arXiv subjects

Mehdi Hosseinzadeh

Publications and source records attributed to Mehdi Hosseinzadeh.

At least 37 records · Page 2Linked to original sources

arXiv2025

Provably-Stable Neural Network-Based Control of Nonlinear Systems

In recent years, Neural Networks (NNs) have been employed to control nonlinear systems due to their potential capability in dealing with situations that might be difficult for conventional nonlinear control schemes. However, to the best of our knowledge, the current literature on NN-based control lacks theoretical guarantees for stability and tracking performance. This precludes the application of NN-based control schemes to systems where stringent stability and performance guarantees are required. To address this gap, this paper proposes a systematic and comprehensive methodology to design provably-stable NN-based control schemes for affine nonlinear systems. Rigorous analysis is provided to show that the proposed approach guarantees stability of the closed-loop system with the NN in the loop. Also, it is shown that the resulting NN-based control scheme ensures that system states asymptotically converge to a neighborhood around the desired equilibrium point, with a tunable proximity threshold. The proposed methodology is validated and evaluated via simulation studies on an inverted pendulum and experimental studies on a Parrot Bebop 2 drone.

math.OC↗

arXiv2025

A Guaranteed-Stable Neural Network Approach for Optimal Control of Nonlinear Systems

A promising approach to optimal control of nonlinear systems involves iteratively linearizing the system and solving an optimization problem at each time instant to determine the optimal control input. Since this approach relies on online optimization, it can be computationally expensive, and thus unrealistic for systems with limited computing resources. One potential solution to this issue is to incorporate a Neural Network (NN) into the control loop to emulate the behavior of the optimal control scheme. Ensuring stability and reference tracking in the resulting NN-based closed-loop system requires modifications to the primary optimization problem. These modifications often introduce non-convexity and nonlinearity with respect to the decision variables, which may surpass the capabilities of existing solvers and complicate the generation of the training dataset. To address this issue, this paper develops a Neural Optimization Machine (NOM) to solve the resulting optimization problems. The central concept of a NOM is to transform the optimization challenges into the problem of training a NN. Rigorous proofs demonstrate that when a NN trained on data generated by the NOM is used in the control loop, all signals remain bounded and the system states asymptotically converge to a neighborhood around the desired equilibrium point, with a tunable proximity threshold. Simulation and experimental studies are provided to illustrate the effectiveness of the proposed methodology.

math.OC↗

arXiv2025

Practical Considerations for Implementing Robust-to-Early Termination Model Predictive Control

Model Predictive Control (MPC) is widely used to achieve performance objectives, while enforcing operational and safety constraints. Despite its high performance, MPC often demands significant computational resources, making it challenging to implement in systems with limited computing capacity. A recent approach to address this challenge is to use the Robust-to-Early Termination (REAP) strategy. At any time instant, REAP converts the MPC problem into the evolution of a virtual dynamical system whose trajectory converges to the optimal solution, and provides guaranteed sub-optimal and feasible solution whenever its evolution is terminated due to limited computational power. REAP has been introduced as a continuous-time scheme and its theoretical properties have been derived under the assumption that it performs all the computations in continuous time. However, REAP should be practically implemented in discrete-time. This paper focuses on the discrete-time implementation of REAP, exploring conditions under which anytime feasibility and convergence properties are maintained when the computations are performed in discrete time. The proposed methodology is validated and evaluated through extensive simulation and experimental studies.

math.OC↗

arXiv2024

BEVPose: Unveiling Scene Semantics through Pose-Guided Multi-Modal BEV Alignment

In the field of autonomous driving and mobile robotics, there has been a significant shift in the methods used to create Bird's Eye View (BEV) representations. This shift is characterised by using transformers and learning to fuse measurements from disparate vision sensors, mainly lidar and cameras, into a 2D planar ground-based representation. However, these learning-based methods for creating such maps often rely heavily on extensive annotated data, presenting notable challenges, particularly in diverse or non-urban environments where large-scale datasets are scarce. In this work, we present BEVPose, a framework that integrates BEV representations from camera and lidar data, using sensor pose as a guiding supervisory signal. This method notably reduces the dependence on costly annotated data. By leveraging pose information, we align and fuse multi-modal sensory inputs, facilitating the learning of latent BEV embeddings that capture both geometric and semantic aspects of the environment. Our pretraining approach demonstrates promising performance in BEV map segmentation tasks, outperforming fully-supervised state-of-the-art methods, while necessitating only a minimal amount of annotated data. This development not only confronts the challenge of data efficiency in BEV representation learning but also broadens the potential for such techniques in a variety of domains, including off-road and indoor environments.

cs.RO↗

arXiv2024

A Diagnostic Model for Acute Lymphoblastic Leukemia Using Metaheuristics and Deep Learning Methods

Acute lymphoblastic leukemia (ALL) severity is determined by the presence and ratios of blast cells (abnormal white blood cells) in both bone marrow and peripheral blood. Manual diagnosis of this disease is a tedious and time-consuming operation, making it difficult for professionals to accurately examine blast cell characteristics. To address this difficulty, researchers use deep learning and machine learning. In this paper, a ResNet-based feature extractor is utilized to detect ALL, along with a variety of feature selectors and classifiers. To get the best results, a variety of transfer learning models, including the Resnet, VGG, EfficientNet, and DensNet families, are used as deep feature extractors. Following extraction, different feature selectors are used, including Genetic algorithm, PCA, ANOVA, Random Forest, Univariate, Mutual information, Lasso, XGB, Variance, and Binary ant colony. After feature qualification, a variety of classifiers are used, with MLP outperforming the others. The recommended technique is used to categorize ALL and HEM in the selected dataset which is C-NMC 2019. This technique got an impressive 90.71% accuracy and 95.76% sensitivity for the relevant classifications, and its metrics on this dataset outperformed others.

cs.CV↗

arXiv2024

Enhanced Heart Sound Classification Using Mel Frequency Cepstral Coefficients and Comparative Analysis of Single vs. Ensemble Classifier Strategies

This paper explores the efficacy of Mel Frequency Cepstral Coefficients (MFCCs) in detecting abnormal heart sounds using two classification strategies: a single classifier and an ensemble classifier approach. Heart sounds were first pre-processed to remove noise and then segmented into S1, systole, S2, and diastole intervals, with thirteen MFCCs estimated from each segment, yielding 52 MFCCs per beat. Finally, MFCCs were used for heart sound classification. For that purpose, in the single classifier strategy, the MFCCs from nine consecutive beats were averaged to classify heart sounds by a single classifier (either a support vector machine (SVM), the k nearest neighbors (kNN), or a decision tree (DT)). Conversely, the ensemble classifier strategy employed nine classifiers (either nine SVMs, nine kNN classifiers, or nine DTs) to individually assess beats as normal or abnormal, with the overall classification based on the majority vote. Both methods were tested on a publicly available phonocardiogram database. The heart sound classification accuracy was 91.95% for the SVM, 91.9% for the kNN, and 87.33% for the DT in the single classifier strategy. Also, the accuracy was 93.59% for the SVM, 91.84% for the kNN, and 92.22% for the DT in the ensemble classifier strategy. Overall, the results demonstrated that the ensemble classifier strategy improved the accuracies of the DT and the SVM by 4.89% and 1.64%, establishing MFCCs as more effective than other features, including time, time-frequency, and statistical features, evaluated in similar studies.

cs.SD↗

arXiv2024

Discrete-Time Implementation of Explicit Reference Governor

Explicit reference governor (ERG) is an add-on unit that provides constraint handling capability to pre-stabilized systems. The main idea behind ERG is to manipulate the derivative of the applied reference in continuous time such that the satisfaction of state and input constraints is guaranteed at all times. However, ERG should be practically implemented in discrete-time. This paper studies the discrete-time implementation of ERG, and provides conditions under which the feasibility and convergence properties of the ERG framework are maintained when the updates of the applied reference are performed in discrete time. The proposed approach is validated via extensive simulation and experimental studies.

eess.SY↗

arXiv2024

RoboHop: Segment-based Topological Map Representation for Open-World Visual Navigation

Mapping is crucial for spatial reasoning, planning and robot navigation. Existing approaches range from metric, which require precise geometry-based optimization, to purely topological, where image-as-node based graphs lack explicit object-level reasoning and interconnectivity. In this paper, we propose a novel topological representation of an environment based on "image segments", which are semantically meaningful and open-vocabulary queryable, conferring several advantages over previous works based on pixel-level features. Unlike 3D scene graphs, we create a purely topological graph with segments as nodes, where edges are formed by a) associating segment-level descriptors between pairs of consecutive images and b) connecting neighboring segments within an image using their pixel centroids. This unveils a "continuous sense of a place", defined by inter-image persistence of segments along with their intra-image neighbours. It further enables us to represent and update segment-level descriptors through neighborhood aggregation using graph convolution layers, which improves robot localization based on segment-level retrieval. Using real-world data, we show how our proposed map representation can be used to i) generate navigation plans in the form of "hops over segments" and ii) search for target objects using natural language queries describing spatial relations of objects. Furthermore, we quantitatively analyze data association at the segment level, which underpins inter-image connectivity during mapping and segment-level localization when revisiting the same place. Finally, we show preliminary trials on segment-level `hopping' based zero-shot real-world navigation. Project page with supplementary details: oravus.github.io/RoboHop/

cs.RO↗

arXiv2024

Closed-Loop Model Identification and MPC-based Navigation of Quadcopters: A Case Study of Parrot Bebop 2

The growing potential of quadcopters in various domains, such as aerial photography, search and rescue, and infrastructure inspection, underscores the need for real-time control under strict safety and operational constraints. This challenge is compounded by the inherent nonlinear dynamics of quadcopters and the on-board computational limitations they face. This paper aims at addressing these challenges. First, this paper presents a comprehensive procedure for deriving a linear yet efficient model to describe the dynamics of quadrotors, thereby reducing complexity without compromising efficiency. Then, this paper develops a steady-state-aware Model Predictive Control (MPC) to effectively navigate quadcopters, while guaranteeing constraint satisfaction at all times. The main advantage of the steady-state-aware MPC is its low computational complexity, which makes it an appropriate choice for systems with limited computing capacity, like quadcopters. This paper considers Parrot Bebop 2 as the running example, and experimentally validates and evaluates the proposed algorithms.

cs.RO↗

arXiv2023

CANE: A Cascade-Control Approach for Network-Assisted Video QoE Management

Prior efforts have shown that network-assisted schemes can improve the Quality-of-Experience (QoE) and QoE fairness when multiple video players compete for bandwidth. However, realizing network-assisted schemes in practice is challenging, as: i) the network has limited visibility into the client players' internal state and actions; ii) players' actions may nullify or negate the network's actions; and iii) the players' objectives might be conflicting. To address these challenges, we formulate network-assisted QoE optimization through a cascade control abstraction. This informs the design of CANE, a practical network-assisted QoE framework. CANE uses machine learning techniques to approximate each player's behavior as a black-box model and model predictive control to achieve a near-optimal solution. We evaluate CANE through realistic simulations and show that CANE improves multiplayer QoE fairness by ~50% compared to pure client-side adaptive bitrate algorithms and by ~20% compared to uniform traffic shaping.

math.OC↗

arXiv2022

Longitudinal Analysis of Heart Rate and Physical Activity Collected from Smartwatches

Smartwatches (SWs) can continuously and autonomously monitor vital signs, including heart rates and physical activities involving wrist movement. The monitoring capability of SWs has several key health benefits arising from their role in preventive and diagnostic medicine. Current research, however, has not explored many of these opportunities, including longitudinal studies. In our work, we gathered longitudinal data points, e.g., heart rate and physical activity, from various brands of SWs worn by 1,014 users. Our analysis shows three common heart rate patterns during sleep but two common patterns during the day. We find that heart rate and physical activities are higher in summer and the first month of the new year compared to other months. Moreover, physical activities are reduced on weekends compared with weekdays. Interestingly, the highest peak of physical activity is during the evening.

cs.HC↗

arXiv2022

Performance Evaluation of Query Plan Recommendation with Apache Hadoop and Apache Spark

Access plan recommendation is a query optimization approach that executes new queries using prior created query execution plans (QEPs). The query optimizer divides the query space into clusters in the mentioned method. However, traditional clustering algorithms take a significant amount of execution time for clustering such large datasets. The MapReduce distributed computing model provides efficient solutions for storing and processing vast quantities of data. Apache Spark and Apache Hadoop frameworks are used in the present investigation to cluster different sizes of query datasets in the MapReduce-based access plan recommendation method. The performance evaluation is performed based on execution time. The results of the experiments demonstrated the effectiveness of parallel query clustering in achieving high scalability. Furthermore, Apache Spark achieved better performance than Apache Hadoop, reaching an average speedup of 2x.

cs.DB↗

arXiv2022

Optimal Active Fault Detection in Inverter-Based Grids

Ground faults in converter-based grids can be difficult to detect because, unlike in grids with synchronous machines, they often do not result in large currents. One recent strategy is for each converter to inject a perturbation that makes faults easier to distinguish from normal operation. In this paper, we construct optimal perturbation sequences for use with the Multiple Model Kalman Filter. The perturbations maximize the difference between faulty and fault-free operation while respecting limits on performance degradation. Simulations show that the optimal input sequence increases the confidence of fault detection while decreasing detection time. It is shown that there is a tradeoff between detection and degradation of the control performance, and that the method is robust to parameter variations.

math.OC↗

arXiv2022

MPC-Based Emergency Vehicle-Centered Multi-Intersection Traffic Control

This paper proposes a traffic control scheme to alleviate traffic congestion in a network of interconnected signaled lanes/roads. The proposed scheme is emergency vehicle-centered, meaning that it provides an efficient and timely routing for emergency vehicles. In the proposed scheme, model predictive control is utilized to control inlet traffic flows by means of network gates, as well as configuration of traffic lights across the network. Two schemes are considered in this paper: i) centralized; and ii) decentralized. In the centralized scheme, a central unit controls the entire network. This scheme provides the optimal solution, even though it might not fulfil real-time computation requirements for large networks. In the decentralized scheme, each intersection has its own control unit, which sends local information to an aggregator. The main responsibility of this aggregator is to receive local information from all control units across the network as well as the emergency vehicle, to augment the received information, and to share it with the control units. Since the decision-making in decentralized scheme is local and the aggregator should fulfil the above-mentioned tasks during a traffic cycle which takes a long period of time, the decentralized scheme is suitable for large networks, even though it may provide a sub-optimal solution. Extensive simulation studies are carried out to validate the proposed schemes, and assess their performance. Notably, the obtained results reveal that traveling times of emergency vehicles can be reduced up to ~50% by using the centralized scheme and up to ~30% by using the decentralized scheme, without causing congestion in other lanes.

math.OC↗

arXiv2022

Stochastic Multi-armed Bandits with Non-stationary Rewards Generated by a Linear Dynamical System

The stochastic multi-armed bandit has provided a framework for studying decision-making in unknown environments. We propose a variant of the stochastic multi-armed bandit where the rewards are sampled from a stochastic linear dynamical system. The proposed strategy for this stochastic multi-armed bandit variant is to learn a model of the dynamical system while choosing the optimal action based on the learned model. Motivated by mathematical finance areas such as Intertemporal Capital Asset Pricing Model proposed by Merton and Stochastic Portfolio Theory proposed by Fernholz that both model asset returns with stochastic differential equations, this strategy is applied to quantitative finance as a high-frequency trading strategy, where the goal is to maximize returns within a time period.

cs.LG↗

arXiv2022

Implementing Optimization-Based Control Tasks in Cyber-Physical Systems With Limited Computing Capacity

A common aspect of today's cyber-physical systems is that multiple optimization-based control tasks may execute in a shared processor. Such control tasks make use of online optimization and thus have large execution times; hence, their sampling periods must be large as well to satisfy real-time schedulability condition. However, larger sampling periods may cause worse control performance. The goal of our work is to develop a robust to early termination optimization approach that can be used to effectively solve onboard optimization problems involved in controlling the system despite the presence of unpredictable, variable, and limited computing capacity. The significance of the developed approach is that the optimization iterations can be stopped at any time instant with a guaranteed feasible solution; as a result, optimization-based control tasks can be implemented with a small sampling period (and consequently with a minimum degradation in the control performance).

math.OC↗

arXiv2022

ROTEC: Robust to Early Termination Command Governor for Systems with Limited Computing Capacity

A Command Governor (CG) is an optimization-based add-on scheme to a nominal closed-loop system. It is used to enforce state and control constraints by modifying reference commands. This paper considers the implementation of a CG on embedded processors that have limited computing resources and must execute multiple control and diagnostics functions; consequently, the time available for CG computations is limited and may vary over time. To address this issue, a robust to early termination command governor is developed which embeds the solution of a CG problem into the internal states of a virtual continuous-time dynamical system which runs in parallel to the process. This virtual system is built so that its trajectory converges to the optimal solution (with a tunable convergence rate), and provides a sub-optimal but feasible solution whenever its evolution is terminated. This allows the designer to implement a CG strategy with a small sampling period (and consequently with a minimum degradation in its performance), while maintaining its constraint-handling capabilities. Simulations are carried out to assess the effectiveness of the developed scheme in satisfying performance requirements and real-time schedulability conditions for a practical vehicle rollover example.

math.OC↗

arXiv2021

Reference Governor-Based Fault-Tolerant Constrained Control

This paper presents a fault-tolerant control scheme for constrained linear systems. First, a new variant of the Reference Governor (RG) called At Once Reference Governor (AORG) is introduced. The AORG is distinguished from the conventional RG by computing the Auxiliary Reference (AR) sequence so that to optimize performance over a prescribed time interval instead of only at the current time instant; this enables the integration of the AORG with fault detection schemes. In particular, it is shown that, when the AORG is combined with a Multi-Model Adaptive Estimator (MMAE), the AR sequence can be determined such that the tracking properties are guaranteed and constraints are satisfied at all times, while the detection performance is optimized, i.e., faults can be detected with a high probability of correctness. In addition a reconfiguration scheme is presented that ensures system viability despite the presence of faults based on recoverable sets. Simulations on a Boeing 747-100 aircraft model are carried out to evaluate the effectiveness of the AORG scheme in enforcing constraints and tracking the desired roll and side-slip angles. The effectiveness of the presented fault-tolerant control scheme in maintaining the airplane viability in the presence of damaged vertical stabilizer is also demonstrated.

math.OC↗