pwc-archive/papers-with-abstracts huggingface dataset:
ReLU Surrogates in Mixed-Integer MPC for Irrigation Scheduling
Authors: Bernard T. Agyeman, Jinfeng Liu, Sirish L. Shah
Date: 2024-09-18 00:00:00
Tasks: Computational Efficiency, global-optimization, Model Predictive Control, Scheduling

Efficient water management in agriculture is important for mitigating the growing freshwater scarcity crisis. Mixed-integer Model Predictive Control (MPC) has emerged as an effective approach for addressing the complex scheduling problems in agricultural irrigation. However, the computational complexity of mixed-integer MPC still poses a significant challenge, particularly in large-scale applications. This study proposes an approach to enhance the computational efficiency of mixed-integer MPC-based irrigation schedulers by employing ReLU surrogate models to describe the soil moisture dynamics of the agricultural field. By leveraging the mixed-integer linear representation of the ReLU operator, the proposed approach transforms the mixed-integer MPC-based scheduler with a quadratic cost function into a mixed-integer quadratic program, which is the simplest class of mixed-integer nonlinear programming problems that can be efficiently solved using global optimization solvers. The effectiveness of this approach is demonstrated through comparative studies conducted on a large-scale agricultural field across two growing seasons, involving other machine learning surrogate models, specifically Long Short-Term Memory (LSTM) networks, and the widely used triggered irrigation scheduling method. The ReLU-based approach significantly reduces solution times -- by up to 99.5\% -- while achieving comparable performance to the LSTM approach in terms of water savings and Irrigation Water Use Efficiency (IWUE). Moreover, the ReLU-based approach maintains enhanced performance in terms of total prescribed irrigation and IWUE compared to the widely-used triggered irrigation scheduling method.
Data-Efficient Quadratic Q-Learning Using LMIs
Authors: J. S. van Hulst, W. P. M. H. Heemels, D. J. Antunes
Date: 2024-09-18 00:00:00
Tasks: Q-Learning, Reinforcement Learning (RL)

Reinforcement learning (RL) has seen significant research and application results but often requires large amounts of training data. This paper proposes two data-efficient off-policy RL methods that use parametrized Q-learning. In these methods, the Q-function is chosen to be linear in the parameters and quadratic in selected basis functions in the state and control deviations from a base policy. A cost penalizing the $\ell_1$-norm of Bellman errors is minimized. We propose two methods: Linear Matrix Inequality Q-Learning (LMI-QL) and its iterative variant (LMI-QLi), which solve the resulting episodic optimization problem through convex optimization. LMI-QL relies on a convex relaxation that yields a semidefinite programming (SDP) problem with linear matrix inequalities (LMIs). LMI-QLi entails solving sequential iterations of an SDP problem. Both methods combine convex optimization with direct Q-function learning, significantly improving learning speed. A numerical case study demonstrates their advantages over existing parametrized Q-learning methods.
On the Stability of Consensus Control under Rotational Ambiguities
Authors: Zhonggang Li, Changheng Li, Raj Thilak Rajan
Date: 2024-09-18 00:00:00

Consensus control of multiagent systems arises in various robotic applications such as rendezvous and formation control. For example, to compute the control inputs of individual agents, the difference in the positions in aligned coordinate frames i.e., the pairwise displacements are typically measured. However, the local coordinate frames might be subject to rotational ambiguities, such as a rotation or a reflection, particularly if the positions of the agent are not directly observed but reconstructed from e.g. pairwise Euclidean distances. This rotational ambiguity causes stability issues in practice, as agents have rotated perceptions of the environment. In this work, we conduct a thorough analysis of the stability in the presence of rotational ambiguities in several scenarios including e.g., proper and improper rotation, and the homogeneity of rotations. We give stability criteria and stability margin on the rotations, which are numerically verified with two traditional examples of consensus control.
System-Level Efficient Performance of EMLA-Driven Heavy-Duty Manipulators via Bilevel Optimization Framework with a Leader--Follower Scenario
Authors: Mohammad Bahari, Alvaro Paz, Mehdi Heydari Shahna, Jouni Mattila
Date: 2024-09-18 00:00:00
Tasks: Bilevel Optimization

The global push for sustainability and energy efficiency is driving significant advancements across various industries, including the development of electrified solutions for heavy-duty mobile manipulators (HDMMs). Electromechanical linear actuators (EMLAs), powered by permanent magnet synchronous motors, present an all-electric alternative to traditional internal combustion engine (ICE)-powered hydraulic actuators, offering a promising path toward an eco-friendly future for HDMMs. However, the limited operational range of electrified HDMMs, closely tied to battery capacity, highlights the need to fully exploit the potential of EMLAs that driving the manipulators. This goal is contingent upon a deep understanding of the harmonious interplay between EMLA mechanisms and the dynamic behavior of heavy-duty manipulators. To this end, this paper introduces a bilevel multi-objective optimization framework, conceptualizing the EMLA-actuated manipulator of an electrified HDMM as a leader--follower scenario. At the leader level, the optimization algorithm maximizes EMLA efficiency by considering electrical and mechanical constraints, while the follower level optimizes manipulator motion through a trajectory reference generator that adheres to manipulator limits. This optimization approach ensures that the system operates with a synergistic trade-off between the most efficient operating region of the actuation system, achieving a total efficiency of 70.3\%, and high manipulator performance. Furthermore, to complement this framework and ensure precise tracking of the generated optimal trajectories, a robust, adaptive, subsystem-based control strategy is developed with accurate control and exponential stability. The proposed methodologies are validated on a three-degrees-of-freedom manipulator, demonstrating significant efficiency improvements while maintaining high-performance operation.
Model-Free Generic Robust Control for Servo-Driven Actuation Mechanisms with Layered Insight into Energy Conversions
Authors: Mehdi Heydari Shahna, Jouni Mattila
Date: 2024-09-18 00:00:00

To advance theoretical solutions and address limitations in modeling complex servo-driven actuation systems experiencing high non-linearity and load disturbances, this paper aims to design a practical model-free generic robust control (GRC) framework for these mechanisms. This framework is intended to be applicable across all actuator systems encompassing electrical, hydraulic, or pneumatic servomechanisms, while also functioning within complex interactions among dynamic components and adhering to control input constraints. In this respect, the state-space model of actuator systems is decomposed into smaller subsystems that incorporate the first principle equation of actuator motion dynamics and interactive energy conversion equations. This decomposition operates under the assumption that the comprehensive model of the servo-driven actuator system and energy conversion, uncertainties, load disturbances, and their bounds are unknown. Then, the GRC employs subsystem-based adaptive control strategies for each state-variant subsystem separately. Despite control input constraints and the unknown interactive system model, the GRC-applied actuator mechanism ensures uniform exponential stability and robustness in tracking desired motions. It features straightforward implementation, experimentally evaluated by applying it to two industrial applications.
Robust Sensor-Limited Control with Safe Input-Output Constraints for Hydraulic In-Wheel Motor Drive Mobility Systems
Authors: Mehdi Heydari Shahna, Pauli Mustalahti, Jouni Mattila
Date: 2024-09-18 00:00:00

In-wheel drive (IWD) systems enhance the responsiveness, traction, and maintenance efficiency of vehicles by enabling each wheel to operate independently. This paper proposes a novel robust torque-observed valve-based control (RTOVC) framework to address velocity tracking in hydraulic IWDs that actuate heavy-duty wheeled mobile robots (HWMRs), considering such challenges as wheel slippages, sensor limitations, rough terrains, and modeling uncertainties. To overcome the sensor-dependent control systems associated with the closed-loop torque/pressure in hydraulic IWD-actuated HWMRs, a robust observer network based on an adaptive barrier Lyapunov function (BLF) is proposed to estimate the required in-wheel motor torque to track the velocity references. Then, another adaptive BLF for valve control signals is employed to modulate the hydraulic fluid to generate the estimated torque for each IWD. The RTOVC strategy ensures user-defined safety within the logarithmic BLF framework by constraining the valve control signal, actual velocity, velocity tracking error, and torque of each hydraulic IWD in an HWMR to avoid exceeding specified limits. Despite its safety constraints, external disturbances, and modeling uncertainties, robustness and uniformly exponential stability of the RTOVC-applied hydraulic IWD mechanism are ensured in HWMRs. Experimental investigations using a 6,500-kg HWMR, actuated by four independent IWDs under intense disturbances and safety-defined constraints, validate the performance of the RTOVC.
Distributed Resilient Secondary Control for Microgrids with Attention-based Weights against High-density Misbehaving Agents
Authors: Yutong Li, Lili Wang
Date: 2024-09-18 00:00:00

Microgrids (MGs) have been equipped with large-scale distributed energy sources (DESs), and become more vulnerable due to the low inertia characteristic. In particular, high-density misbehaving DESs caused by cascading faults bring a great challenge to frequency synchronization and active power sharing among DESs. To tackle the problem, we propose a fully distributed resilient consensus protocol, which utilizes confidence weights to evaluate the level of trust among agents with a first-order filter and a softmax-type function. We pioneer the analysis of this nonlinear control system from the system operating range and the graph structure perspectives. Both necessary and sufficient conditions are provided to ensure DACC to be uniformly ultimately bounded, even in a robust network with low connectivity. Simulations on a modified IEEE33-bus microgrid testbed with 17 DESs validate that DACC outperforms existing methods in the presence of 8 misbehaving DESs.
Communication, Sensing and Control integrated Closed-loop System: Modeling, Control Design and Resource Allocation
Authors: Zeyang Meng, Dingyou Ma, Zhiqing Wei, Ying Zhou, Zhiyong Feng
Date: 2024-09-18 00:00:00

The wireless communication technologies have fundamentally revolutionized industrial operations. The operation of the automated equipment is conducted in a closed-loop manner, where the status of devices is collected and sent to the control center through the uplink channel, and the control center sends the calculated control commands back to the devices via downlink communication. However, existing studies neglect the interdependent relationship between uplink and downlink communications, and there is an absence of a unified approach to model the communication, sensing, and control within the loop. This can lead to inaccurate performance assessments, ultimately hindering the ability to provide guidance for the design of practical systems. Therefore, this paper introduces an integrated closed-loop model that encompasses sensing, communication, and control functionalities, while addressing the coupling effects between uplink and downlink communications. Through the analysis of system convergence, an inequality pertaining to the performances of sensing, communication, and control is derived. Additionally, a joint optimization algorithm for control and resource allocation is proposed. Simulation results are presented to offer an intuitive understanding of the impact of system parameters. The findings of this paper unveil the intricate correlation among sensing, communication, and control, providing insights for the optimal design of industrial closed-loop systems.
Physics-Informed Building Occupancy Detection: a Switching Process with Markov Regime
Authors: Amir-Mohammad Esmaieeli-Sikaroudi, Boris Goikhman, Dmitri Chubarov, Hung Dinh Nguyen, Michael Chertkov, Petr Vorobev
Date: 2024-09-18 00:00:00

Energy efficiency of buildings is considered to be one of the major means of achieving the net-zero carbon goal around the world. The big part of the energy savings are supposed to be coming from optimizing the operation of the building heating, ventilation, and air conditioning (HVAC) systems. There is a natural trade-off between the energy efficiency and the indoor comfort level, and finding an optimal operating schedule/regime requires knowing the occupancy of different spaces inside of the building. Moreover, the COVID-19 pandemic has also revealed the need to sustain the high quality of the indoor air in order to reduce the risk of spread of infection. Occupancy detection from indoor sensors is thus an important practical problem. In the present paper, we propose detection of occupancy based on the carbon dioxide measurements inside the building. In particular, a new approach based on the, so-called, switching auto-regressive process with Markov regime is presented and justified by the physical model of the carbon dioxide concentration dynamics. We demonstrate the efficiency of the method compared to simple Hidden Markov approaches on simulated and real-life data. We also show that the model is flexible and can be generalized to account for different ventilation regimes, simultaneously detecting the occupancy and the ventilation rate.
Revolutionizing Pharmaceutical Manufacturing: Advances and Challenges of 3D Printing System and Control
Authors: Rahul Kumar, Vikram Singh, Priya Gupta
Date: 2024-09-18 00:00:00

The advent of 3D printing has transformed the pharmaceutical industry, enabling precision drug manufacturing with controlled release profiles, dosing, and structural complexity. Additive manufacturing (AM) addresses the growing demand for personalized medicine, overcoming limitations of traditional methods. This technology facilitates tailored dosage forms, complex geometries, and real-time quality control. Recent advancements in drop-on-demand printing, UV curable inks, material science, and regulatory frameworks are discussed. Despite opportunities for cost reduction, flexibility, and decentralized manufacturing, challenges persist in scalability, reproducibility, and regulatory adaptation. This review provides an in-depth analysis of the current state of AM in pharmaceutical manufacturing, exploring recent developments, challenges, and future directions for mainstream integration.
Modeling and Simulation of a Fully Autonomous Electric Vehicle (AEV)
Authors: Qasim Ajao, Lanre Sadeeq
Date: 2024-09-18 00:00:00

With continuous advancements in science and technology, there is increasing focus on environmental sustainability, leading to heightened interest in autonomous electric vehicles (AEVs). AEVs hold significant potential for enhancing electric mobility, energy efficiency, environmental preservation, and driving capabilities. They offer numerous advantages that could revolutionize transportation and urban lifestyles, notably by improving road safety through the reduction of human error, a leading cause of accidents. This paper presents a comprehensive simulation of an AEV by developing and integrating models for the driving system, battery, motor, transmission, and vehicle body within the MATLAB/Simulink environment. Each component is configured and interconnected to create a pure vehicle model. The simulation is conducted under UDDS cycle conditions to evaluate the vehicle's performance in speed maintenance, driving distance, acceleration capabilities, and battery state-of-charge (SoC) dynamics. The results demonstrate that the vehicle exhibits strong output characteristics. Furthermore, the driver model incorporates an energy brake recovery function, which, when set to a 50% recovery efficiency, increases the driving distance by 25% compared to a vehicle without this function.
A novel pedestrian road crossing simulator for dynamic traffic light scheduling systems
Authors: Dayuan Tan, Mohamed Younis, Wassila Lalouani, Shuyao Fan, Guozhi Song
Date: 2024-09-18 00:00:00
Tasks: Management, Scheduling

The major advances in intelligent transportation systems are pushing societal services toward autonomy where road management is to be more agile in order to cope with changes and continue to yield optimal performance. However, the pedestrian experience is not sufficiently considered. Particularly, signalized intersections are expected to be popular if not dominant in urban settings where pedestrian density is high. This paper presents the design of a novel environment for simulating human motion on signalized crosswalks at a fine-grained level. Such a simulation not only captures typical behavior, but also handles cases where large pedestrian groups cross from both directions. The proposed simulator is instrumental for optimized road configuration management where the pedestrians' quality of experience, for example, waiting time, is factored in. The validation results using field data show that an accuracy of 98.37 percent can be obtained for the estimated crossing time. Other results using synthetic data show that our simulator enables optimized traffic light scheduling that diminishes pedestrians' waiting time without sacrificing vehicular throughput.
Distributed Deep Koopman Learning for Nonlinear Dynamics
Authors: Wenjian Hao, Lili Wang, Ayush Rai, Shaoshuai Mou
Date: 2024-09-17 00:00:00

Koopman operator theory has proven to be highly significant in system identification, even for challenging scenarios involving nonlinear time-varying systems (NTVS). In this context, we examine a network of connected agents, each with limited observation capabilities, aiming to estimate the dynamics of an NTVS collaboratively. Drawing inspiration from Koopman operator theory, deep neural networks, and distributed consensus, we introduce a distributed algorithm for deep Koopman learning of the dynamics of an NTVS. This approach enables individual agents to approximate the entire dynamics despite having access to only partial state observations. We guarantee consensus not only on the estimated dynamics but also on its structure, i.e., the matrices encountered in the linear equation of the lifted Koopman system. We provide theoretical insights into the convergence of the learning process and accompanying numerical simulations.
Data-conforming data-driven control: avoiding premature generalizations beyond data
Authors: Mohammad Ramadan, Evan Toler, Mihai Anitescu
Date: 2024-09-17 00:00:00

Data-driven and adaptive control approaches face the problem of introducing sudden distributional shifts beyond the distribution of data encountered during learning. Therefore, they are prone to invalidating the very assumptions used in their own construction. This is due to the linearity of the underlying system, inherently assumed and formulated in most data-driven control approaches, which may falsely generalize the behavior of the system beyond the behavior experienced in the data. This paper seeks to mitigate these problems by enforcing consistency of the newly designed closed-loop systems with data and slow down any distributional shifts in the joint state-input space. This is achieved through incorporating affine regularization terms and linear matrix inequality constraints to data-driven approaches, resulting in convex semi-definite programs that can be efficiently solved by standard software packages. We discuss the optimality conditions of these programs and then conclude the paper with a numerical example that further highlights the problem of premature generalization beyond data and shows the effectiveness of our proposed approaches in enhancing the safety of data-driven control methods.
Improved Dynamic Response in Grid-Forming Converters with Current Limiting Control during Fault Conditions
Authors: Somayeh Mehri Boroojeni, Ehsan Sharafoddin
Date: 2024-09-17 00:00:00

Modern power systems increasingly demand converter-driven generation systems that integrate seamlessly with grid infrastructure. Grid-based converters are particularly advantageous, as they operate in harmony with conventional synchronous machines. However, most existing research focuses on managing grid-forming converters (GFM) under normal conditions, often neglecting the converters' behavior during faults and their short-circuit capabilities. This paper addresses these gaps by introducing a power matching-based current limitation scheme, which ensures GFM converter synchronization while preventing over currents. It also highlights the limitations of grid-following techniques, which need to maintain robust grid-forming properties during fault conditions. Unlike conventional methods, no assumptions are made regarding outer power loops or droop mechanisms, and current references are immediately restricted to prevent wind-ups. A dynamic virtual damping algorithm is proposed to improve fault isolation further. This technique enhances fault-ride-through capability and maintains grid-forming properties even in weak grid conditions. The dynamic virtual damping controller and fault mode for GFMs are modeled and validated using detailed simulations in MATLAB. These results demonstrate that altering outer power sources, rather than internal structures, improves converter performance during faults, ensuring grid stability and reliability.
Robust Attitude Estimation with Quaternion Left-Invariant EKF and Noise Covariance Tuning
Authors: Yash Pandey, Rahul Bhattacharyya, Yatindra Nath Singh
Date: 2024-09-17 00:00:00

Accurate estimation of noise parameters is critical for optimal filter performance, especially in systems where true noise parameter values are unknown or time-varying. This article presents a quaternion left-invariant extended Kalman filter (LI-EKF) for attitude estimation, integrated with an adaptive noise covariance estimation algorithm. By employing an iterative expectation-maximization (EM) approach, the filter can effectively estimate both process and measurement noise covariances. Extensive simulations demonstrate the superiority of the proposed method in terms of attitude estimation accuracy and robustness to initial parameter misspecification. The adaptive LI-EKF's ability to adapt to time-varying noise characteristics makes it a promising solution for various applications requiring reliable attitude estimation, such as aerospace, robotics, and autonomous systems.
Black-box Stealthy GPS Attacks on Unmanned Aerial Vehicles
Authors: Amir Khazraei, Haocheng Meng, Miroslav Pajic
Date: 2024-09-17 00:00:00
Tasks: Sensor Fusion

This work focuses on analyzing the vulnerability of unmanned aerial vehicles (UAVs) to stealthy black-box false data injection attacks on GPS measurements. We assume that the quadcopter is equipped with IMU and GPS sensors, and an arbitrary sensor fusion and controller are used to estimate and regulate the system's states, respectively. We consider the notion of stealthiness in the most general form, where the attack is defined to be stealthy if it cannot be detected by any existing anomaly detector. Then, we show that if the closed-loop control system is incrementally exponentially stable, the attacker can cause arbitrarily large deviation in the position trajectory by compromising only the GPS measurements. We also show that to conduct such stealthy impactfull attack values, the attacker does not need to have access to the model of the system. Finally, we illustrate our results in a UAV case study.