Showing papers presented at "Mediterranean Conference on Control and Automation in 2019"

Anisotropy-Based Bounded Real Lemma for Multiplicative Noise Systems: the Finite Horizon Case

Abstract: In this paper, the advanced analysis of linear discrete time varying multiplicative noise system in terms of anisotropy-based theory is considered. Accurate formulae for anisotropic norm of such system with mutually independent multiplicative noises and additive input disturbances are given. There two types of anisotropy-based bounded real lemmas were derived, the first one in terms of backward-time difference Riccati equations, the another one in terms of inequalities.

Trust is the Cure to Distributed Consensus with Adversaries

Abstract: Distributed consensus is a prototypical distributed optimization and decision making problem in social, economic and engineering networked systems. In collaborative applications investigating the effects of adversaries is a critical problem. In this paper we investigate distributed consensus problems in the presence of adversaries. We combine key ideas from distributed consensus in computer science on one hand and in control systems on the other. The main idea is to detect Byzantine adversaries in a network of collaborating agents who have as goal reaching consensus, and exclude them from the consensus process and dynamics. We describe a novel trust-aware consensus algorithm that integrates the trust evaluation mechanism into the distributed consensus algorithm and propose various local decision rules based on local evidence. To further enhance the robustness of trust evaluation itself, we also introduce a trust propagation scheme in order to take into account evidences of other nodes in the network. The resulting algorithm is flexible and extensible, and can incorporate more complex designs of decision rules and trust models. To demonstrate the power of our trust-aware algorithm, we provide new theoretical security performance results in terms of miss detection and false alarm rates for regular and general trust graphs. We demonstrate through simulations that the new trust-aware consensus algorithm can effectively detect Byzantine adversaries and can exclude them from consensus iterations even in sparse networks with connectivity less than 2f+1, where f is the number of adversaries.

A Blockchain Construct for Energy Trading against Sybil Attacks

Abstract: In recent years, vehicular networks have been drawing special attention because of its significant potential role in the future smart city. Safety is a crucial status in vehicular networks, especially in energy trading where security of transactional data and safety against critical attacks is of most concern. The Sybil attack is one where an adversary can create multiple fake identities, become a part of the P2P network and try to manipulate the decision of the entire network in accordance to his own will. Thus in view to enhance the security of system, this paper focuses on a Sybil attack mitigation method based on blockchain and it's Proof of Work (PoW) consensus. Also a framework of automated privacy-preserving selection of charging stations (CS) based on pricing and the distance to the electric vehicle (EV) is presented. A blockchain based approach increases the transparency between EV and CS while preserving the privacy of the EV owners.

Recent Advances in Analysis and Design of Cyber-physical Systems using Passivity Indices

Abstract: Analysis and resilient design of Cyber-physical Systems have greatly benefited from energy based concepts of passivity and dissipativity. Recently, there has been much research devoted to the use of passivity indices in different components of Cyber-physical systems. Passivity indices are measures of passivity, indicating how passive a system is or how far is it from being passive and generalize passivity based methods to systems that might not be passive. In this paper, we will review recent advances in the use of passivity indices in Cyber-physical systems. We will overview how the indices have been defined and applied to different components of Cyber-physical systems and how they are used in the resilient design of compositional Cvber-phvsical systems.

A Reinforcement Learning Approach to Health Aware Control Strategy

Abstract: Health-aware control (HAC) has emerged as one of the domains where control synthesis is sought based upon the failure prognostics of system/component or the Remaining Useful Life (RUL) predictions of critical components The fact that mathematical dynamic (transition) models of RUL are rarely available, makes it difficult for RUL information to be incorporated into the control paradigm A novel framework for health aware control is presented in this paper where reinforcement learning based approach is used to learn an optimal control policy in face of component degradation by integrating global system transition data (generated by an analytical model that mimics the real system) and RUL predictions The RUL predictions generated at each step, is tracked to a desired value of RUL The latter is integrated within a cost function which is maximized to learn the optimal control The proposed method is studied using simulation of a DC motor and shaft wear

Weed Detection and Classification in High Altitude Aerial Images for Robot-Based Precision Agriculture

Abstract: With the rise of herbicide-resistant weeds, standard farming practices are no longer effective. Precision agriculture technologies can replace some of the standard practices but require information on the physical distribution of weed and crop species. In this work we outline a robotic weed management system and develop the image analysis portion of this system to test it with real aerial survey data. This portion of the system acts as an integral part of a robotic control loop, providing information on weed location and species to a robotic sprayer that will precisely apply the correct herbicide. The image analysis pipeline we develop proves the feasibility of obtaining this information from high altitude aerial surveys of agricultural land that already commonly take place. Our system performs well enough to reliably detect the presence of weed vegetation and to correctly classify it by species with an accuracy of 93.8 % between four classes of weed in images taken at an effective altitude of 80 feet (24.4 m).

Evil Twin Attack Detection using Discrete Event Systems in IEEE 802.11 Wi-Fi Networks

Abstract: Wi-Fi technology has seen rapid growth in the last two decades. It has revolutionized the way we access the Internet. However, they are vulnerable to Denial of Service attacks, Encryption Cracking, and Rogue Access Points etc. In this manuscript, our focus is on Evil Twin Attack, the most common type of Rogue Access Point (RAP). An evil twin AP lures client(s) into connecting to it, disguising itself as a genuine AP by spoofing its MAC address and SSID (Service Set IDentifier). Once a client is connected to the evil twin AP, the attacker can spy on its communication, re-direct client(s) to malicious websites, compromise credentials. Whitelisting AP(s), timing based solutions, patching AP/client etc., are some existing methods to detect evil twin AP(s) in a network. However, practically methods demand comprehensive set up and maintenance, they suffer from scalability and compatibility issues. Some even require protocol modifications, thus making it expensive and practically infeasible in a large scale network with no proof of correctness. To address these issues, we propose a Discrete Event System (DES) based approach for Intrusion Detection System (IDS) for evil twin attacks in a Wi-Fi network.

A Data-driven Adaptive Controller Reconfiguration for Fault Mitigation: A Passivity Approach

Abstract: This paper presents a new data-driven fault identification and controller reconfiguration algorithm. The presented algorithm relies only on the system's input and output data, and it does not require a detailed system description. The proposed algorithm detects changes in the input-output behavior of the system, whether due to faults or malicious attacks and then reacts by reconfiguring the existing controller. This method does not identify the internal structure of the system nor the extent and nature of the attack; hence it can quickly react to faults and attacks. The proposed method can be readily applied to various applications without significant modifications or tuning, as demonstrated by the examples in the paper.

Distributed Adaptive Control of Multi-Zone HVAC Systems

Abstract: Heating, Ventilation, and Air-Conditioning (HVAC) systems consume up to 20% of total energy consumption, hence the need for efficient HVAC control mechanisms. Since buildings consist of multiple interconnected zones, the thermal interaction between them should be taken into account while designing a control mechanism. In this paper, we propose a distributed control scheme for a multi-zone building which can control zone temperature by considering exchange of information between zones. Each local controller regulates zone temperature and compensates for the thermal effect of neighboring zones. In addition, most common control approaches require either accurate knowledge of system parameters or historical data of system operation. However, in the presence of dynamic parametric uncertainties or disturbances these control schemes may not perform efficiently and as desired. For this reason we extend the proposed scheme to a distributed adaptive control scheme which can react to and compensate for possible changes in system parameters. In this case, system parameters are considered unknown and the controller gains are estimated on-line by an adaptive law. The applicability of the proposed schemes is demonstrated by an example.

Finite time functional observers for descriptor systems. Application to fault tolerant control

Abstract: In this paper a new finite-time convergent functional dynamical observer design for descriptor systems is presented. The conditions for its existence and stability are given. This observer is then applied to the sensor and actuator faults detection and estimation. This estimation is used in the observer-based fault tolerant control design. All the obtained results are illustrated by numerical examples.

Blending of Inputs and Outputs for Modal Velocity Feedback

Abstract: Dynamical systems like mechanical structures can be effectively damped by applying forces which oppose the velocity measured at the very same location. To apply this principle also to systems with multiple actuators and sensors of different type and at different locations, a novel control approach is presented in this paper. The control approach aims to damp individual modes by a minimum-gain feedback of blended measurement outputs to blended control inputs. To that end, a numerically efficient algorithm is proposed for computing input and output blending vectors which yield the desired isolation of the target mode(s). The effectiveness of the proposed approach is demonstrated by increasing the modal damping of an aeroelastic system.

Dynamic Predictive Modeling Approach of User Behavior in Virtual Reality based Application

Abstract: Virtual Reality (VR) is considered to be a powerful modern medium for immersive data visualization and exploration. However, few studies have proposed solutions to complement data visualization in immersive environment considering the user's behavior. This paper addresses dynamic modeling of user behavior approach in an interactive VR based application. In this application, real-time data communication is employed to track accurate location and orientation of head mounted display device worn by the user. In our experiment, we use example of collected data and provide a methodology to predict next movements of the user by using nonlinear autoregressive (NAR) and location in the application by the nonlinear autoregressive neural network with exogenous inputs (NARX). Results suggest both neural networks are suitable for performing prediction which can be used to achieve an improved feeling of presence while reducing required high computational power. Data analysis part of the research is also linked to human behaviors to improve studies which are usually performed by traditional survey techniques.

Analysis of Higher Derivative Degree PID Controllers via Virtual Laboratory

Abstract: A new controller has recently been developed, belonging to the generalized class of PID regulators. This article discusses a simulation tool whose role is to promote the use of the developed controller. The advantage of the new controller consists in the fact that the filtration has been taking into account during the controller design process that has enabled to use also higher derivatives degrees. The paper summarizes the development of PID controllers with higher derivative degree in a general way. The corresponding developed simulation tool can run offline or online. The simulation tool provides a comparison of setpoint and disturbance step responses in the control circuit with the first order time-delayed controlled system influenced by a noise. Generally, the simulation tool is an easy way for all potential users from the control field who want to become familiar with the new control design technique that extends classical PID control by higher derivative degrees.

Dynamic Output Feedback Controller Synthesis using an LMI-based α- Strictly Negative Imaginary Framework

Abstract: This paper deals with the dynamic output feedback controller synthesis utilizing the α - strictly negative imaginary systems property. The proposed scheme ensures robust stability in closed-loop against the set of all stable, strictly proper negative imaginary uncertainties that satisfies the DC loop gain condition pertaining to negative imaginary stability. In addition to that, a prescribed decay rate in the closed-loop time response is also enforced via α - pole placement. Two illustrative examples have been studied to demonstrate the usefulness of the proposed controller synthesis scheme.

Lamarckian Inheritance in Neuromodulated Multiobjective Evolutionary Neurocontrollers

Abstract: This paper presents a novel evolutionary multiobjective neurocontroller with unsupervised learning and Lamarckian inheritance for robot navigation. Multiobjective evolution of network weights and topologies (NEAT-MODS) is augmented with Lamarckian inherited neuromodulated learning. NEAT-MODS is an NSGA-II augmented multiobjective neurocon-troller that uses two conflicting objectives. NEAT-MODS uses a selection process that aims to ensure Pareto-optimal genotypic diversity and elitism. Neuromodulation is a biologically-inspired technique that can adapt the per-connection learning rates of synaptic plasticity. Effectiveness of the design is demonstrated using a series of experiments with a simulated robot traversing a simple maze containing target goals. It is shown that when Lamarckian inheritance is combined with evolved neuromodulated learning, neural controllers are synthesized in fewer generations than by neuromodulated evolution alone. The proposed Lamarckian neuromodulated approach is found to be statistically superior to neuromodulation alone when applied to solve a multiobjective navigation problem.

IoT-Enabled Distributed Data Processing for Precision Agriculture

Abstract: Large scale monitoring systems, enabled by the emergence of networked embedded sensing devices, offer the opportunity of fine grained online spatio-temporal collection, communication and analysis of physical parameters. Various applications have been proposed and validated so far for environmental monitoring, security and industrial control systems. One particular application domain has been shown suitable for the requirements of precision agriculture where such systems can improve yields, increase efficiency and reduce input usage. We present a data analysis and processing approach for distributed monitoring of crops and soil where hierarchical aggregation and modelling primitives contribute to the robustness of the network by alleviating communication bottlenecks and reducing the energy required for redundant data transmissions. The focus is on leveraging the fog computing paradigm to exploit local node computing resources and generate events towards upper decision systems. Key metrics are reported which highlight the improvements achieved. A case study is carried out on real field data for crop and soil monitoring with outlook on operational and implementation constraints.

Some new results on the state liveness of open guidepath-based traffic systems

Abstract: Guidepath-based traffic systems is a pertinent abstraction that has been used extensively by the Discrete Event Systems (DES) community for the study of the traffic dynamics that take place in the automated unit-load material handling systems (MHS) encountered in various production and distribution facilities. A particular problem that has drawn extensive attention in the DES-based investigation of these systems, is the preservation of the system “liveness” i.e., the preservation of the ability of the system agents to complete successfully their currently allocated tasks and engage repeatedly to similar tasks in the future. The first part of this paper establishes that for a large subclass of the considered traffic systems, the preservation of their traffic liveness in a maximally permissive manner reduces to the observation of a particular property that must be possessed by the admitted traffic states. The second part of the paper provides some complexity analysis for assessing the aforementioned property on any given traffic state, under some further assumptions regarding the operation of the considered traffic systems and the structure of the traffic states under consideration. 11 The first author was partially supported by NSF grant ECCS-1707695 and the second author by the GARC project GC19-06175J.

Constrained filtered PID Controller for IPDT plants

Abstract: The paper deals with design of filtered constrained series PI and PID controller for integral-plus-dead-time (IPDT) plants. Its performance is broadly scalable by the multiple real dominant pole method which integrates the controller, noise filter and prefilter parameters tuning. Performance evaluation uses shape related measures built on the concept of monotonicity. The paper points out the possibility of two-fold interpretation of the controller's time constants and shows possible exploitation of this fact in achieving the required output transient shapes of the system with constrained input.

A Generalization of Linear Positive Systems

Abstract: The dynamics of linear positive systems maps the positive orthant to itself. Namely, it maps a set of vectors with zero sign variations to itself. Hence, a natural question is: what linear systems map the set of vectors with k sign variations to itself? To address this question we use tools from the theory of cooperative dynamical systems and the theory of totally positive matrices. Our approach yields a generalization of positive linear systems called k-positive linear systems, which reduces to positive systems for k=1. We show an application of this new class of systems to the analysis of invariant sets in nonlinear time-varying dynamical systems.

Power Grid Load Management using Electric Vehicles: A Distributed Resource Allocation Approach

Abstract: Technological advancements in modern age has introduced complex networks and multi-agent systems all around us. The main aim of multi-agent systems is to implement local control laws to achieve a global objective. The Distributed Resource Allocation (DRA) approach has been utilised in this paper for successful incorporation of a large number of Plug-in Electric Vehicles (PEVs) with the power grid. The DRA approach has been used to find an optimal charging strategy for all PEVs such that it satisfies the power grid objective in terms of the smoothening and flattening of grid load profile. The consensus protocol, an application of DRA approach has been implemented to provide a fair scheme of charging strategy to each individual PEV connected to the grid based on their commitment factor.

A Simple Machine Learning Technique for Model Predictive Control

Abstract: This paper is devoted to a simple approach for the offline computation of closed-loop optimal control for dynamical systems with imposed terminal state arising in Model Predictive Control Scheme (MPC). The here-proposed approach simply relies on some integrations of the characteristic equations associated to the optimal control problem, together with the classical supervised learning of a one-hidden-layer neuron network, to get a closed-loop MPC completely computed offline. Some examples are provided in the paper, which demonstrate the ability of this approach to tackle some quite large problems, with state dimensions reaching 50, without encountering limitations due to the so-called curse of dimensionality.

Two-an-One Pursuit with a Non-Zero Capture Radius

Abstract: In this paper, we revisit the “Two Cutters and Fugitive Ship” differential game that was addressed by Isaacs, but move away from point capture. We consider a two-on-one pursuit-evasion differential game with simple motion and pursuers endowed with circular capture sets of radius l > 0. The regions in the state space where only one pursuer effects the capture and the region in the state space where both pursuers cooperatively and isochronously capture the evader are characterized, thus solving the Game of Kind. Concerning the Game of Degree, the algorithm for the synthesis of the optimal state feedback strategies of the cooperating pursuers and of the evader is presented.

Binary Hypothesis Testing by Two Collaborating Observers: A Fresh Look

Abstract: We consider the binary hypothesis testing problem with two observers. There are two possible states of nature (or hypotheses). Observations are collected by two observers. The observations are statistically related to the true state of nature. Given the observations, the objective of both observers is to find out what is the true state of nature. We present three different approaches to address the problem. In the first (centralized) approach, the observations collected by both observers are sent to a central coordinator where hypothesis testing is performed. In the second approach, each observer performs hypothesis testing based on locally collected observations. At every time step decision information is exchanged until consensus is achieved. In the third approach, sequential hypothesis testing problem is formulated for each observer. The sequential hypothesis testing problem is solved for each observer using locally collected observations. Taking into account the asymmetric and random stopping times of the observers, a consensus algorithm has been designed. Numerical study has been done to assess the performance of the three approaches.

UAV Coverage Using Hexagonal Tessellation

Abstract: Unmanned aerial vehicles (UAVs) are increasingly being used for coverage applications. Some common examples include inspecting agricultural fields for certain plant diseases, tracking wildfire, photogrammetry, flying over an area to find avalanche victims, and several other search and rescue operations. Although fixed-wing UAVs can survey large areas more quickly and have a better battery lifetime compared to multirotor systems, they fail to provide a close up inspection of a certain area by hovering over it. Quadrotors provide excellent inspection capabilities; however, they have notoriously short battery lifetimes. In this paper, we propose a coverage algorithm through hexagonal tiling of a target region. We present a coverage path using the average distance, d, that can be travelled by a drone on a single charge, and the radius, r, of the viewing cone of a typical downward facing camera mounted on it as parameters. We compare our method with classical zigzag coverage pattern. Our results show that for large enough regions, the Hamiltonian circuit that passes through the centers of the tiling hexagons produces a shorter path.

Nonlinear Hierarchical Model Predictive Control for the Energy Management of a Hybrid Electric Vehicle

Abstract: In this paper a hierarchical model predictive control framework for the energy management of a hybrid electric vehicle is extended by replacing the supervisory linear model predictive controller with a nonlinear model predictive controller. The nonlinear system dynamics of a parallel hybrid electric vehicle are incorporated into a nonlinear optimal control problem which is solved at each time instance. The discretization of the continuous time system dynamics is realized via direct multiple shooting. The low-level linear model predictive controller considers the actuator dynamics for optimal tracking of the driver's torque demand while the high-level controller considers the slow dynamics of the vehicle and the battery. The proposed strategy is validated in simulation with the worldwide harmonized light vehicles test cycle and compared to a hierarchical model predictive control framework with linearized system dynamics.

Optimal Trajectories for a Mobile Robot with Bounded Accelerations in the Presence of a Wall or a Bounded Obstacle

Abstract: The time-optimal path planning problem in the plane for a point mass mobile robot navigating in an obstacle-presence environment is studied. The robot is subjected to maximal acceleration limits. The paper extends previous results which solved this problem in an obstacle-free environment. The problem is characterized as an optimal control problem with state-dependent obstacle constraints. Two equivalent ways of analysis are employed to obtain the necessary conditions for optimality under these constraints. The optimality conditions yield eight path primitives that form the time-optimal trajectories in the presence of obstacles. Representative examples are studied in order to demonstrate the method.

On Switching between Motion and Force Control

Abstract: In motion control technologies, an automatic switching between trajectory following and set reference force, upon the impact, is a frequently encountered requirement. Despite both, motion and force controls, are something of well-understood and elaborated in the control theory and engineering practice, a reliable switching between them is not always self-evident. It can lead to undesired deadlocks, limit cycles, chattering around switching point and, as consequence, to wearing or damages in the controlled plant and its environment. This paper contributes to analysis and understanding of the autonomous switching from the motion to force control and vice versa. Simple output and state feedback controllers are assumed, and the conditions to be held in vicinity to the switching state are explored. A simple yet robust hysteresis-relay-based switching strategy is shown to be suitable for such type of motion control applications. The hybrid automaton, as most general tool, is used for exploring and analyzing the transients. A multiple Lyapunov function approach is applied for stability analysis of the switched control system. A second-order system, with uncertain nonlinear dynamics, is demonstrated as an illustrative numerical example.

Surface Defect Detection for Automated Inspection Systems using Convolutional Neural Networks

Abstract: Optical inspection using unmanned aerial vehicles is a popular trend for detection of surface defects on industrial infrastructure, and full automation is the next step in order to improve potential and reduce costs. Binary classification of the obtained visual image data into defect and defect-free sets is one sub-task of these systems and is still often carried out either completely manually by an expert or by using pre-defined features as classifiers for automatic image post-processing. In contrast, deep convolutional neural networks (CNN) are able to perform both the feature extraction and classification tasks simultaneously by internal hierarchical learning. In this work, custom CNNs and a transfer-learned AlexNet are applied to an experimental data set with artificial defects in order to analyze suitability and required network depth for such surface inspections. Experiments are performed using a set of 2500 camera images total, yielding a classification accuracy of up to 99 % with a single CNN. Thereby, the amount of actual defects that are falsely classified as negative are minimized. Results proof the general effectiveness of the methodology and motivate the application to specific inspection tasks.

A New Guidance Law for Look-Angle Constrained Interception of Moving Targets

Abstract: This paper considers the problem of interception of nonmaneuvering moving targets achieving a desired impact angle with seeker's field-of-view limits, and investigates a two-gain guidance law as solution. Closed-form expressions of the guidance gains are derived imposing a desired impact angle and maximum look-angle as design inputs. Numerical simulations present the viability of the proposed solution.

Indoor and Outdoor Vehicle Localization using UWB Transceivers

Abstract: In this paper, an experiment is described which involved the usage of a set-up of 4 development boards based on Ultra Wide Band transceiver DW1000: three of them are configured as anchors while the forth one is configured as a tag. It is assumed that the tag is placed on a fixed reference (e.g. a traffic sign) and the anchors are placed on a moving vehicle (e.g on three corners of the vehicle). The paper deals with the improvement of the measurement of the distance between the tag and the anchors starting from the firmware of the transceiver. The actual firmware is modified such that the measurement sequence is performed faster in order to improve the accuracy of the localization.