this paper proposes a novel nonlinear model predictive controller to handle the multi-objective control requirement of a full car system. In order to evaluate the performance of the proposed controller, comparisons among the proposed control algorithm, the existing proportional- integral-derivative method, and the sliding-mode controller are carried out by a numerical analysis. The numerical results show that the proposed method excels the other methods and is effective in the adjustment of the vehicle ride height.

关键词： Air suspension nonlinear model predictive controller multi-objective control ride height control

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Adaptive nonlinear model predictive controller based on Polynomial form NARMAX model for Autonomous Underwater Vehicle 15

Adaptive Nonlinear Model Predictive Controller based on Poly...

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15th IEEE-India-Council International Conference (IEEE INDICON)

作者： Rath, Biranchi Narayan Subudhi, Bidyadhar Natl Inst Technol Rourkela Dept Elect Engn Rourkela India

ISBN: (纸本)9781538682357

In this paper, a constrained Adaptive nonlinear model predictive Control (ANMPC) is proposed for an underactuated Autonomous Underwater vehicle (AUV) without depending on the steady state knowledge of AUV. The dynamics of AUV being nonlinear, a polynomial form of nonlinear Autoregressive Moving Average Exogenous (NARMAX) model is used to identify its dynamics. To deal with actuator constraint and uncertainty associated with dynamics and disturbances, an ANMPC is proposed in this paper. The ANMPC uses the identified NARMAX model for on-line prediction and steering the AUV to desired depth taking into account the constraints imposed on the stern planes. From the simulations results, it is observed that that the proposed ANMPC control strategy exhibits efficient tracking performance.

关键词： Autonomous Underwater vehicle(AUV) nonlinear model predictive controller NARMAX

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Adaptive nonlinear model predictive controller based on Polynomial form NARMAX model for Autonomous Underwater Vehicle

Adaptive Nonlinear Model Predictive Controller based on Poly...

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IEEE India Conference (INDICON)

作者： Biranchi Narayan Rath Bidyadhar Subudhi Department of Electrical Engineering National Institute of Technology Rourkela India

ISBN: (纸本)9781538682364

In this paper, a constrained Adaptive nonlinear model predictive Control (ANMPC) is proposed for an underactuated Autonomous Underwater vehicle (AUV) without depending on the steady state knowledge of AUV. The dynamics of AUV being nonlinear, a polynomial form of nonlinear Autoregressive Moving Average Exogenous (NARMAX) model is used to identify its dynamics. To deal with actuator constraint and uncertainty associated with dynamics and disturbances, an ANMPC is proposed in this paper. The ANMPC uses the identified NARMAX model for on-line prediction and steering the AUV to desired depth taking into account the constraints imposed on the stem planes. From the simulations results, it is observed that that the proposed ANMPC control strategy exhibits efficient tracking performance.

关键词： Autonomous underwater vehicle (AUV) nonlinear model predictive controller NARMAX Autonomous underwater vehicles nonlinear model predictive control underwater vehicles predictive controllers Polynomial nonlinear models dynamics

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model predictive Path-Following Framework for Generalized N-Trailer Vehicles in the Presence of Dynamic Obstacles modeled as Soft Constraints

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 7018-7032页

作者： Deniz, Nestor Nahuel Cheein, Fernando Auat Univ Tecn Federico Santa Maria Dept Elect Engn Valparaiso 2340000 Chile Heriot Watt Univ Edinburgh Ctr Robot Sch Engn & Phys Sci UK Natl Robotarium Edinburgh EH14 4AS Scotland Harper Adams Univ Newport England

Collision avoidance is crucial for autonomous navigation systems. Many studies have addressed obstacle avoidance for single unicycles and car-like vehicles in on-road conditions. In this work, we extend the scope to generalised N-trailer vehicles, comprising a single active segment pulling multiple trailers. Unlike approaches that treat obstacles as hard constraints, we model them as soft constraints using Gaussian functions. This method maintains the convexity of the search space, reducing computational demands. However, the regions occupied by obstacles remain feasible. Thus, the Gaussian function's amplitudes need to be carefully chosen to discourage navigation through these areas. Moreover, closed-loop stability is guaranteed by generating auxiliary references when the nominal path is occluded. The efficacy of this approach is demonstrated through simulated and field experiments with a tractor pulling two trailers. These experiments show the method's capability to navigate around obstacles efficiently while maintaining computational efficiency, validating its practical applicability. Videos of the experiments and the implemented algorithms are available at https://***/:f:/g/personal/nestor_deniz_usm_cl/EtU54g 1NeslNhD8V7dAeu20B0umnQa4FKiMlzThkTAXYvg?e=swEX wg. Despite the success in real-time implementation, more research is needed to address the open questions discussed at the end of this article. Note to Practitioners -This work focuses on implementing obstacle avoidance for a kind of vehicles widely used in agriculture, mining, luggage transportation, and industry. A LiDAR Velodyne VLP16, configured with its lowest rotation speed for denser point clouds, is used to scan the environment. Proper attachment of the LiDAR to the tractor's body minimises vibration and azimuth movements, ensuring accurate obstacle detection. Obstacles are modelled as Gaussian functions to maintain the convexity and optimise computational efficiency. The Gaussian

关键词： N-trailers vehicles obstacle avoidance nonlinear model predictive controller efficient motion controller N-trailers vehicles obstacle avoidance nonlinear model predictive controller efficient motion controller

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Data-driven dynamic modeling for precise trajectory tracking of a bio-inspired robotic fish

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APPLIED OCEAN RESEARCH 2025年 156卷

作者： Wang, Zhiping Li, Zonggang Xia, Guangqing Kang, Huifeng Li, Bin Li, Qingquan Zheng, Lixin Lanzhou Jiaotong Univ Sch Mech Engn Lanzhou 730070 Gansu Peoples R China Lanzhou Jiaotong Univ Robot Inst Lanzhou 730070 Gansu Peoples R China Dalian Univ Technol State Key Lab Struct Anal Optimizat & CAE Software Dalian 116024 Liaoning Peoples R China North China Inst Aerosp Engn Hebei Key Lab Transmedia Aerial Underwater Vehicle Langfang 065000 Hebei Peoples R China

We propose utilizing an attention mechanism and deep neural networks to develop a hydrodynamic identification model, integrated with a time-triggered nonlinear model predictive controller (ENMPC) for precise trajectory tracking of a robotic fish. A central pattern generator (CPG) network was employed to design a synergistic gait controller for the robotic fish that could coordinate its pectoral fins and flexible body/caudal fins to enable multimodal motion. We derived a nonlinear map between the driving parameters and the thrust/torque of the robotic fish using a computational fluid dynamics (CFD) simulation dataset. The attention mechanism was applied to incorporate laminar flow effects and construct a hydrodynamic identification model based on a bidirectional long short-term memory (Bi-LSTM) network. This identification model serves as the foundation for learning a control transformation model that operates as its inverse. Finally, event-triggered nonlinear model predictive constraints were adjusted to account for external disturbances and thereby ensure the convergence of robotic fish tracking errors while minimizing computational costs.

关键词： Bionic robotic fish Central pattern generator Bi-LSTM network nonlinear model predictive controller Trajectory tracking control

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Experimental investigations of an energy-efficient dynamic positioning controller for different sea conditions

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OCEAN ENGINEERING 2024年 299卷

作者： Alagili, Osama Fernando, Eranga Ahmed, Salim Imtiaz, Syed Murrant, Kevin Gash, Bob Islam, Mohammed Zaman, Hasanat Mem Univ Newfoundland Fac Engn & Appl Sci 240 Queen Elizabeth Ave St John NF A1B3X5 Canada CNR 1 Arctic Ave St John NF A1B3T5 Canada

The primary objective of a Dynamic Positioning (DP) controller is to maintain vessel position under varying environmental disturbances, while minimizing thruster usage. This work presents the development of an innovative energy -efficient DP controller, named Green NMPC (GNMPC), which minimizes thruster demand while upholding position constraints. Inspired from the structure of the economic nonlinear model predictive controller (ENMPC), GNMPC aligns with "green" objectives and performance metrics, notably thruster energy efficiency. Extensive DP tests were conducted across a spectrum of wave conditions, including head seas, oblique angles, and large position set -point changes, to validate the efficacy of the GNMPC approach and evaluate the dynamic positioning system's effectiveness in diverse challenging situations. The results demonstrated that the proposed controller is energy efficient compared to a benchmark NMPC and proportional-integral- derivative (PID) controller. It successfully reduced thruster demand in the sway direction compared to NMPC while preserving the vessel's positioning objectives.

关键词： Dynamic positioning Control Algorithms nonlinear model predictive controller

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Efficiency-aware nonlinear model-predictive control with real-time iteration scheme for wave energy converters

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INTERNATIONAL JOURNAL OF CONTROL 2023年第8期96卷 1909-1921页

作者： Guerrero-Fernandez, Juan L. Gonzalez-Villarreal, Oscar J. Rossiter, John Anthony Univ Sheffield Dept Automat Control & Syst Engn Sheffield S Yorkshire England Cranfield Univ Ctr Autonomous & Cyber Phys Syst Cranfield Beds England

Several solutions have been proposed in the literature to maximise the harvested ocean energy, but only a few consider the overall efficiency of the power take-off system. The fundamental problem of incorporating the power take-off system efficiency is that it leads to a nonlinear and non-convex optimal control problem. The main disadvantage of the available solutions is that none solve the optimal control problem in real-time. This paper presents a nonlinear model predictive control (NMPC) approach based on the real-time iteration (RTI) scheme to incorporate the power take-off system's efficiency when solving the optimal control problem at each time step in a control law aimed at maximising the energy extracted. The second contribution of this paper is the derivation of a condensing algorithm O(N-2) for 'output-only' cost functions required to improve computational efficiency. Finally, the RTI-NMPC approach is tested using a scaled model of the Wavestar design, demonstrating the benefit of this new control formulation.

关键词： Wave energy converters nonlinear model predictive controller real-time iteration power take-off-system efficiency condensing algorithm O(N-2)

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Resilient Multi-range Radar Detection System for Autonomous Vehicles: A New Statistical Method

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2023年第1期19卷 695-708页

作者： Enayati, Javad Asef, Pedram Wilson, Peter Univ Hertfordshire Sch Phys Engn & Comp Sci Hatfield AL10 9AB England Univ Bath Fac Engn & Design Dept Elect & Elect Engn Bath BA2 7AY England

Critical issues with current detection systems are their susceptibility to adverse weather conditions and constraint on the vertical field view of the radars limiting the ability of such systems to accurately detect the height of the targets. In this paper, a novel multi-range radar (MRR) arrangement (i.e. triple: long-range, medium-range, and short-range radars) based on the sensor fusion technique is investigated that can detect objects of different sizes in a level 2 advanced driver-assistance system. To improve the accuracy of the detection system, the resilience of the MRR approach is investigated using the Monte Carlo (MC) method for the first time. By adopting MC framework, this study shows that only a handful of fine-scaled computations are required to accurately predict statistics of the radar detection failure, compared to many expensive trials. The results presented huge computational gains for such a complex problem. The MRR approach improved the detection reliability with an increased mean detection distance (4.9% over medium range and 13% over long range radar) and reduced standard deviation over existing methods (30% over medium range and 15% over long-range radar). This will help establishing a new path toward faster and cheaper development of modern vehicle detection systems.

关键词： Autonomous vehicles Triple radar Level 2 ADAS system Lane keeping assistance nonlinear model predictive controller Safety Statistical method Autonomous emergency braking

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