The tradeoff between model fidelity and computational cost remains a central challenge in the computational modeling of extrusion-based 3D printing, particularly for real-time optimization and control. Although high-fidelity simulations have advanced considerably for offline analysis, dynamical modeling tailored for online, control-oriented applications is still significantly underdeveloped. In this study, we propose a reduced-order dynamical flow model that captures the transient behavior of extrusion-based 3D printing. The model is grounded in physics-based principles derived from the Navier-Stokes equations and further simplified through spatial averaging and input-dependent parameterization. To assess its performance, the model is identified via a nonlinear least-squares approach using computational fluid dynamics (CFD) simulation data spanning a range of printing conditions and subsequently validated across multiple combinations of training and testing scenarios. The results demonstrate strong agreement with the CFD data within the nozzle, the nozzle-substrate gap, and the deposited-layer regions. Overall, the proposed reduced-order model successfully captures the dominant flow dynamics of the process while maintaining a level of simplicity compatible with real-time control and optimization.
This paper presents a novel application of control barrier functions (CBFs) for estimating the state of power (SOP) during charging and discharging cycles of lithium-ion batteries. We define SOP as the maximum amount of power that can be maintained over a specified time period. The proposed algorithm predicts the maximum achievable power level within a constraint set defining the operational boundaries of the cell, namely, state of charge (SOC), voltage, and core temperature. To demonstrate the efficacy of this approach, we simulate battery performance under the urban dynamometer driving schedule (UDDS), a representative profile of city driving conditions. Comparisons with (i) model predictive control (MPC) and (ii) a conventional bisection-based approach illustrate the merits of the CBF-based method in terms of practicality for real-world automotive applications. The aim of this study is to advance efforts to create safer and more effective battery management solutions for electric vehicles and energy storage technologies.
Helicopter aerial refueling is a particularly challenging maneuver because of the complex aerodynamic interaction between the helicopter, the hose-drogue, and the tanker. To address this, a control design and analysis framework for autonomous helicopter aerial refueling is presented here. The helicopter control architecture is based on standard inner and outer-loop cascaded dynamic inversion. The outer-loop dynamic-inversion-based control is augmented by a reinforcement learning (RL) controller that corrects the outer-loop commands to account for the unpredictable drogue motion. This RL corrective input and inner-loop tracking error result in imperfect dynamic inversion in the outer-loop, leading to a nonlinear residual term in the outer-loop dynamics. Hence, we derive analytical stability and performance bounds of the proposed controller in the presence of bounded drogue uncertainty, RL control actions, and imperfect inner-loop tracking. We then use these analytical expressions to design the model-based controller. Simulations in a high-fidelity environment with full-scale helicopter and drogue models validate the proposed method. These simulation results show that the proposed control strategy reduces the mean docking error from 0.26 m with the pure model-based controller to 0.08 m, demonstrating an improvement of 69% in docking error. Furthermore, the controller is shown to have a docking success rate of 88%, while adding additional disturbances from atmospheric turbulence, wind, and state uncertainty reduces the docking success rate to 70%.
This study presents the design of a generalized adaptive variable-gain sliding mode control policy that achieves finite-time convergence of a perturbed first-order sliding mode in the presence of input saturation constraints. An important feature of the proposed scheme is the fractional power nature of the integral term which mitigates the chattering effect and reduces steady-state error associated with the discontinuous integral term of the conventional super-twisting control policies considered in previous studies. Moreover, in contrast with most existing schemes, the perturbation and its derivative are assumed to be bounded by unknown constants in this study, and an adaptive gain update mechanism is incorporated within the proposed scheme to realize global finite-time convergence of the sliding variable to a uniform predefined bound around the origin. In particular, Lyapunov stability analysis is used to demonstrate that the proposed scheme ensures convergence of the sliding variable to a uniform ultimate bound that can be made arbitrarily small through continuous and saturated control action. This strategy also enables the proposed scheme to mitigate the integral windup effect that most existing architectures suffer from when deployed under input saturation constraints. Extensive experimental tests for achieving precise position and speed control of a DC motor are used to illustrate its advantages compared to leading alternative designs.
The multiredundant degrees-of-freedom (DoF) in snake robots present substantial challenges in attaining oriented movement toward target directions. Under the coupled influence of various factors, these systems progressively deviate from desired trajectories, thereby diminishing locomotion precision. This paper systematically investigates head orientation strategies and their effects on orientation control and stability during the snake robot's serpentine locomotion. First, through biomechanical analysis and mechanistic simplification of biological snakes' orientation mechanisms, a dynamic model of the snake robot and a central pattern generator (CPG) control network based on Hopf oscillators are established. Building on this foundation, we implement head orientation algorithms under different strategies and propose a multimetric evaluation framework. The methodology comprises three steps: (1) formulation of distinct head orientation strategies considering the characteristics of signal continuity and joint angular patterns; (2) definition of state variables for serpentine locomotion and corresponding orientation evaluation metrics; (3) parametric investigation of control parameters' impacts on head orientation accuracy through the proposed framework. Finally, comprehensive simulations and prototype experiments demonstrate the approach's effectiveness. This work provides theoretical and practical guidance for achieving precise orientation control of snake robots in complex operational scenarios.
This study presents a novel automated fluid resuscitation framework designed to maintain hemodynamic stability in the presence of limited and noisy physiological data. We propose a robust nonlinear state-space modeling (RNSSM) algorithm, trained via variational auto-encoder learning, to capture mean arterial pressure (MAP) responses to fluid infusion in hemorrhagic scenarios. The model is integrated with a radial basis function (RBF) optimal control approach that combines function approximation and predictive optimization to regulate fluid infusion dosages during resuscitation. The accuracy of the RNSSM was confirmed using real-world data. Additionally, the superior performance of the RBF optimal controller in fluid dose adjustment was demonstrated in comparison with state-of-the-art fluid resuscitation control algorithms. Simulation results indicate that this approach addresses key limitations of existing methods by enabling more accurate, subject-specific hemodynamic regulation for fluid management in critical care.
Achieving large deformations in dielectric elastomers without dielectric breakdown remains a challenge that limits their technological implementation. This work analyzes the performance of proportional-integral-derivative (PID) feedback control for driving voltage-induced deformations in circular membrane actuators. The dynamic model includes hyperelastic material behavior, strain stiffening at large stretches, electro-elastic coupling, inertial nonlinearities, and a PID control law. When driven by open-loop voltages without feedback, the membrane has one equilibrium at low and high voltages. Three equilibria (corresponding to small, intermediate, and large deformations) are possible at moderate voltages. The use of PID feedback control effectively produces small-stretch equilibria at low and moderate voltages. PID control can generate large stretches at moderate applied voltages, although these large stretches are more difficult to control. Interestingly, the use of proportional control only (without integral and derivative gains) generally results in the membrane reaching intermediate stretches when large-stretch commands are given. These intermediate stretches, which are statically unstable, are stabilized by the controller. Precise tuning of the PID controller gains can produce large-stretch equilibria. Divergence and flutter instability occur for larger controller gains. For small-stretch commands, the through-thickness electric fields remain far below breakdown fields, even though time-dependent voltages cause dynamic overshoot in the membrane. Avoiding dielectric breakdown for large-stretch commands requires more careful tuning of the controller gains. PID feedback control may permit dielectric elastomers to achieve large deformations in soft transducer applications.
Thermal control is critical for space telescopes, as thermo-elastic deformation can significantly degrade imaging performance. This imposes stringent specifications on temperature range, thermal gradient, and temporal temperature stability. While most frameworks focus on offline thermal optimization, online control approaches include PID controllers and predictive control using compact thermal models. However, PID-based methods cannot naturally incorporate temperature constraints or thermal gradient specifications, while compact-models fail to capture spatial variations. Although standard model predictive control (MPC) using detailed thermal models can address these limitations, solving the resulting constrained optimization problem in real-time is computationally prohibitive. This work presents the first application of MPC for thermal control of a simulated Cassegrain telescope located in a 500 km sun-synchronous orbit. We employ a reduced-order model constructed via proper orthogonal decomposition that enables real-time MPC implementation. Thermal gradient constraint (1 degrees C maximum difference across the telescope structure) and stability constraint (2 degrees C maximum variation during 800 s imaging sessions) are directly encoded into the MPC formulation. Simulation results demonstrate that this reduced-order MPC (ROMPC) approach achieves 38% improvement in temperature stability over PID control and 24% over bang-bang control during stabilized operation. For critical optics components, ROMPC reduces thermal gradients compared to PID and bang-bang control. Furthermore, transient analysis reveals that ROMPC improves temporal temperature stability by avoiding the large overshoots (+/- 6 degrees C), a key limitation of PID control. Thus, ROMPC provides superior thermal performance than traditional controllers by leveraging global spatiotemporal awareness.
State-of-the-art soft manipulators are unable to vary stiffness continuously, are complex to manufacture, and have limited control bandwidth. In this work, we present a novel planar bidirectional pneumatically actuated soft manipulator that advances all three limitations-the stiffness is continuously tunable by a factor of 1.6, the actuator is manufactured from a single 3D print, and we are able to demonstrate free-space tracking of a time-varying signal. We are also able to modify the impedance of the actuator during a collision using a dynamic model-based feed-forward and feedback controller and demonstrate an increase in virtual stiffness by a factor of 4. The incorporation of feedback resulted in 0.7 mm tip position root-mean-square error (RMSE) (a reduction of 47% over the purely feed-forward controller) while tracking a 5 mm amplitude sinusoid up to 0.75 Hz across the entire stiffness range.
Thermochemical energy storage systems offer a sustainable solution for storing excess renewable energy. This work focuses on a particular application based on CaO/Ca(OH)2 for heat storage. To ensure safe and efficient hydration of CaO (storage discharging), a control strategy is required. A nonlinear controller design model is developed and refined, incorporating input delays and pump dynamics in the cooling water circuit. With experimental data, the parameters and input delays in the system model are identified. A model predictive control (MPC) strategy is then designed, with four objectives targeting reactor temperature, cooling water temperature, and thermal output power. A proportional-integral-derivative (PID) controller is implemented for comparison. The control strategies are evaluated in terms of tracking performance, energy transfer, and real-time feasibility. Simulation results show that MPC effectively tracks system temperatures and power while fulfilling input, output, and state constraints. The results also confirm the real-time feasibility of the MPC approaches.
This paper presents a new observer-based output feedback boundary tracking control strategy to improve the tracking performance in an uncertain parabolic partial differential equation (PDE) with external disturbances. First, using the measurable signal of the distributed parameter system (DPS), a state observer is designed to estimate unknown disturbances and distributed states. Then, a disturbance-compensation-based output-feedback boundary control law is derived from the estimated values to realize the high-precision tracking performance for exogenous reference signal and effective compensation of external disturbances. Some lemmas and theorems are given to prove that the closed-loop system is exponentially stable. Finally, some numerical simulations demonstrate the effectiveness of the proposed method.
This work demonstrates vibration suppression of a single-link flexible manipulator (SLFM) and a two-link flexible manipulator (TLFM) using input shaping. In this, inputs include pulse width modulation (PWM) duty cycle and desired angular position. The shaping of these parameters is done based on the damping ratio of the system. Vibrations due to flexibility in flexible manipulators are suppressed for better end-effector accuracy. Extra damping for the first link is achieved using a belt, and a nonrotating second link is connected to the first link to get modified single flexible link. The modified system requires fewer iterations for vibration suppression. Results are compared with a nonlinear dynamics model developed using the assumed mode method (AMM) and decoupled natural orthogonal complement (DeNOC) matrices. A simple proportional-derivative (PD) controller manages the angular position in simulations. The process of input shaping is explained in detail, showing angular position and tip deflection variations with PWM duty cycle and time. The effect of input shaping on vibration suppression is shown for a two-link flexible manipulator, i.e., when both the links are rotated.
There is a current trend toward the electrification of mobile machines that have traditionally been dominated by diesel engine-driven hydraulics, necessitating hydraulic pumps that are driven by electric motors. The benefits of power density are possibly by integrating an electric motor and hydraulic pump inside a single casing. In comparison to coupling a separate electric motor and pump, the integrated machine eliminates a set of bearings and a shaft seal. Additionally, the leakage from the hydraulic pump can be used as a coolant for the electrical machine, improving power density. In this paper, a hydrostatic radial piston pump is proposed to integrate with an axial flux permanent magnet (PM) machine. This pump uses spherical head pistons that can tilt while reciprocating inside the cylinders, eliminating the need for joints at the slippers. To reduce the frictional loss between the slipper pad and the cam at high speeds, the cam freely rotates. In the earlier work, a detailed model of the pump was developed, including the losses, and the pump performance was predicted for an integrated machine. This work focuses on the standalone pump prototype test, which required a separate driver and shaft sealing, unlike the integrated machine. The pump mathematical model was therefore modified to account for the shaft seal losses and experimental churning losses. With these modifications, the standalone pump performance was predicted from the mathematical model and then compared with experimental results for an actual pump prototype.
Cyber-physical power systems have seen a considerable rise in malicious false data injection (FDI) attacks over the last decade. Metering infrastructure is most vulnerable to such attacks as they are spread out over a large topological area, and their location often is accessible to consumers/generators. We consider such a scenario in our study where low magnitude stealthy FDI attacks are injected at different buses of an IEEE 14-bus, 30-bus, and 118-bus systems. We propose a novel reduced sparse transformer (RST) neural network to detect the presence of FDI attack at multiple buses. The proposed RST uses a time series input of past measurements of active power at each bus received from the metering units and uses an encoder-only architecture to predict the presence of an attack at selected buses. We compare results with a baseline softmax or vanilla transformer neural network (TNN), sparsemax attention-based TNN, convolutional neural network long short-term memory (CNN-LSTM), and bidirectional LSTM (bi-LSTM) networks which are state-of-the art recurrent architectures for time series, text, and natural language prediction. The proposed RST architecture shows significant improvement in classification metrics for multi-label and single label cases for each of the attacked bus locations. The GitHub repository can be found here: GitHub Repository.
Laser powder bed fusion (LPBF) is a metal additive manufacturing process that uses a high-power laser to melt a predefined shape in a bed of metal powder, layer by layer. The size of the melted pool throughout the process can significantly affect the mechanical properties of the final part; too small of a melt pool may result in poor fusion, too large will cause porosity. The size of the melt pool is governed by inherently complex multiphysical interactions. Complex models have been developed and simplified in the literature, and in this paper, a nonlinear first-order single state energy transfer model is used to simulate the size of the melt pool transverse surface area. The error is defined as the difference between the melt pool area and a desirable reference value, and a sliding mode control (SMC) law is developed to use input laser power to drive the system to a zero-error manifold in finite time. Since the model used takes advantage of potentially unrealistic geometrical assumptions about the melt-pool shape, the control law is further developed to be robust to inaccuracies and real-time changes in the system parameters related to this assumption. The performance of the controller is compared with other control strategies in the presence of bounded parameter uncertainty.
Cooperative rendezvous of a fixed-wing unmanned aerial vehicle (UAV) and a moving platform is a challenging and significant issue, where environmental disturbances, computational load, and time-varying rendezvous points remain the prevalent challenges. Aiming at the stabilizing control of the fixed-wing UAV under the disturbed environment, a Gaussian process (GP)-based robust model predictive control with Laguerre functions and shrinking horizon strategy is proposed. Firstly, a new online Gaussian process prediction method is developed to predict the future trajectory of the moving platform. Then, a robust control scheme is designed to compensate for the effect of bounded disturbances on the UAV. Furthermore, to decrease the computational load of solving the optimization problem in real-time, a novel prediction horizon update strategy and Laguerre functions are developed. Finally, the reliability and effectiveness of the proposed algorithm are verified by the joint experiment results. Compared with the existing approaches, the proposed method achieves a 30.33% reduction in computational load.
Robot motion control is challenging due to the ever-demanding requirements on precision and agility under highly nonlinear dynamics, large uncertainties, and unknown disturbances. Sliding mode control (SMC) is well-known for its good robustness, but suffers from chattering. Thus, many chattering-reduced SMCs were explored at the price of compromised tracking performance. However, they are either not accurate enough or too complicated. To enhance the tracking performance of robot manipulators, this paper proposes a modified SMC that establishes the sliding surface in the joint state space of the plant and the controller to filter the switching term and achieve high tracking accuracy. Furthermore, by analyzing the equivalent systems in the sliding mode of various SMCs, we point out that dynamic SMCs contain a feedback loop around the input gain matrix uncertainty in the sliding mode. Therefore, closed-loop stability of the whole system requires not only convergence of the sliding variable but also stability of the equivalent system in the sliding mode. Then experimental comparisons among the first-order SMC with sliding layers (FOSMC), two types of second-order SMC (SOSMC), and the proposed SMC on a six-axis industrial robot show that the proposed one effectively reduces chattering and outperforms the others.
Integrated power systems (IPS) aboard electrified ships require energy management strategies that ensure safe, autonomous operation. Next-generation platforms are expected to make such decisions with minimal human oversight. However, the complex, multidomain, multitimescale dynamics of IPS-combined with high ramp rate loads like electronic warfare systems-pose significant challenges. Additionally, these systems often face uncertain, time-varying, mission-specific constraints that create nonconvex feasible regions, limiting the effectiveness of conventional energy management approaches. This work presents a hierarchical, two-stage framework for safe and adaptive energy management in shipboard IPS. At the upper level, a sampling-based rapidly exploring random tree (RRT) algorithm identifies feasible long-term power and energy trajectories within nonconvex constraint spaces. At the lower level, a robust model predictive control (MPC) scheme ensures accurate trajectory tracking with bounded error, accommodating the dynamics of major components while maintaining constraint satisfaction. The framework is demonstrated on a two-zone IPS model with a high ramp rate load. Simulation results show the proposed planner efficiently generates feasible mission plans that adapt to evolving constraints, while the MPC controller ensures reliable tracking and constraint adherence. By bridging long-term planning with short-term control, this architecture enables safe, flexible, and autonomous operation of complex shipboard power systems. It addresses key limitations of existing strategies in managing nonconvex constraints and dynamic mission contexts, making it well-suited for resilient autonomy in future maritime platforms.
An upgraded tuned liquid column damper (UTLCD), combining a tuned liquid column damper (TLCD) with an undamped tuned mass damper (TMD), has recently been introduced and shown to be more efficient than a traditional TMD or TLCD. This study introduces an upgraded version of the UTLCD, where a UTLCD is connected to the ground through an inerter (UTLCDI), to passively control the vibration of offshore platforms. Unlike the earlier inerter-based TLCD models, the UTLCDI is simpler because it connects to the main structure with a spring. In this work, an analytical model of the UTLCDI-platform system is developed, in which a key improvement is that the mass ratio between the undamped TMD and TLCD is treated as a design variable rather than being fixed as done in previous works. This change offers more options in finding the optimal UTLCDI configuration. Then, optimal configurations of UTLCDI for different inertance ratios are determined. The obtained results show that an optimized UTLCDI is more effective and robust than an optimal UTLCD with the same weight. Specifically, the optimal configuration of UTLCDI, characterized by a higher inertance ratio, offers greater control effectiveness. Moreover, the maximum displacements of both the TMD mass and the liquid column in an optimized UTLCDI configuration are much smaller than those observed in the UTLCD. Additionally, with the same inertance ratio given, the UTLCDI provides better control performance compared to the inerter-based TMD (TMDI). However, it is less robust than the TMDI when the natural frequency of the structure changes.
In recent years, advances in sports medicine have significantly improved rehabilitation strategies for exercise-induced injuries. Among them, robot-assisted rehabilitation systems have emerged as an effective approach for knee joint recovery due to their precise and controllable training capabilities. Knee exoskeletons equipped with series elastic actuators (SEAs) improve the safety of human-robot interaction and reduce the risk of joint injury by using compliant elements to absorb unexpected external impacts. However, the integration of SEAs introduces several control challenges, including modeling uncertainties, friction, and external disturbances, which degrade model accuracy and control performance. To cope with these unknown nonlinearities, this paper employs a radial basis function neural network for real-time approximation. In addition, a prescribed-time Lyapunov-based stability criterion is incorporated to guarantee system convergence within a prescribed time. To reduce redundant data transmission and communication burden caused by frequent control updates, a dynamic event-triggered mechanism (DETM) is developed, significantly lowering the control update frequency. Rigorous Lyapunov-based analysis confirms that all signals in the closed-loop system remain bounded and achieve uniform convergence within the prescribed time. Simulation results further demonstrate the effectiveness of the proposed control scheme.
