Search Results (1,307)

To improve the tracking accuracy and the adaptability of intelligent vehicles in various road conditions, an adaptive model predictive controller combining reinforcement learning is proposed in this paper. Firstly, to solve the problem of control accuracy decline caused by a fixed prediction time domain, a low-computational-cost adaptive prediction horizon strategy based on a two-dimensional Gaussian function is designed to realize the real-time adjustment of prediction time domain change with vehicle speed and road curvature. Secondly, to address the problem of tracking stability reduction under complex road conditions, the Deep Q-Network (DQN) algorithm is used to adjust the weight matrix of the Model Predictive Control (MPC) algorithm; then, the convergence speed and control effectiveness of the tracking controller are improved. Finally, hardware-in-the-loop tests and real vehicle tests are conducted. The results show that the proposed adaptive predictive horizon controller (DQN-AP-MPC) solves the problem of poor control performance caused by fixed predictive time domain and fixed weight matrix values, significantly improving the tracking accuracy of intelligent vehicles under different road conditions. Especially under variable curvature and high-speed conditions, the proposed controller reduces the maximum lateral error by 76.81% compared to the unimproved MPC controller, and reduces the average absolute error by 64.44%. The proposed controller has a faster convergence speed and better trajectory tracking performance when tested on variable curvature road conditions and double lane roads. Full article

This paper presents and compares two model predictive control (MPC) approaches for battery cell state-of-charge (SOC) balancing. In both approaches, a linearized discrete-time model that takes into account individual cell capacities is used. The first approach is a linear MPC controller that effectively regulates multiple cells to track a target SOC level while satisfying physical constraints. The second approach is based on explicit MPC implementation to reduce online computation while achieving a comparable performance. The simulation results suggest that explicit MPC can deliver the same balancing performance as linear MPC, while achieving faster online execution. Specifically, explicit MPC reduces the computation time by $37.3\%$ in a five-cell battery example, with the cost of higher offline computation. However, simulation results also reveal a significant limitation for explicit MPC for battery systems with a larger number of cells. As the number of cells increases and/or the prediction horizon increases, the computational requirements grow exponentially, making its application to SOC balancing for large battery systems impractical. To the best of the authors’ knowledge, this is the first study that compares MPC and explicit MPC algorithms in the context of battery cell balancing. Full article

The development and implementation of scientifically substantiated solutions for the improvement and modernization of electromechanical devices, systems, and complexes, including electric drives, is an urgent theoretical and applied task for energetics, industry, transport, and other key areas, both in global and national contexts. The aim of this paper is to identify a rational model of an induction motor that balances computational simplicity and control system performance based on predictive approaches while ensuring maximum energy efficiency and reference tracking during the operation in dynamic modes. Five main mathematical models of an induction machine with different levels of detail have been selected. Three predictive control models have been implemented using GRAMPC (v 2.2), Matlab MPC Toolbox (v 24.1), and fmincon (R2024a) (from Matlab Optimization Toolbox). It has been established that in the dynamic mode of operation, the equivalent induction motor circuit with parameters $R_{fe} =const$, $L_{\mu }=f\left(I_{1d}\right)$, and $T_F=f\left({\omega }_{Rm}\right)$ is the most appropriate in terms of the following criteria: accuracy of control action generation, computation speed, and calculation of energy consumption. Full article

The stroke control of a hydrostatic transmission (HST) is crucial for improving the energy efficiency and power variability of heavy-duty vehicles, including agricultural, construction, mining, and forestry equipment. This study introduces a new control strategy: an Artificial Neural Network (ANN) controller that imitates a Multiple Model Predictive Controller (MPC). The goal is to compare their performance in controlling the HST’s stroke. The proposed controller is designed to track complex stroke reference trajectories for both primary and secondary regulations under realistic disturbances, such as engine and load torques, which are influenced by soil and road conditions for an HST system in line with a nonlinear and time-varying mathematical model. Processor-in-the-Loop simulations suggest that the ANN controller holds several advantages over the Multiple MPC and classical control strategies. These benefits include its suitability for multi-input–multi-output systems, its insensitivity to external stochastic disturbances (like white noise), and its robustness against modeling errors and uncertainties, making it a promising option for real-time HST implementation and better than the Multiple MPC scheme in terms of simplicity and computational cost-effectiveness. Full article

Aiming at the stochasticity, uncertainty, and strong perturbation of the linear Fresnel solar thermal power collection subsystem, this study establishes a multivariate prediction model for the linear Fresnel collector subsystem based on complex environmental characteristics and designs a PID controller and MPC controller for the tracking and control of the outlet temperature. By analyzing the heat transfer process of the collector, constructing a model in Multiphysics for three-dimensional modeling of the collector, extracting data through simulation, fuzzy clustering the data and using different clustering centers for parameter identification in order to obtain the multi-model. By using the field data from the site of Dunhuang Dacheng Linear Fresnel Molten Salt Collector Field, considering the inlet temperature, normal direct irradiance and wind speed are used as the perturbation quantities, and the flow rate of molten salt is used as the control quantity. Considering three representative weather conditions, the switching criterion of minimizing the real-time point error is adopted for switching the outlet temperature of the collector. Simulation analysis results show that under the same conditions, the tracking error of the single model is relatively large, with the output temperature error fluctuating between −100 °C and 100 °C and containing many burrs. In contrast, the output temperature error of the multi-model switching control is controlled within 50 °C, which features a smaller tracking error and a faster tracking speed compared with the single-model control. When faced with large disturbances, the multi-model MPC switching control achieves better tracking performance than the multi-model PID switching control. It tracks temperatures closer to the set value, with a faster tracking speed and more excellent anti-interference performance. Full article

Digital Twins (DTs) have become central to intelligent asset management within Industry 4.0, enabling real-time monitoring, diagnostics, and predictive maintenance. However, implementing Prognostics and Health Management (PHM) strategies within DT frameworks remains a significant challenge, particularly in systems experiencing multi-component degradation (MCD). MCD occurs when several components degrade simultaneously or in interaction, complicating detection and isolation processes. Traditional data-driven fault detection models often require extensive historical degradation data, which is costly, time-consuming, or difficult to obtain in many real-world scenarios. This paper proposes a model-based, control-driven approach to MCD detection, which reduces the need for large training datasets by leveraging reference tracking performance in closed-loop control systems. We benchmark the accuracy of four control strategies—Proportional-Integral (PI), Linear Quadratic Regulator (LQR), Model Predictive Control (MPC), and a hybrid model—within a Digital Twin-enabled hydraulic system testbed comprising multiple components, including pumps, valves, nozzles, and filters. The control strategies are evaluated under various MCD scenarios for their ability to accurately detect and isolate degradation events. Simulation results indicate that the hybrid model consistently outperforms the individual control strategies, achieving an average accuracy of 95.76% under simultaneous pump and nozzle degradation scenarios. The LQR model also demonstrated strong predictive performance, especially in identifying degradation in components such as nozzles and pumps. Also, the sequence and interaction of faults were found to influence detection accuracy, highlighting how the complexities of fault sequences affect the performance of diagnostic strategies. This work contributes to PHM and DT research by introducing a scalable, data-efficient methodology for MCD detection that integrates seamlessly into existing DT architectures using containerized RESTful APIs. By shifting from data-dependent to model-informed diagnostics, the proposed approach enhances early fault detection capabilities and reduces deployment timelines for real-world DT-enabled PHM applications. Full article

Model predictive control (MPC) is significantly affected by parameter mismatch in inverter applications, whereas model-free predictive control (MFPC) avoids parameter dependence through the ultra-local model (ULM). However, the traditional MFPC based on the algebraic method needs to store historical data for multiple cycles, which results in a sluggish dynamic response. To address the above problems, this paper proposes a model-free predictive control method based on the ultra-local model and an adaptive super-twisting sliding mode observer (ASTSMO). Firstly, the effect of parameter mismatch on the current prediction error of conventional MPC is analyzed through theoretical analysis, and a first-order ultra-local model of the inverter is established to enhance robustness against parameter variations. Secondly, a super-twisting sliding mode observer with adaptive gain is designed to estimate the unknown dynamic terms in the ultra-local model in real time. Finally, the superiority of the proposed method is verified through comparative validation against conventional MPC and the algebraic-based MFPC. Simulation results demonstrate that the proposed method can significantly enhance robustness against parameter variations and shorten the settling time during dynamic transients. Full article

Produced water treatment (PWT) is a critical process in offshore oil and gas production, ensuring compliance with stringent environmental discharge regulations and minimizing environmental impact. This process is characterized by inherent nonlinearities, coupled system dynamics, and the presence of significant disturbances that can impede operational efficiency and separation performance. Effective control strategies are essential to maintain stable operation and high separation efficiency under dynamic and uncertain conditions. This paper presents a comprehensive evaluation of advanced control methods applied to a pilot-scaled PWT facility designed to replicate offshore conditions. Four control solutions are assessed, i.e., (i) baseline approach using PID controllers; (ii) Multi-Input–Multi-Output (MIMO) $H_{\infty }$ control; (iii) MIMO Model Predictive Control (MPC); and (iv) MIMO Model Reference Adaptive Control (MRAC). The motivation lies in their differing capabilities for disturbance rejection, tracking accuracy, robustness, and computational feasibility. Real-world operational data were used to assess each strategy in regulating critical process variables, the interface water level in the three-phase gravity separator, and the pressure drop ratio (PDR) in the hydrocyclone, both closely linked to de-oiling efficiency. The results highlight the distinct advantages and limitations of each method. In general, the baseline PID solution offers simplicity but limited adaptability, while advanced strategies such as MIMO $H_{\infty }$, MPC, and MRAC solutions demonstrate enhanced reference-tracking and de-oiling performances subject to diverse operating conditions and disturbances, though different control solutions still exhibit different dynamic characteristics. The findings provide systematic insights into selecting optimal control architectures for offshore PWT systems, supporting improved operational performance and reduced environmental footprint. Full article

Model predictive control (MPC) is a commonly used online strategy for maximizing economic benefits in smart homes that integrate photovoltaic (PV) panels, electric vehicles (EVs), and battery energy storage systems (BESSs). However, prediction errors associated with PV power and load demand can lead to economic losses. Scenario-based MPC can mitigate the impact of prediction errors by computing the expected objective value of multiple stochastic scenarios. However, reducing the number of scenarios is often necessary to lower the computation burden, which in turn causes some economic loss. To achieve online operation and maximize economic benefits, this paper proposes utilizing the consensus alternating direction method of multipliers (C-ADMM) algorithm to quickly calculate the scenario-based MPC problem without reducing stochastic scenarios. First, the system layout and relevant component models of smart homes are established. Then, the stochastic scenarios of net load prediction error are generated through Monte Carlo simulation. A consensus constraint is designed about the first control action in different scenarios to decompose the scenario-based MPC problem into multiple sub-problems. This allows the original large-scale problem to be quickly solved by C-ADMM via parallel computing. The relevant results verify that increasing the number of stochastic scenarios leads to more economic benefits. Furthermore, compared with traditional MPC with or without prediction error, the results demonstrate that scenario-based MPC can effectively address the economic impact of prediction error. Full article

This article presents an interaction-aware motion planning framework that integrates a graph neural network (GNN) based multi-modal trajectory predictor with a model predictive control (MPC) based planner. Unlike past studies that predict a single future trajectory per agent, our algorithm outputs three distinct trajectories for each surrounding road user, capturing different interaction scenarios (e.g., yielding, non-yielding, and aggressive driving behaviors). We design a GNN-based predictor with bi-directional gated recurrent unit (Bi-GRU) encoders for agent histories, VectorNet-based lane encoding for map context, an interaction-aware attention mechanism, and multi-head decoders to predict trajectories for each mode. The MPC-based planner employs a bicycle model and solves a constrained optimal control problem using CasADi and IPOPT (Interior Point OPTimizer). All three predicted trajectories per agent are fed to the planner; the primary prediction is thus enforced as a hard safety constraint, while the alternative trajectories are treated as soft constraints via penalty slack variables. The designed motion planning algorithm is examined in real-world intersection scenarios from the INTERACTION dataset. Results show that the multi-modal trajectory predictor covers possible interaction outcomes, and the planner produces smoother and safer trajectories compared to a single-trajectory baseline. In high-conflict situations, the multi-modal trajectory predictor anticipates potential aggressive behaviors of other drivers, reducing harsh braking and maintaining safe distances. The innovative method by integrating the GNN-based multi-modal trajectory predictor with the MPC-based planner is the backbone of the effective motion planning algorithm for robust, safe, and comfortable autonomous driving in complex intersections. Full article

Three-level non-inverting buck–boost converters are promising for electric vehicle charging stations due to their wide voltage regulation capability and bidirectional power flow. However, the number of three-level operating states is four times that of two-level operating states, and the lack of a unified switching state selection mechanism leads to serious challenges in its application. To address these issues, a finite control set model predictive control (FCS-MPC) strategy is proposed, which can determine the optimal set and select the best switching state from the excessive number of states. Not only does the proposed method achieve fast regulation over a wide voltage range, but it also maintains the input- and output-side capacitor voltage balance simultaneously. A further key advantage is that the number of switching actions in adjacent cycles is minimized. Finally, a hardware-in-the-loop experimental platform is built, and the proposed control method can realize smooth transitions between multiple operation modes without the need for detecting modes. In addition, the state polling range and the number of switching actions are superior to conventional predictive control, which provides an effective solution for high-performance multilevel converter control in energy systems. Full article

Given the growing need for high-performance operation of 4WID-UGVs, coordinated optimization of trajectory tracking, vehicle stability, and energy efficiency poses a challenge. Existing control strategies often fail to effectively balance these multiple objectives, particularly in integrating energy-saving goals while ensuring precise trajectory following and stable vehicle motion. Thus, a hierarchical control architecture based on Model Predictive Control (MPC) is proposed. The upper-level controller focuses on trajectory tracking accuracy and computes the optimal longitudinal acceleration and additional yaw moment using a receding horizon optimization scheme. The lower-level controller formulates a multi-objective allocation model that integrates vehicle stability, energy consumption, and wheel utilization, translating the upper-level outputs into precise steering angles and torque commands for each wheel. This work innovatively integrates multi-objective optimization more comprehensively within the intelligent vehicle context. To validate the proposed approach, simulation experiments were conducted on S-shaped and circular paths. The results show that the proposed method can keep the average lateral and longitudinal tracking errors at about 0.2 m, while keeping the average efficiency of the wheel hub motor above 85%. This study provides a feasible and effective control strategy for achieving high-performance, energy-saving autonomous driving of distributed drive vehicles. Full article

This paper addresses the issue of the poor collision avoidance effect of underactuated surface vehicles (USVs) during local path tracking. A virtual ship group control method is suggested by using Freiner coordinates and a model predictive control (MPC) algorithm. We track the planned path using the MPC algorithm according to the known vessel state and build a hierarchical weighted cost function to handle the state of the virtual vessel, to ensure that the vessel avoids obstacles while tracking the path. In addition, the control system incorporates an Extended Kalman Filter (EKF) algorithm to minimize the state estimation error by continuously updating the ship state and providing more accurate state estimation for the system in a timely manner. In order to validate the anti-interference and robustness of the control system, the simulation experiment is carried out with the “Yukun” as the research object by adding the interference of wind and wave of level 6. The outcome shows that the algorithm suggested in this paper can accurately perform the trajectory-tracking task and make collision avoidance decisions under six levels of external interference. Compared with the original MPC algorithm, the improved MPC algorithm reduces the maximum rudder angle output value by 58%, the integral absolute error by 46%, and the root mean square error value by 46%. The control method provides a new technical choice for trajectory tracking and collision avoidance of USVs in complex marine environments, with a reliable theoretical basis and practical application value. Full article

Direct torque control (DTC) based on the finite control set model predictive control (FCS-MPC) provides a straightforward and intuitive solution for controlling permanent magnet synchronous motors (PMSMs). However, conventional FCS-MPC relies on appropriately tuned weighting factors in the cost function, which have a significant impact on the control performance and increase design complexity. This paper presents a comprehensive experimental comparison of emerging FCS-MPC strategies for DTC of PMSMs that eliminate the need for weighting factors. Specifically, a sequential FCS-MPC approach is benchmarked against decision-making-based FCS-MPC methods that employ Euclidean distance normalisation. Extensive experimental results, obtained across a wide range of operating conditions, are used to assess current total harmonic distortion (THD), torque and flux ripple, and transient performance. Results indicate that while all methods yield comparable current THD, decision-making-based strategies achieve superior torque and flux regulation with reduced ripple compared to the sequential approach. These findings demonstrate that decision-making-based FCS-MPC methods provide additional flexibility in defining control objectives, eliminating the need to design weighting factors, such as those used in the sequential method while offering superior performance. Full article

To address the issue of key component degradation in hybrid electric commercial vehicles under complex driving cycles negatively impacting system economy and durability, this paper proposes a model predictive control (MPC)-based energy management co-optimization strategy. Firstly, dynamic degradation models for the key components are established, enabling high-fidelity characterization of component health status. Secondly, a system-level model incorporating vehicle dynamics, power battery, and electric drive motor is developed, with the degradation feedback mechanism deeply integrated. Building on this foundation, an MPC-based energy management strategy for multi-objective optimization is designed. Its core functionality lies in the cooperative allocation of power sources within a rolling horizon framework: by integrating component degradation status as critical feedback into the control loop, the strategy proactively coordinates the optimization objectives between operational economy (minimization of equivalent energy consumption) and key component durability (degradation mitigation). Simulation results demonstrate that, compared to traditional energy management strategies, the proposed strategy significantly enhances system performance while ensuring vehicle drivability: equivalent energy efficiency improves by approximately 3.5%, component degradation is reduced by up to 87%, and superior state of charge (SOC) regulation capability for the battery is achieved. This strategy provides an effective control method for achieving intelligent, long-life, and high-efficiency operation of hybrid electric commercial vehicles. Full article