Search Results (18)

The objective of this study was to quantify the method-to-method variation between two widely used field indicators of the end-of-milking vacuum-exposure period (i.e., operational overmilking duration), and to identify cow- and milking-level factors associated with this variation. Operational overmilking was defined using two approaches: (i) MPC vacuum fluctuation patterns collected via VaDia™ recording devices, and (ii) milk flow curves generated from milking system data, with simulated ACR take-off thresholds ranging from 0.2 to 0.8 kg/min. Seven quarter combinations were analyzed to determine their effect on method-to-method variation. Multivariable modelling was used to investigate the factors which influenced the absolute difference in operational overmilking duration (ADOD) between methods, with larger ADOD indicating greater method-to-method variation. All quarter combinations showed large method-to-method variations. VaDia^TM-derived estimates indicated longer overmilking durations and higher milk flow at the onset of overmilking compared with the milk flow curve approach. Our findings showed that a combination of the rear quarters was significantly associated with the lowest ADOD, and that a combination of the front quarters was significantly associated with the highest ADOD. All other combinations did not differ from each other, indicating that combinations including one front and one rear quarter performed similarly, and that recording all four quarters did not improve agreement between methods within this dataset. Milk flow factors associated with increased ADOD included longer low flow times, longer high flow times, longer machine-on times, and increased yield. Vacuum values associated with increased ADOD included high short milk tube vacuum during the full milking, and high mouthpiece chamber vacuum levels during both the full milking and overmilking periods. High short milk tube vacuum during overmilking was associated with decreased ADOD. Wider teat diameters, longer teat lengths, and increased parity were associated with increased ADOD. These findings indicated that vacuum-based and flow-based indicators of operational overmilking capture different aspects of the end-milking process and should be clearly specified when measuring or reporting overmilking in research or commercial milking systems. Full article

In industrial processes, mismatches between models and actual systems often degrade the performance of Model Predictive Control (MPC), potentially leading to instability or safety violations under dynamic operating conditions. To address this challenge, the paper introduces a hybrid control architecture named Trans-Tube-MPC, which leverages Transformer-based temporal modeling and tube-based robust constraints to enhance the robustness of the control system against model failures. The approach employs a Transformer network trained on closed-loop operational data to predict and compensate for state deviations caused by disturbances, while adaptive tube constraints dynamically adjust prediction boundaries to mitigate the risk of overcorrection. The innovation of this method lies in the introduction of a dynamically adjusted tube width, which adapts based on the prediction discrepancy between the Transformer model and the state-space model, thus allowing the control system to remain robust even in the face of model failures. Experimental studies demonstrate that the Trans-Tube-MPC framework can maintain control performance under significant model parameter deviations where conventional MPC would fail. The proposed method provides an effective solution to the problem of model mismatch and prediction error and shows significant advantages in dealing with control issues under model failure conditions, establishing a new way to reconcile data-driven adaptability with the reliability of control systems. Full article

To address the issue of poor yaw stability in distributed-drive electric pickup trucks at medium-to-high speeds, particularly under the influence of continuously varying tire forces and road adhesion coefficients, a novel Kalman filter-based method for estimating the road adhesion coefficient, combined with a Tube-based Model Predictive Control (Tube-MPC) algorithm, is proposed. This integrated approach enables real-time estimation of the dynamically changing road adhesion coefficient while simultaneously ensuring vehicle yaw stability is maintained under rapid response requirements. The developed hierarchical yaw stability control architecture for distributed-drive electric pickup trucks employs a square root cubature Kalman filter (SRCKF) in its upper layer for accurate road adhesion coefficient estimation; this estimated coefficient is subsequently fed into the intermediate layer’s corrective yaw moment solver where Tube-based Model Predictive Control (Tube-MPC) tracks desired sideslip angle and yaw rate trajectories to derive the stability-critical corrective yaw moment, while the lower layer utilizes a quadratic programming (QP) algorithm for precise four-wheel torque distribution. The proposed control strategy was verified through co-simulation using Simulink and Carsim, with results demonstrating that, compared to conventional MPC and PID algorithms, it significantly improves both the driving stability and control responsiveness of distributed-drive electric pickup trucks under medium- to high-speed conditions. Full article

This study proposes a Tube-based Robust Model Predictive Control (Tube-RMPC) strategy for autonomous vehicle control to address model parameter uncertainties and variations in road–tire adhesion coefficients in complex road scenarios. More specifically, the proposed approach improves the representation of vehicle dynamic behavior by introducing a unified vehicle–tire modeling framework. To facilitate computational tractability and algorithmic implementation, the model is systematically linearized and discretized. Furthermore, the Tube-based Robust Model Predictive Control strategy is developed to improve adaptability to uncertainty in the road surface adhesion coefficient. The Tube-based Robust Model Predictive controller ensures robustness by establishing a robust invariant tube around the nominal trajectory, effectively mitigating road surface variations and enhancing stability. Finally, a co-simulation platform integrating CarSim and Simulink is employed to validate the proposed method’s effectiveness. The experimental results demonstrate that Tube-RMPC improves the path-tracking performance, reducing the maximum tracking error by up to 9.17% on an S-curve and 2.25% in a double lane change, while significantly lowering RMSE and enhancing yaw stability compared to MPC and PID. Full article

Active frequency response (AFR) plays a crucial role in addressing the challenge of insufficient frequency regulation caused by the spatiotemporal distribution of power grid frequency. However, power fluctuations in renewable energy sources impact the frequency regulation performance of renewable energy units participating in AFR, and there is a lack of systematic assessment of their frequency regulation capabilities. This paper proposes a process-optimized AFR method for renewable energy based on distributed model predictive control (DMPC) using tube and robust control barrier functions (RCBF). The method integrates tube MPC for renewable energy units in fault regions and constrains control parameters in normal regions using RCBF, forming an enhanced DMPC-based coordination process for interconnected systems. This optimization ensures that both conventional and renewable energy units can effectively perform AFR under fluctuating renewable energy conditions. Furthermore, within the AFR online decision-making process, the optimal deloading rate for renewable energy is determined to maintain sufficient power reserves and frequency regulation capabilities. Finally, simulations of an interconnected system with a high proportion of renewable energy validate the effectiveness of this process-driven approach in enhancing the AFR capabilities of renewable energy sources. Full article

In this paper, we present a data-driven model predictive control (DDMPC) framework specifically designed for constrained single-input single-output (SISO) nonlinear systems. Our approach involves customizing a set-theoretic receding horizon controller within a data-driven context. To achieve this, we translate model-based conditions into data series of available input and output signals. This translation process leverages recent advances in data-driven control theory, enabling the controller to operate effectively without relying on explicit system models. The proposed framework incorporates a robust methodology for managing system constraints, ensuring that the control actions remain within predefined bounds. By means of time sequences, the controller learns the underlying system dynamics and adapts to changes in real time, providing enhanced performance and reliability. The integration of set-theoretic methods allows for the systematic handling of uncertainties and disturbances, which are common when the trajectory of a nonlinear system is embedded inside a linear trajectory state tube. To validate the effectiveness of our DDMPC framework, we conduct extensive simulations on a nonlinear DC motor system. The results demonstrate significant improvements in control performance, highlighting the robustness and adaptability of our approach compared to traditional model-based MPC techniques. Full article

This paper presents tube-based Model Predictive Control (MPC) for the path and velocity tracking of an autonomous articulated vehicle. The target platform of this study is an autonomous articulated vehicle with a non-steerable axle. Consequently, the articulation angle and wheel torque input are determined by the tube-based MPC. The proposed MPC aims to achieve two objectives: minimizing path tracking error and enhancing robustness to disturbances. Furthermore, the lateral stability of the autonomous articulated vehicle is considered to reflect its dynamic characteristics. The vehicle model for the MPC is formulated using local linearization to minimize modeling errors. The reference state is determined using a virtual controller based on the linear quadratic regulator to provide the optimal reference for the MPC solver. The proposed algorithm was evaluated through a simulation study with base algorithms under noise injection into the sensor signal. Simulation results demonstrate that the proposed algorithm achieved the smallest path tracking error, compared to the base algorithms. Additionally, the proposed algorithm demonstrated robustness to external noise for multiple signals. Full article

The path tracking performance of autonomous vehicles is degraded by common disturbances, especially those that affect the safety of autonomous vehicles (AVs) in obstacle avoidance conditions. To improve autonomous vehicle tracking performances and their computational efficiency when subjected to common disturbances, this paper proposes a tube linear model predictive controller (MPC) framework for autonomous vehicles. A bicycle vehicle dynamics model is developed and employed in the tube MPC control design in the proposed framework. A robust invariant set is calculated with an efficient linear programming (LP) method, and it is used to guarantee that the constraints are satisfied under common disturbance conditions. The results show that the computational cost of robust positively invariant sets that are constructed by the LP method is much less than that obtained by the traditional method. In addition, all the trajectories of the tube linear MPC successfully avoided obstacles when under disturbance conditions, but only about 80% of the trajectories obtained with the traditional MPC successfully avoided obstacles under disturbance conditions. The proposed framework is effective. Full article

In order to enhance the performance of disturbance rejection in AUV’s path tracking, this paper proposes a novel tube-based event-triggered path-tracking strategy. The proposed tracking strategy consists of a speed control law and an event-triggered tube model predictive control (tube MPC) scheme. Firstly, the speed control law using linear model predictive control (LMPC) technology is obtained to converge the nominal path-tracking deviation. Secondly, the event-triggered tube MPC scheme is used to calculate the optimal control input, which can enhance the performance of disturbance rejection. Considering the nonlinear hydrodynamic characteristics of AUV, a linear matrix inequality (LMI) is formulated to obtain tight constraints on the AUV and the feedback matrix. Moreover, to enhance real-time performance, tight constraints and the feedback matrix are all calculated offline. An event-triggering mechanism is used. When the surge speed change command does not exceed the upper bound, adaptive tight constraints are obtained. Finally, numerical simulation results show that the proposed tube-based event-triggered path-tracking strategy can enhance the performance of disturbance rejection and ensure good real-time performance. Full article

This paper proposes a novel robust model predictive control (RMPC) scheme for constrained linear discrete-time systems with bounded disturbance. Firstly, the adjustable error tube set, which is affected by local error and error variety rate, is introduced to overcome uncertainties and disturbances. Secondly, the auxiliary control rate associated with the cost function is designed to minimize the discrepancy between the actual system and the nominal system. Finally, a constrained deep neural network (DNN) architecture with symmetry properties is developed to address the optimal control problem (OCP) within the constrained system while conducting a thorough convergence analysis. These innovations enable more flexible adjustments of state and control tube cross-sections and significantly improve optimization speed compared to the homothetic tube MPC. Moreover, the effectiveness and practicability of the proposed optimal control strategy are illustrated by two numerical simulations. In practical terms, for 2-D systems, this approach achieves a remarkable 726.23-fold improvement in optimization speed, and for 4-D problems, it demonstrates an even more impressive 7218.07-fold enhancement. Full article

This paper presents an optimization method that aims to mitigate disturbances in the radial-feed system of the ring-pendulum double-sided polisher (RDP) during processing. We built a radial-feed system model of an RDP and developed a single-tube robust model predictive control system to enhance the disturbance rejection capability of the radial-feed system. To constrain the system states inside the terminal constraint set and further enhance the system’s robustness, we added the ε-approximation to approach the single-tube terminal constraint set. Finally, the effectiveness of the proposed method for the RDP radial-feed system was verified through simulations and experiments. These findings demonstrate the potential of the proposed method for improving the performance of the RDP radial-feed system in practical applications. The polish processing results demonstrated a substantial improvement in the accuracy of the surface shape measurements obtained by applying the STRMPC method. Compared to the MPC method, the PV value decreased from 1.49 λ PV to 0.99 λ PV, indicating an improvement in the convergence rate of approximately 9.78%. Additionally, the RMS value decreased from 0.257 λ RMS to 0.163 λ RMS, demonstrating a remarkable 35.6% enhancement in the convergence rate. Full article

This paper aims to enhance the lateral path tracking control of autonomous vehicles (AV) in the presence of external disturbances. While AV technology has made significant strides, real-world driving scenarios often pose challenges such as slippery or uneven roads, which can adversely affect the lateral path tracking control and reduce driving safety and efficiency. Conventional control algorithms struggle to address this issue due to their inability to account for unmodeled uncertainties and external disturbances. To tackle this problem, this paper proposes a novel algorithm that combines robust sliding mode control (SMC) and tube model predictive control (MPC). The proposed algorithm leverages the strengths of both MPC and SMC. Specifically, MPC is used to derive the control law for the nominal system to track the desired trajectory. The error system is then employed to minimize the difference between the actual state and the nominal state. Finally, the sliding surface and reaching law of SMC are utilized to derive an auxiliary tube SMC control law, which helps the actual system keep up with the nominal system and achieve robustness. Experimental results demonstrate that the proposed method outperforms conventional tube MPC, linear quadratic regulator (LQR) algorithms, and MPC in terms of robustness and tracking accuracy, especially in the presence of unmodeled uncertainties and external disturbances. Full article

Model predictive control (MPC) heavily relies on the accuracy of the system model. Nevertheless, process models naturally contain random parameters. To derive a reliable solution, it is necessary to design a stochastic MPC. This work studies the chance constrained MPC of systems described by parabolic partial differential equations (PDEs) with random parameters. Inequality constraints on time- and space-dependent state variables are defined in terms of chance constraints. Using a discretization scheme, the resulting high-dimensional chance constrained optimization problem is solved by our recently developed inner–outer approximation which renders the problem computationally amenable. The proposed MPC scheme automatically generates probability tubes significantly simplifying the derivation of feasible solutions. We demonstrate the viability and versatility of the approach through a case study of tumor hyperthermia cancer treatment control, where the randomness arises from the thermal conductivity coefficient characterizing heat flux in human tissue. Full article

This paper aims to perform experimental validation of RF–plasma interaction behaviors for the purposes of wave transmission and reflection. Wave reflection from plasma is of interest as it finds applications in pulse compression and RF polarizer-based systems. Simulations are performed using a combination of Magic3D and COMSOL multiphysics to characterize the plasma–wave interaction and discharge tube properties. The goal is to generate plasma with characteristics that wholly reflect the incident electromagnetic wave. A glass tube of inner diameter 22 mm and length 100 mm, with 12 mm brass electrodes, is fabricated for plasma generation. Argon-based DC glow discharge is sustained at 500 volts at a pressure of 3.8 Torr. Plasma density is calculated to be $2.529\times {10}^{19}$ m${}^{-3}$, with a corresponding plasma frequency of 7.18 GHz. Due to this higher frequency, a 3 GHz incident RF wave is reflected, as measured through S-parameter measurements using a network analyzer. Off and on states of the tube correspond to $S_{11}=-40$ dB and $S_{11}=-13$ dB, which show wave transmission and reflection, respectively. When the plasma column is ignited, the reflected wave has a phase difference of ${180}^{\circ }$. Full article

Microtube-like porous carbon (MPC) and tube-like porous carbon–sulfur (MPC-S) composites were synthesized by carbonizing milkweed pappus with sulfur, and they were used as cathodes for lithium–sulfur batteries. The morphology and uniformity of these materials were characterized using X-ray powder diffraction, Raman spectroscopy, scanning electron microscopy, transmission electron microscopy with an energy-dispersive X-ray analyzer, thermogravimetric analysis, and X-ray photoelectron spectrometry. The electrochemical performance of the MPC-S cathodes was measured using the charge/discharge cycling performance, C rate, and AC impedance. The composite cathodes with 93.8 wt.% sulfur exhibited a stable specific capacity of 743 mAh g⁻¹ after 200 cycles at a 0.5 C. Full article