Nonlinear Model Predictive Control of Shipboard Boom Cranes Based on Moving Horizon State Estimation

Cao, Yuchi; Li, Tieshan; Hao, Li‐Ying

doi:10.3390/jmse11010004

Cited by 7 publications

(4 citation statements)

References 33 publications

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“…Furthermore, to enhance the robustness of MPC DP controllers, tube-MPC schemes have been proposed [102]. Considering the effects of DP system linearization, nonlinear MPC has gradually gained attention and has been combined with other anti-disturbance techniques, making it more capable of dealing with complex environments [40,98,103,104]. For example, nonlinear MPC techniques combined with DO [101,105,106] and nonlinear MPC combined with the unscented Kalman filter [20] have been proposed for marine DP, the control framework of which is shown as Figure 2.…”

Section: Optimal Control Designmentioning

confidence: 99%

Dynamic Positioning Control for Marine Crafts: A Survey and Recent Advances

Gao,

2024

JMSE

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This paper surveys the recent advances in dynamic positioning (DP) control for marine crafts. DP of marine crafts means that a craft can maintain a fixed position and heading, or move along a predetermined trajectory slowly without the anchoring system, using only its own thruster system to counteract ocean disturbances. The survey is by no means exhaustive but provides a survey of some of the major technological advancements in DP controller design over the years of research and development. Firstly, the model of marine crafts and some difficult problems in DP control are introduced including the impact of multiple source disturbance, unavailable velocity measurement information, resource conservation and performance optimization, destabilizing impact of faults and network security and compound multi-constraint restrictions. Then, the DP control schemes in recent years are summarized and classified in detail. Finally, some theoretical and technical problems are proposed, including online data-driven model-free control, man–machine combination intelligent control and composite hierarchical anti-disturbance control to guide future investigations.

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Section: Optimal Control Designmentioning

confidence: 99%

Dynamic Positioning Control for Marine Crafts: A Survey and Recent Advances

Gao,

2024

JMSE

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“…V e, t) = 1 2 e T e + ϕ T ϕ (12) It is not difficult to see that V (e, t) is positive definite when ∥e∥ −→ ∞ , V (e, t) −→ ∞ . Next, we derive in which conditions .…”

Section: B Parameter Identification Of Pmsmmentioning

confidence: 99%

“…The study of model predictive control (MPC) theory has led to the widespread attention given to the Moving Horizon Estimation (MHE) method, which is also based on the moving optimization principle [12,13]. MHE uses the finite horizon N for moving horizontal movement measurements instead of individual instantaneous measurements.…”

Section: Introductionmentioning

confidence: 99%

PMSM Sensorless Control Based on Moving Horizon Estimation and Parameter Self-Adaptation

Chen,

Wan

2024

Electronics

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The field of sensorless control of permanent magnet synchronous motor (PMSM) systems has been the subject of extensive research. The accuracy of sensorless controllers depends on the precise estimation of PMSM state quantities, including rotational speed and rotor position. In order to enhance state estimation accuracy, this paper proposes a moving horizon estimator that can be utilized in the sensorless control system of PMSM. Considering the parameter variations observed in PMSM, a nonlinear mathematical model of PMSM is established. A model reference adaptive system (MRAS) is employed to identify parameters such as resistance, inductance, and magnetic chain in real time. This approach can mitigate the impact of parameter fluctuations. Moving horizon estimation (MHE) is an estimation method based on optimization that can directly handle nonlinear system models. In order to eliminate the influence of external interference and improve the robustness of state estimation, a method based on MHE has been designed for PMSM, and a sensorless observer has been established. Considering the traditional MHE with large computation and high memory occupation, the calculation of MHE is optimized by utilizing a Hessian matrix and gradient vector. The speed and position of the PMSM are estimated within constraints during a single-step iteration. The results of the simulation demonstrate that in comparison to the traditional control structure, the estimation error of rotational speed and rotor position can be reduced by utilizing the proposed method. A more accurate estimation can be achieved with good adaptability and computational speed, which can enhance the robustness of the control system of PMSM.

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“…[11] MHE has often been used in systems with moderate to sluggish system dynamics and more computer resources because to its high computational cost. Nonetheless, there are various techniques in the literature to quicken this method [12].…”

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confidence: 99%

SCSO-MHEF: Sand Cat Swarm Optimization based MHEF for Nonlinear LTI-IoT Sensor Data Enhancement

Bashir,

Queen,

Habib

2024

IJEER

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Sensor data is an integral component of internet of things (IoT) and edge computing environments and initiatives. In IoT, almost any entity imaginable can be outfitted with a unique identifier and the capacity to transfer data over a network. The estimate problem was formulated as a min-max problem subject to system dynamics and limitations on states and disturbances within the moving horizon strategy framework. In this paper, a novel Sand Cat Swarm Optimization Based MHEF for Nonlinear LTI IOT Sensor Data Enhancement (SCSO-MHEF) is proposed. In the proposed method the MHEF is optimized using Sand Cat Swarm Optimization to enhance sensor data stability tuned by initial parameters. Simulation experiments were conducted on various and unique scenarios in various orders LTI system with IOT sensor data in order to validate the suggested approach. This method can be used to analyze systems with dynamically changing systems. The proposed SCSO-MHEF technique overall accuracy of 84.5%, 87.3 %, and 99.5 % better than Kalman Filter (KF), EKF and Moving Horizon Filter (MHEF) respectively.

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Nonlinear Model Predictive Control of Shipboard Boom Cranes Based on Moving Horizon State Estimation

Cited by 7 publications

References 33 publications

Dynamic Positioning Control for Marine Crafts: A Survey and Recent Advances

Dynamic Positioning Control for Marine Crafts: A Survey and Recent Advances

PMSM Sensorless Control Based on Moving Horizon Estimation and Parameter Self-Adaptation

SCSO-MHEF: Sand Cat Swarm Optimization based MHEF for Nonlinear LTI-IoT Sensor Data Enhancement

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