With the rapid development of autonomous vehicles, heavy-haul freight trains (HhFT), with their capability of transporting large volumes of cargo over long distances, are emerging as promising subjects for research in automatic control. One of the main challenges in operating HhFT lies in ensuring accurate trajectory tracking performance under the influence of adverse factors such as external disturbances, model uncertainties, and actuator-related issues, including actuator faults and input time delays. If not effectively addressed, these factors can significantly degrade the control performance of the system. This paper presents a robust trajectory tracking control system with prescribed performance for HhFTs using multiple electric locomotives. In this framework, a prescribed performance function (PPF) is designed to ensure that the position tracking errors of the locomotives remain within a predefined bound. Simultaneously, an extended state observer (ESO) is employed to estimate the states and the lumped disturbances representing the adverse effects. Based on the outputs of the PPF and the ESO, a robust sliding mode controller (RSMC) is designed. The stability of the closed-loop system is proven through Lyapunov stability theory, showing that all system states converge to a neighborhood of the origin in finite time. Computer simulation results clearly demonstrate the superior control performance of the proposed system compared to previously introduced methods.
The oscillatory motion of floating offshore wind turbines (FOWTs) under erratic sea conditions negatively impacts power generation efficiency, increases structural fatigue loading, and reduces system longevity. While individual blade pitch control can be used to regulate rotor speed and stabilize platform dynamics, it imposes significant mechanical loads on the pitch actuators. To alleviate this burden, mooring line actuation (MLA) offers a promising complementary strategy. This paper investigates the potential and challenges of mooring line actuation for dynamic stabilization of FOWTs. Two platform configurations-a spar-buoy and a tension-leg platform (TLP)-are modeled and validated. For TLP, a tuned mass damper (TMD) strategy is examined as a benchmark for comparative stabilization performance. A comprehensive controllability investigation is conducted to evaluate the effectiveness of MLA in influencing platform degrees-of-freedom (DOFs). Based on these insights, a linear quadratic regulator (LQR) controller is designed to modulate mooring line lengths and associated tensions for active stabilization. Numerical simulations reveal that MLA provides significantly greater stabilization benefits for the TLP configuration compared to the spar-buoy. This underscores the importance of integrated control co-design (CCD) to improve MLA performance, especially for platforms with lower inherent controllability. Across a range of operational scenarios, the proposed MLA strategy demonstrates effective simultaneous surge and pitch suppression with minimal mooring line actuation, offering a viable path toward load-reducing, performance-enhancing control architectures in next-generation FOWTs.
This paper introduces a data-driven sensorimotor control framework for a flapping-wing unmanned aerial vehicle (FWUAV). It integrates an imitation learning algorithm for optimal controls with a deep neural pose estimation scheme. Recognizing that a direct concatenation of the neural pose estimator with the learning-based controller fails, we propose an alternating learning algorithm, namely, ALICE, for the coordinated integration of the two learning schemes. In particular, we enhance the learning capability of the estimator and the controller such that they converge to a synergistic pair. The proposed framework demonstrates excellent stabilizing capabilities compared to alternative ablated strategies or even an end-to-end controller. Furthermore, the presented technique overcomes the common restrictions of existing methods for FWUAV control, particularly the requirement for high-frequency flapping to justify linearization over averaged dynamics.
This work presents a novel geometry for a gerotor hydraulic pump integrated into the rotor of a brushless, direct-current, frameless motor. The design eliminates excess dynamic seals and produces a pump that is more compact, more efficient, higher bandwidth, and capable of greater pressure and flow performance than any servopump previously presented at this scale. Two pumps are presented, with one capable of achieving pressures in excess of 2.9 MPa, flows up to 1.6 L/min, output power levels of 31.5 W with 25% efficiency, and operation at bandwidths up to 37 Hz in a compact form weighing 265 g. Due to its compact size and high output, the proposed design achieved a peak power density of 119 W/kg. Variations of the second produced pressures up to 2500 kPa, flows over 4.5 L/min, hydraulic power of 25 W, and weighed 410 g. The proposed design was quiet in operation, with a maximum sound pressure level at 1 m of 55.8 dBA. Because the proposed design contains no external moving parts, a simple fabric wrap reduced pump noise to a maximum of 47.7 dBA. The pump was evaluated for positional-feedback control of representative systems via integration into two setups: one driving standard rigid hydraulic pistons and one driving a soft robotic actuator. The pump was able to control these setups with positional accuracy on the order of 1 mm and angular accuracy of 2 deg, respectively.
Abstract In this paper, we present a novel formulation involving a coupled ODE–PDE system with memory effects. The system dynamics are modeled using PDEs for heat distribution and ODEs for discrete system states, with an emphasis on incorporating memory terms that influence system behavior over time. A state feedback control law is designed to stabilize the system using the backstepping approach globally. The well-posedness of the closed-loop system is established through rigorous mathematical proofs, ensuring the existence and uniqueness of solutions. Exponential stability is demonstrated using a new Lyapunov function and an analysis of energy decay rates. The article concludes with a numerical example that validates the theoretical findings, showcasing the convergence of system states to equilibrium and confirming the effectiveness of the proposed control strategy.
Abstract This paper presents an event-triggered model predictive control (ET-MPC) strategy for a DC–DC buck converter to reduce computational burden while maintaining high dynamic performance. A four-mode discrete-time model is established to accurately capture the converter switching behavior, and a Kalman filter is integrated into the control framework to estimate load disturbances and improve control accuracy. Unlike conventional time-triggered MPC (TT-MPC), which solves an optimization problem at every time-step, the proposed ET-MPC evaluates the optimal switching sequence only when a voltage deviation exceeds a predefined threshold. This mechanism significantly reduces the number of online optimizations while preserving the regulation of output voltage. Simulation results demonstrate that ET-MPC achieves up to 94% reduction in computational effort with comparable transient response, low steady-state error, and acceptable switching frequency. The proposed controller enables an efficient real-time implementation of model predictive control (MPC) for power converters under varying operating conditions.
Abstract Wing-assisted inclined running (WAIR), an impressive evolutionary capability observed in young Chukar birds, is an attractive maneuver that can be extended to legged aerial systems like Husky, a multimodal aerial-legged robot. This study proposes a control method using a single rigid body (SRB) model with massless legs and thruster forces collocated at the center of mass of the rigid body. A model predictive controller (MPC) is then used to find the optimal ground reaction forces, thruster forces, and moments to track a reference position and velocity trajectory. An analysis on the controllability of the linearized dynamics also shows the effect of thrusters on a legged robot. matlab simulation results of a high-fidelity model on a slope of 40 deg are obtained that highlight the fact that posture manipulation can be used to vector thrust forces on steep slopes. The simulation also provides insight into how the combined efforts of the thrusters and the tractive forces from the legs make WAIR possible in thruster-assisted legged systems.
Abstract Demand response has become an important part of smart home energy management, and an excellent demand response policy can be very effective in reducing energy consumption and comfort violation. However, the uncertainty of renewable energy, the complex interaction between photovoltaic (PV) systems, controllable equipment and the grid, and the uncertain behavior of users bring great challenges to the optimization of smart home energy management. In this paper, we propose an intelligent home energy management framework, dual predictive control-deep deterministic policy gradient method for smart home energy management systems (D2PG-SHEMS). Our method employs an ensemble model combining random forest (RF) and long short-term memory (LSTM) networks, where deep Q-network (DQN) is employed to optimize the ensemble parameters for accurate PV power forecasting. Based on the ensemble model's predictions, the control module utilizes deep deterministic policy gradient (DDPG) combined with imitation learning (IL) to enhance training efficiency for optimal energy management. According to the experimental results over a 72-h testing period, the proposed method reduces energy costs by 69.7% and 34.6% compared to the rule based control (RBC)-based and standard RL methods, respectively, while maintaining the comfort violation level at 35.190.
This paper presents an approach for identifying all the inertial parameters of a solid (mass, position of the center of gravity, and inertia matrix) without repositioning the solid. The identification is based on the use of a hexapod parallel robot capable of six degree-of-freedom (DOF) motions and a 6-component force/torque sensor. The solid to be characterized is placed on the sensor, which is attached to the robot. The robot is used to impose different sinusoidal excitation trajectories in succession. The reaction forces are recorded at the same time, and the inertial parameters are identified by solving the Newton-Euler equations in the frequency domain using the Fourier transform. This solution in the frequency domain allows for precise computation with the following advantages: (i) maximum decoupling of the equations for optimal resolution, individually adapted to each parameter; (ii) simplicity of the experimental setup and the resolution method. Only three elements are required (the solid to be evaluated, the force/torque sensor, and the robot). Data processing is not overly complex and does not require overly restrictive synchronization of the clocks of the different systems; (iii) fine adjustment of the force/torque sensor (offset calibration) is not necessary.
Within the underwater exploration and patrolling domain, a novel approach based on the max-plus algebra framework is presented to model and control the coordinated and synchronized behavior of a shoal of three fish robots. These robots aim to cyclically survey a submerged area with many points of interest (POI) through predefined paths. The work leads to obtaining a max-plus linear system, which represents the behavior of such robots, followed by the formalization of a “System Synchronization Problem (SSP)” for such a system, to check the mission's feasibility in the desired time. The theoretical basis for the SSP is revised, and the results confirm that robots can adhere to the defined model through a control law derived as a solution of the SSP. Consequently, it becomes achievable to synchronize the system with the model. Calculations to find the solution of the SSP are presented and performed using the ScicosLab software. Finally, a numerical example is provided to illustrate the resolution process and the solution of the SSP.
Hydraulic piston accumulators are widely used for energy storage and shock absorption in a broad range of industrial applications. While simplified models are often sufficient for general dimensioning, detailed design, energy optimization, and condition monitoring require more accurate representations of thermal and gas dynamics. This paper presents an extended thermal model of a steel-type hydraulic piston accumulator, in which the accumulator is segmented longitudinally to better capture spatial temperature variations. The model is validated experimentally using surface-mounted thermocouples and compared with the widely used benchmark model based on a single thermal time constant. Five operating scenarios are evaluated, including both natural and forced convection conditions. The extended model consistently demonstrates improved accuracy in estimating piston position, with steady-state errors reduced by up to 93% compared to the benchmark. Sensitivity analyses further investigate the influence of thermal parameters, indicating the robustness of the extended model across varied conditions. The proposed model offers a more reliable framework for simulation, monitoring, and control of hydraulic accumulators in practical applications.
This paper presents an optimal trajectory planning and tracking control framework for a tilt-rotor Vertical Take-Off and Landing (VTOL) aircraft during longitudinal transition flight. Specifically, the Multiple Shooting Method (MSM) is employed to generate a flight trajectory that consists of takeoff, transition, and level flight. Unlike prior works, MSM yields a dynamic flight trajectory rather than a quasi-equilibrium trajectory. After that, a Linear Parameter-Varying (LPV) Model Predictive Control (MPC) scheme is developed to track the dynamic trajectory. The linear parameter-varying-model predictive control (LPV-MPC) scheme efficiently accounts for varying nonlinearities by previewing the scheduling parameters (velocity, pitch rate, and rotor tilting angle) along the dynamic flight trajectory. The LPV-MPC formulates a convex optimization problem that minimizes the weighted tracking error and control inputs while satisfying constraints of states and control inputs. The proofs of stability and recursive feasibility are also presented. The tracking control is evaluated in simulation scenarios of initial state error and measurement noises. Furthermore, the LPV-MPC is compared with nonlinear MPC and further validated in hardware-in-the-loop (HIL) experiment with excellent tracking performance and computational efficiency for real-time implementation.
This study proposes a novel methodology for efficient multi-unmanned aerial vehicle (UAV) coverage path planning (CPP) tailored to solar panel cleaning applications. The approach incorporates rectilinear path planning and vehicle routing problem (VRP) strategies, enabling optimized and coordinated multi-UAV operations for large-scale solar farms. By systematically dividing the target area into rows and assigning optimized routes to UAVs, the methodology minimizes mission time and maximizes cleaning efficiency. The proposed CPP approach integrates realistic constraints such as UAV battery life, flight time, and operator limitations, ensuring practicality and robustness. Comprehensive evaluations, including simulations using the Gazebo platform and real-world tests with UAVs, validate the effectiveness of the methodology. The results demonstrate significant improvements in path optimization, energy efficiency, and area coverage, confirming its applicability to solar panel maintenance and other large-scale coverage tasks.
System modeling frameworks can be categorized into imperative and declarative paradigms. A model's paradigm effects its efficacy: imperative models allow simple execution, while declarative models capture the behavior of the underlying system. This paper compares these paradigms, as well as functional and object-oriented frameworks, in light of physics-based systems. This is done by exploring the principles of systems modeling and simulation. Simulation is shown to be the composition of functions representing system behavior. Simulatable frameworks can be differentiated by their ability to identify and compose these functions for a specific input and output pairing. The various frameworks are explored, applying concepts more typically studied in computer science to general systems engineering. The frameworks are investigated by comparing simulations of a driven double pendulum in various modeling languages. Observations include that functional, declarative models allow for greater reusability and holistic system simulation.
This study addresses the effects of uneven temperature distribution on the performance of photovoltaic (PV) modules in series, parallel, series-parallel, and parallel-series electrical configurations. This comprehensive and extensive work presents a new mathematical approach that models thermal gradients caused on by environmental or structural factors. In comparison to pure series or parallel connections, the results indicate that hybrid configurations, in particular, series-parallel and parallel-series, show a higher tolerance to temperature mismatches. In large-scale PV installations, where perfect thermal uniformity is uncommon, the results emphasize the significance of thermal management and configuration selection in reducing power loss from localized heating. Experimenatl validation is carried out showing a good agrremeent with the current simulation model.
This study addresses the challenge of stabilizing cooling seawater temperature and liquid level in a marine intercooled gas turbine seawater heat exchanger by proposing a multivariable decoupling control strategy. A mathematical model of the constant-temperature mixing (CTM) system is developed, and pseudo-control variables are introduced to decouple the strongly correlated multi-input multi-output (MIMO) loops. Incremental proportional-integral-derivative (PID) control algorithms are designed for regulating seawater inlet temperature and mixing tank level. Experimental validation on a marine intercooled gas turbine test platform demonstrates that the proposed controller achieves effective decoupling, with temperature deviations confined within +/- 1.0 degrees C and liquid level fluctuations below +/- 0.1 m under varying operational conditions. The results highlight the controller's robustness and practical applicability in complex marine environments.
This paper presents a control-oriented dynamic model, controller, and closed-loop mobility characterization for the first wind-powered spherical rover capable of net upwind motion. This device, termed the Spherical Sailing Omnidirectional Rover (SSailOR), incorporates design features within a spherical, terrestrial rover that mimic the role that a centerboard (or keel) and lifting sails play in allowing net upwind motion for sailboats. Specifically, a traction hoop enables significant lateral resistance, thereby providing a nonholonomic constraint in the direction of travel. Lifting sails enables net thrust even when traveling significantly upwind, while also providing heading control. While providing unique capabilities, the SSailOR gives rise to a complex design and control space, where careful model-based design and control are necessary to ensure that the SSailOR can simultaneously (i) make net upwind progress, (ii) respond quickly to wind speed/direction changes, (iii) limit heel angle, and (iv) control its heading. To simultaneously address these challenges, we first present a control-oriented dynamic model. This is followed by the presentation of a combined heading and heel angle controller. Finally, with the dynamic model and control structure in place, we present a detailed closed-loop Pareto analysis, which illustrates the tradeoff between transient and steady-state performance, along with the design features that favor one modality of performance over another.
Model predictive control (MPC) plays a vital role in maintaining frequency stability in marine microgrids, particularly as renewable energy sources (RESs) are increasingly integrated into maritime power systems. To address the challenges of variable generation and fluctuating loads, this study proposes a hybrid optimization framework that combines a genetic algorithm (GA) with Gorilla troop optimizer (GTO). The hybrid approach enhances MPC performance by improving reliability and efficiency in real-time frequency regulation. Developed in the matlab/simulink environment, the proposed GA-GTO-based MPC demonstrates improved computational efficiency and higher accuracy in frequency prediction. Simulation results indicate that the optimized controller reduces frequency oscillations from 1.2 Hz (proportional-integral-derivative (PID)) and 0.75 Hz (standard MPC) to 0.2 Hz, while also lowering response latency from 5 s to 2 s. These improvements highlight the potential of hybrid optimization techniques to advance control strategies for marine microgrids, ensuring stable operation in renewable energy-dominated environments. Future work will focus on adaptive real-time optimization using machine learning and scalability analysis for larger marine power systems.
Large upright heterogeneous flexible cylinders need to be transported by huge cranes in some safety-critical industrial applications. Unfortunately, the coupling effects between swing and twisting of the cable-suspended load, as well as bending and torsion of the flexible cylinder, degrade efficiency and safety of the material transportation. Although many publications have been directed at understanding compound-pendulum dynamics and beam vibrations, little attention has been focused on the coupled dynamics between load swing and twisting, as well as bending and torsion of cylindrical payloads. A three-dimensional model of a bridge crane moving an upright heterogeneous flexible cylinder has been developed to study such dynamic coupling. Furthermore, a control method has been developed to attenuate vibrations induced by the complex coupling effects. Several experiments have been performed to verify the effectiveness of the proposed dynamic model and control methods.
This paper proposes an innovative reinforcement learning (RL)-based autonomous braking algorithm that can be personalizable for optimal one-pedal driving (OPD) of electric vehicles. To address the shortcomings of OPD-including its counterintuitive braking, which confuses drivers, causes fatigue and discomfort, and promotes a lack of conformity/trust-we propose a framework that integrates the state-of-the-art Twin Delayed Deep Deterministic Policy Gradient (TD3) RL agent with Learning from Human Demonstrations (LfD) via behavior cloning. An infusion term, lambda, controls the influence of human demonstrations on policy shaping, allowing varying levels of personalization. For the RL agent, a comprehensive reward function is designed to balance precise braking, human comfort, and regenerative braking energy. Seven unique agents with different lambda values are meticulously trained and evaluated against a baseline ( lambda=0) and a human-like (HL) algorithm in a full-braking scenario. The results show that incorporating a moderate value of human demonstration ( lambda=0.3) results in a more personalized and optimal control policy. Compared to the baseline ( lambda=0), the proposed agent achieves an improvement of 212% in precise braking and 0.3% in energy recovery, and a reduction of 24% in root-mean-square (RMS) jerk and 10% in human-like action dissimilarity. In comparison to the HL algorithm, the proposed agent shows an improvement of 0.4% in energy recovery and a reduction of 22% and 10% in RMS acceleration and jerk, respectively.
