Distributed-drive commercial vehicles are prone to skidding or rolling over when operating on low-friction roads or negotiating tight curves. To address this issue, this paper proposes a control strategy based on Adaptive Model Predictive Control (AMPC) to coordinate yaw and roll stability of distributed-drive commercial vehicles. By analyzing the improved β−β˙ phase-plane boundary and the roll stability threshold, this study identifies the yaw rate, sideslip angle, and predicted lateral load transfer rate (PLTR) as key indicators for vehicle stability assessment. The AMPC controller employs these metrics to dynamically adjust the control weights associated with yaw and roll stability in real time, thereby calculating the required additional yaw moment, which is applied through optimal torque distribution among all four wheels to achieve coordinated control. Finally, experiments are conducted on a Simulink-TruckSim co-simulation platform to assess the performance of AMPC. Compared with the conventional MPC method, the proposed approach achieves obvious improvements in both roll and yaw stability under sinusoidal and fishhook operating conditions.

Adaptive MPC method based on control compensation for disturbance and model parameters

ABSTRACT 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).

Virtual Leader-Follower Based Platooning Under Mixed Traffic: A Data-Driven Distributionally Robust MPC Method

<div>To achieve accurate and stable path tracking for unmanned mining trucks in the face of changing paths and response delays in steering, this study raised a lateral control strategy for unmanned mining trucks based on MPC and considering steering delay response characteristics. Under the basis of deriving the state space equation from the commonly used two degrees of freedom truck dynamics model, this method introduces the dynamic relationship between steering angle issuance and actual response to form an augmented form of state vector to overcome the control instability caused by steering response delay. Then, based on the MPC method, a constrained objective function is constructed to solve for the optimal control law. In response to the problem of inaccurate selection of prediction and control time domains, this article proposes an adaptive selection method for prediction and control time horizon based on a modified particle swarm optimization (MPSO) algorithm, which obtains the optimal prediction and control time horizon that meet the preset training road conditions in this study, preventing the problem of control accuracy and control oscillation hard to balance caused by the horizon being too small or too large, thereby improving the control effect. Finally, the tracking performance of this algorithm was compared with pure tracking algorithms using a truck dynamics model bench simulation developed independently based on Simulink and a mining truck real-vehicle verification. The algorithm demonstrated good path tracking performance.</div>

As a core component of the photovoltaic-storage microgrid systems, three-port DC–DC converters have attracted significant attention in recent years. This paper proposes a multi-vector modulated model predictive control (MVM-MPC) method based on vector analysis for a non-isolated three-port DC–DC converter formed by paralleling two bidirectional DC–DC converters. The proposed modulated MPC method utilizes three basic vectors to calculate the optimal switching sequence for minimizing the error vector. It can significantly minimize voltage ripple while maintaining the nonlinear and dynamic performance characteristics of a traditional MPC. MATLAB/Simulink R2024a simulations and hardware-in-loop (HIL) experimental results demonstrate that, compared with finite control set MPC and traditional two-vector modulated MPC methods, the proposed approach achieves remarkable reductions in current ripple and voltage ripple, along with excellent dynamic performance featuring smooth mode-switching.

To ensure grid stability and efficient power conversion, advanced inverter technologies play a crucial role in integrating photovoltaic (PV) systems into the electrical grid. The article suggests a photovoltaic system which is connected to the grid utilizing a nine-level Packed U-Cell (PUC9) topology, which incorporates a PV panel coupled with a DC-DC boost converter. This setup is managed by a Maximum Power Point Tracking (MPPT) algorithm, specifically using the Perturb and Observe (P&O) method, to optimize energy output by adjusting to varying irradiance and temperature conditions. The PUC9 inverter is designed with four pairs of complementary switches, one DC source, and two flying capacitors, and connects to the grid via a filtering inductor. This topology generates nine distinct voltage levels with fewer active and passive components than traditional multilevel inverters, leading to improved output quality and reduced total harmonic distortion (THD). The study assesses the performance of an advanced Finite Control Set Model Predictive Control (FCS-MPC) approach, comparing it to a traditional Proportional-Integral (PI) Pulse-width Modulation (PWM) method. While PI-PWM is recognized for its straightforward design and simplicity in application, it often struggles to maintain consistent performance under variable conditions due to its dependency on fixed control parameters. In contrast, the FCS-MPC approach offers dynamic response and better adaptability, which are essential for effective power conversion and system stability. Simulations conducted in MATLAB/SimulinkTM prove that the suggested MPC method provides improved tracking accuracy for maximum power points under changing irradiance conditions while maintaining efficient integration of power into the grid. The findings underline the potential of the PUC9 topology combined with the FCS-MPC strategy to provide high-quality output with improved robustness and resilience, making them viable solutions for contemporary renewable energy applications.

This study proposes a novel finite control set model predictive control strategy (FCS-MPC) to suppress common-mode voltage (CMV) in electric propulsion systems while maintaining current quality. The key innovation is a virtual multi-vector synthesis method that eliminates zero-voltage vectors by adaptively generating small and medium vectors based on the modulation index and voltage sector. Unlike conventional CMV-reduction methods that compromise current quality or rely on fixed switching states, the proposed method enhances voltage resolution without relying on zero vectors. Simulation results demonstrate that, compared to conventional MPC, the proposed method reduces the CMV from approximately 100 V to 33 V—a 66.7% reduction. In terms of current quality, it achieves a 22.0% reduction in total harmonic distortion (THD) compared to the conventional reduced-CMV MPC method under low modulation conditions, while avoiding its excessive switching frequency. Experimental validation confirms both stable waveform generation and robust CMV suppression, while the proposed MPC reduces DSP execution time from 30.88 μs to 22.71 μs, thereby increasing computational availability to 77.3% and enabling real-time implementation on low-cost hardware. These results confirm the practicality of the proposed controller for real-time marine propulsion systems requiring both electromagnetic compatibility and high current quality.

Abstract This study proposes a robust H ∞ -model predictive control cooperative adaptive cruise control framework for heterogeneous vehicle platoons addressing communication delays and parametric uncertainties. Key innovations include: (1) A distributed architecture enabling real-time computational feasibility through linear matrix inequality solutions; (2) Integration of safety, tracking precision, passenger comfort, and communication reliability via S-procedure-based asymmetric input constraints Validated through Prescan-Simulink co-simulations, the controller demonstrates delay compensation efficacy across three scenarios: normal cruise, traffic light-induced platoon disassembly/reassembly, and external vehicle merging. The method reduces spacing oscillations by 41% compared to intelligent driver model baselines while maintaining string stability under 1.0 s V2X delays, while also outperform a pure H ∞ control by faster catch up in disrupted scenarios.

According to the wheel/rail actual dimensions, the modeling process of a 3-D full-size wheel/rail sliding contact finite element model is introduced in detail. During modeling process, the partitioning strategy method and MPC method are adopted. The temperature characteristics of the contact region during sliding contact are researched. The research results show the contact patch shape is close to an ellipse. The stress in the contact area is very concentrated, and the maximum von Mises stress appears in the subsurface at a distance of 2 mm from the contact interface. During the sliding contact, the maximum temperature appears at the contact center. The temperature on wheel contact surface ascends continuously and is significantly greater than the rail surface temperature. High temperatures of contact region are mainly distributed in the contact surface and subsurface, and the influence depth of temperature does not exceed 3 mm.

A wave energy converter (WEC) utilizing the inertial gyroscope coupled with a hydraulic power take-off (PTO) unit for energy transformation and application is investigated. The structure design of various components of WEC are introduced. Power is obtained by the dynamic response of the floating body under the excitation of wave energy, and converted into gyroscopic precession in the form of swing angle and torque, and directly to drive the hydraulic pump module of the hydraulic PTO system to output the hydraulic energy. The fluctuation of pressure and flow in the hydraulic PTO system can be adjusted and smoothed by means of the accumulator, which can effectively improve power output stationarity of the hydraulic motor-generator. The mathematical models of energy conversion and transmission process encompassing the wave-to-hydraulic PTO unit are established. The impact rules of major variables in the gyroscope and hydraulic PTO unit acting on WEC system are investigated by numerical simulations. It can provide scientific basis for optimizing utilization of this wave energy conversion system, and provide mathematical guidance for the optimization control of the WEC system. To improve the computation real time and control precision of power output of the hydraulic motor-generator unit, an Adaptive Hierarchical Model Predictive Control (AHMPC) method is proposed and put into used. The AHMPC strategy utilizes a larger wave prediction sequence level to solve the offline unconstrained energy maximization control problem, and then a smaller level to deal with the online constraints. The AHMPC method saves 3 times the computation time compared to the traditional MPC method, reduces the energy fluctuation amplitude in the system by 96%, so that the hydraulic motor-generator can operate at the desired speed based on the available wave forecast information while satisfying the state constraints.

Coordinated control of EAU-based MEA power grid using MPC method

A Tube-based MPC Method for Path Tracking of Autonomous Vehicles with Disturbances at Handling Limits

This paper proposes a multi-objective nonlinear model predictive control (MOMPC) method based on an improved non-dominated sorting genetic algorithm II (NSGAII) for the path tracking problem of unmanned surface vehicles (USVs). To enhance performance in cross-track error, a varying look-ahead distance is utilized in the line of sight (LOS) algorithm, which allows the MPC control algorithm to compute the look-ahead distance and desired speed rather than directly calculating the control input. Since the cost function of the MPC algorithm includes multiple objective terms, a multi-objective model predictive control algorithm is employed to improve overall control performance. Additionally, an adaptive rotation-based simulated binary crossover (ARSBX) is integrated into the NSGAII algorithm, and the non-dominated sorting method is optimized to reduce computation time. These enhancements increase diversity and exploration in the solution space, enabling the algorithm to find the optimal solution more efficiently. Simulations conducted in two different scenarios demonstrate that the nonlinear MPC method based on the improved NSGAII successfully tracks the desired path; it achieved an improvement of approximately 41% in time performance and about 5% in path-tracking error performance, exhibiting strong control performance and robustness.

An energy trade-off management strategy for hybrid ships based on event-triggered model predictive control

In the context of constructing new power systems, distribution networks are increasingly incorporating distributed resources such as distributed photovoltaic (PV) systems, decentralized wind turbines (WTs), and new types of energy storage system (ESS), which may lead to prominent issues such as voltage overruns and reverse heavy overloads in the distribution network. While distributed resources are valuable for voltage regulation, their regulation characteristics vary with their operation means, and the randomness and volatility of renewable power generation will also influence the optimization and regulation of voltage in the distribution network. This paper proposes a multi-timescale reactive power optimization and regulation method for distribution networks in a multi-source interactive environment. Firstly, the voltage regulation characteristics of distributed PV systems, decentralized ESSs, and distributed WTs are analyzed. Based on this analysis, a multi-timescale voltage optimization scheme for distribution networks using the MPC method is proposed, which optimizes the voltage regulation strategies for each distributed resource in a rolling manner. Furthermore, an event-triggered real-time voltage zoning control strategy based on voltage sensitivity is proposed to address the real-time sudden voltage overlimit problems. The modified IEEE 33-node system is used to verify the performance of the proposed method. Simulation results indicate that the issue of voltage overruns at distribution network nodes has been improved, and the intraday rolling optimization yields results are more realistic compared with the day-ahead optimization method.

Abstract Most real‐world systems are affected by external disturbances, which may be impossible or costly to measure. For instance, when autonomous robots move in dusty environments, the perception of their sensors is disturbed. Moreover, uneven terrains can cause ground robots to deviate from their planned trajectories. Thus, learning the external disturbances and incorporating this knowledge into the future predictions in decision‐making can significantly contribute to improved performance. Our core idea is to learn the external disturbances that vary with the states of the system, and to incorporate this knowledge into a novel formulation for robust tube model predictive control (TMPC). Robust TMPC provides robustness to bounded disturbances considering the known (fixed) upper bound of the disturbances, but it does not consider the dynamics of the disturbances. This can lead to highly conservative solutions. We propose a new dynamic version of robust TMPC (with proven robust stability), called state‐dependent dynamic TMPC (SDD‐TMPC), which incorporates the dynamics of the disturbances into the decision‐making of TMPC. In order to learn the dynamics of the disturbances as a function of the system states, a fuzzy model is proposed. We compare the performance of SDD‐TMPC, MPC, and TMPC via simulations, in designed search‐and‐rescue scenarios. The results show that, while remaining robust to bounded external disturbances, SDD‐TMPC generates less conservative solutions and remains feasible in more cases, compared to TMPC.

Over the past decade, post-quantum cryptography has reached a tipping point; institutional bodies and stakeholders have initiated standardization and deployment, and various projects have achieved a reasonably high level of progress and even deployment and implementation. In July 2022, at the end of Round 3 of the NIST's PQC competition, 3 candidates were proposed for the NIST standardization for post-quantum digital signatures scheme: one signature scheme based on MLWE (Crystals-Dilithium), one signature based on NTRU (Falcon), and one signature based on hash (Sphincs+). Although the performance profiles and “black-box” security of these schemes are well understood, resistance to side-channel attacks remains a weak point for all of them. After that, the NIST announced that the PQC standardization process is continuing with a fourth round, with the following KEMs still under consideration: BIKE, Classic McEliece, HQC, and SIKE. However, there are no candidates of digital signature schemes left for consideration. As such, the NIST has issued a call for additional digital signature proposals to be considered in the PQC standardization process. Acceptance of documents ended on June 1, 2023. As a result, 40 candidates were selected for the role of DS standard, namely: 6 DS algorithms based on codes, one DS algorithm based on isogenies, 7 DS algorithms based on lattice operations, 7 candidates for the role of DS algorithm based on the MPC method -in-the-Head and 10 algorithms based on multivariate transformations, 4 DS schemes were selected based on symmetric cryptographic transformations, and 5 more candidates based on other types of cryptographic transformations. The NIST is primarily interested in additional general purpose signature schemes that are not based on structured lattices. For certain applications, such as certificate transparency, the NIST may also be interested in signature schemes that have short signatures and fast verification. The NIST is open to receiving additional materials based on structured lattices, but intends to diversify post-quantum signature standards. Therefore, any structured array-based signature proposal would need to significantly outperform CRYSTALS-Dilithium and FALCON in relevant applications and/or provide significant additional security properties to be considered for standardization. Thus, the purpose of this paper is to analyze, evaluate, and compare digital signature algorithms based on lattice cryptography, an additional PQC NIST competition, and compare them with already standardized lattice-based DS mechanisms, such as CRYSTALS-Dilithium and FALCON.

Improved energy-based arm current MPC method for HMMC under over-modulation conditions in unbalanced AC networks

Revisiting reachability-driven explicit MPC for embedded control