Model Predictive Control of a Shipborne Hydraulic Parallel Stabilized Platform Based on Ship Motion Prediction

To improve the accuracy of tracking unmanned vehicles on known trajectories, two optimised model predictive control (MPC) trajectory tracking control systems are designed based on the adaptive compensation and robust control of a radial basis function (RBF) neural network. Based on the traditional MPC trajectory tracking controller and the local approximation characteristics of the RBF neural network, the proposed RBF compensation–MPC control system is designed to compensate for the inaccuracy in the MPC prediction model arising from modelling errors. The results show that this method can achieve a root mean square error of less than 0.3703 m for the lateral position. Subsequently, to suppress the error generated by the RBF neural network and reduce the degree of vehicle sideslip, the error is considered to be external interference, and the anti-interference characteristic of the RBF robust control is incorporated into the RBF robust-MPC control system. Following the re-optimisation of the RBF robust control, the root mean square error of the lateral position is set within 0.2352 m. The results of a MATLAB/Carsim joint simulation show that using the RBF robust control can improve the tracking accuracy of the traditional MPC controller compared with RBF compensation control, while simultaneously improving the driving stability of the vehicle.

A time-varying ensemble model for ship motion prediction based on feature selection and clustering methods

Making Non-Centralized a Model Predictive Control Scheme by Using Distributed Smith Dynamics

In this paper, the overall structure of the microgrid and the power coordination strategy control method are firstly determined. The photovoltaic cell is used as the representative of the distributed power in the microgrid; the energy storage battery is connected to the microgrid through a buck-boost circuit of model predictive control; the diesel generator is connected to the microgrid through a model predictively controlled active front end rectifier is connected to the DC bus. Then, in view of the difficulty in setting the weighting factors of multiple objective functions in traditional model predictive control, a model predictive control with a sequence structure is introduced in this paper, which can greatly reduce the switching frequency on the premise of ensuring the stable operation of the system. At the same time, a crowbar circuit is added to ensure a stable voltage output for the problem of DC overvoltage that may exist in the system. Finally, an experimental platform based on TMS320F28335 is built to verify the correctness and feasibility of the proposed method.

Accurate prediction of ship motion is very important for ensuring marine safety, weapon control, and aircraft carrier landing, etc. Ship motion is a complex time-varying nonlinear process which is affected by many factors. Time series analysis method and many machine learning methods such as neural networks, support vector machines regression (SVR) have been widely used in ship motion predictions. However, these single models have certain limitations, so this paper adopts a multi-model prediction method. First, ensemble empirical mode decomposition (EEMD) is used to remove noise in ship motion data. Then the random forest (RF) prediction model optimized by genetic algorithm (GA), back propagation neural network (BPNN) prediction model and SVR prediction model are respectively established, and the final prediction results are obtained by results of three models. And the weights coefficients are determined by the correlation coefficients, reducing the risk of prediction and improving the reliability. The experimental results show that the proposed combined model EEMD-GARF-BPNN-SVR is superior to the single predictive model and more reliable. The mean absolute percentage error (MAPE) of the proposed model is 0.84%, but the results of the single models are greater than 1%.

The performance of the path tracking control system is poor when vehicles navigate under extreme conditions of high speed and large curvature due to the uncertainty of vehicle model parameters, modeling errors and disturbances. To address this issue, this paper introduces a Linear Model Predictive Controller (GP‐SOMPC) based on Gaussian Process (GP) and Snake Optimizer (SO) to achieve high‐performance trajectory tracking control under large curvatures through learning. This algorithm adopts a two‐degree‐of‐freedom vehicle dynamics model and models disturbances and unmodeled dynamics as Gaussian Processes (GP) using collected experimental data. Additionally, an Adaptive Weighting Coefficient Model Predictive Controller based on the Snake Optimizer (SO) method is presented to improve the poor adaptation of vehicle trajectory tracking accuracy according to reference path curvature under fixed weighting coefficients in traditional Model Predictive Control (MPC), which results in inadequate trajectory tracking precision. The simulation experiments have verified that the proposed GP‐SOMPC algorithm can enhance trajectory tracking precision under high‐speed, large curvature conditions and exhibits good adaptability to variations in path curvature.

In this article, two new algorithms of the redundant force branch of 6-PUS/UPU parallel robot are proposed. They are model predictive control combining with proportional, integral, and differential algorithm and fuzzy combining with model predictive control algorithm. The shortcoming of the traditional model predictive control algorithm is complex adjustment, large amount of calculation, the dynamic performance effect of the system. The proposed PID model predictive control algorithm can make the controller parameters adjustment more convenient. However, PID model predictive control algorithm can’t obtain good control performance under sudden change in situation. Combining model predictive control algorithm with fuzzy theory, fuzzy model predictive control algorithm has better anti-interference ability than PID model predictive control algorithm and can reduce predictive horizon length as possible as it can. Simulation results show that fuzzy model predictive control algorithm can effectively improve real-time performance of control system, the dynamic tracking performance and robustness than the traditional model predictive control and PID model predictive control algorithm.

This paper proposes an improved constrained networked model predictive tracking control design for the chamber pressure of a coke furnace under uncertainty and packet losses. Unlike conventional constrained model predictive control (MPC) strategies that have a limitation in the consideration of both set-point tracking and the dynamic process responses, the system state variables and output tracking errors are combined and thus can be regulated simultaneously in the new MPC scheme. Based on such advantages, there are more degrees of freedom for the subsequent controller design and improved system performance can then be obtained. Case studies on the regulation of chamber pressure of a coke furnace under process uncertainties and packet losses are investigated to verify the proposed approach in comparison with typical traditional constrained MPC schemes.

To improve the dynamic and steady-state control performance of permanent magnet synchronous motors under the three-vector model predictive current control method, this study proposes a switching current predictive control method based on the exponential moving average algorithm, which evaluates the magnitude of the change of the q-axis current slope in real time to discriminate the motor’s operating conditions and selects the optimal control method for different operating conditions. Meanwhile, the traditional three-vector model predictive current control method is improved by introducing a comparison mechanism for the q-axis current slope to select the second effective voltage vector, avoiding the secondary optimization calculation of the value function and reducing the computational complexity of the traditional method. By comparing the proposed method with the traditional three-vector model predictive current control method, the experimental results prove that the proposed method improves the system’s dynamic response and steady-state performance.

Proactive self-adaptation using Model Predictive Control (MPC) is well studied for software systems operating in dynamic environments. However, its practical adoption is limited by two key challenges. First, the modeling gap: traditional MPC requires state-space models that are difficult to construct for complex software systems, often demanding extensive domain expertise and incurring high computational costs. Second, the tuning gap: configuring MPC to balance abstract and competing objectives-such as performance, resource efficiency, and control stability-typically relies on manual, expert-driven tuning, impeding autonomous operation. To address these challenges, we propose a dual-layer MPC framework. At the lower layer, an Input Convex Neural Network (ICNN) is used to learn complex nonlinear dynamics directly from data, enabling tractable optimization and reducing the modeling burden. At the upper layer, a delta-driven controller manager adaptively tunes the lowerlayer MPC by monitoring deviations in system behavior and estimating the impact of different cost components on overall utility, thereby automating the tuning process. We evaluate our approach on SIMDEX, a job scheduling simulator, where it demonstrates superior performance over both the traditional requirements-oriented MPC framework CobRA and the nonadaptive ICNN-based controller, achieving a better balance of performance, resource efficiency, and control stability.

In order to harness a greater share of wind energy resources, offshore wind energy projects are venturing into deep-sea locations. In this progression, the issue of grid integration control becomes increasingly challenging. Traditional Model Predictive Control (MPC) has been introduced in offshore wind energy grid integration control due to its merits, such as not requiring modulators, dispensing with decoupling, incorporating constraint handling, and facilitating online optimization. However, it heavily relies on a model and consequently experiences a substantial loss of control effectiveness in the face of system parameter variations. In light of this, this study presents an active-disturbance-rejection-based three-vector sequence model predictive control approach. This method effectively mitigates the influence caused by changes in system parameters, endowing the system with self-disturbance rejection capabilities and enhancing its fault recovery abilities. The method employs self-disturbance control to track voltage reference values and employs the concept of sequence control to eliminate weighting factors in the cost function. Furthermore, it employs three-vector control to achieve error-free operation. The simulation results confirmed that the proposed method effectively minimizes voltage and power transients. It demonstrated superior control effectiveness and shorter response times, enabling the system to rapidly restore to a stable operational state following disturbances.

Although the traditional model predictive control (MPC) can theoretically provide AC current and circulating current control for modular multilevel converters (MMCs) in battery energy storage grid-connected systems, it suffers from stability problems due to the power quality of the power grid and model parameter mismatches. A two discrete-time disturbance observers (DOBs)-based MPC strategy is investigated in this paper to solve this problem. The first DOB is used to improve the AC current quality and the second enhances the stability of the circulating current control. The distortion and fluctuation of grid voltage and inductance parameter variation are considered as lump disturbances in the discrete modeling of a MMC. Based on the proposed method, the output prediction is compensated by disturbance estimation to correct the AC current and circulating current errors, which eventually achieve the expected tracking performance. Moreover, the DOBs have a quite low computational cost with minimum order and optimal performance properties. Since the designed DOBs work in parallel with the MPC, the control effect is improved greatly under harmonics, 3-phase unbalance, voltage sag, inductance parameter mismatches and power reversal conditions. Simulation results confirm the validity of the proposed scheme.

This paper focuses on motion prediction for a ship navigating through sea swell. Ship motion prediction may be useful for helicopter maritime operations, notably for search and rescue missions. An efficient prediction method based on adaptive notch filters is proposed for non stationary perturbations. Classic methods of prediction are reviewed for comparison. An application using real ship motion data is considered in a performance evaluation. Finally, a comparative analysis based on prediction performance and real-time implementation constraints is presented.

Unmanned Underwater Vehicles (UUVs) can be utilized to perform difficult tasks in cluttered environments such as harbor and port protection. However, since UUVs have nonlinear and highly coupled dynamics, motion planning and control can be difficult when completing complex tasks. Introducing models into the motion planning process can produce paths the vehicle can feasibly traverse. As a result, Sampling-Based Model Predictive Control (SBMPC) is proposed to simultaneously generate control inputs and system trajectories for an autonomous underwater vehicle (AUV). The algorithm combines the benefits of sampling-based motion planning with model predictive control (MPC) while avoiding some of the major pitfalls facing both traditional sampling-based planning algorithms and traditional MPC. The method is based on sampling (i.e., discretizing) the input space at each sample period and implementing a goal-directed optimization (e.g., A*) in place of standard numerical optimization. This formulation of MPC readily applies to nonlinear systems and avoids the local minima which can cause a vehicle to become immobilized behind obstacles. The SBMPC algorithm is applied to an AUV in a 2D cluttered environment and an AUV in a common local minima problem. The algorithm is then used on a full kinematic model to demonstrate the benefits.

This paper introduces a quasi‐linear parameter‐varying model predictive control (qLPV MPC) framework for robust trajectory tracking of fully actuated unmanned underwater vehicles (UUVs), explicitly considering external disturbances and input constraints. To address the challenges of nonlinear dynamics, actuator limitations, and external disturbances, the UUV system is formulated as a qLPV model to balance the computational efficiency and the control accuracy. Then, a qLPV MPC method is designed for UUVs by incorporating an auxiliary feedback control law and a terminal constraint set, which ensures iterative feasibility and asymptotic stability. The input‐to‐state stability (ISS) is mathematically proven through Lyapunov‐based analysis, so that the proposed framework could stabilize bounded tracking errors despite system disturbances. Finally, an iterative solution method based on sequential quadratic programming (SQP) is introduced to obtain efficient solutions of the optimal control sequence while reducing the computational complexity. Simulation studies are conducted to validate the effectiveness of the proposed controller. The results show that the proposed method exhibits smaller tracking errors and faster convergence speeds compared to traditional robust nonlinear model predictive control (NMPC).