Showing papers presented at "Mediterranean Conference on Control and Automation in 2017"

A review on multi-robot systems categorised by application domain

Abstract: Literature reviews on Multi-Robot Systems (MRS) typically focus on fundamental technical aspects, like coordination and communication, that need to be considered in order to coordinate a team of robots to perform a given task effectively and efficiently. Other reviews only consider works that aim to address a specific problem or one particular application of MRS. In contrast, this paper presents a survey of recent research works on MRS and categorises them according to their application domain. Furthermore, this paper compiles a number of seminal review works that have proposed specific taxonomies in classifying fundamental concepts, such as coordination, architecture and communication, in the field of MRS.

A Nonlinear Model Predictive Control scheme for cooperative manipulation with singularity and collision avoidance

Abstract: This paper addresses the problem of cooperative transportation of an object rigidly grasped by N robotic agents. In particular, we propose a Nonlinear Model Predictive Control (NMPC) scheme that guarantees the navigation of the object to a desired pose in a bounded workspace with obstacles, while complying with certain input saturations of the agents. Moreover, the proposed methodology ensures that the agents do not collide with each other or with the workspace obstacles as well as that they do not pass through singular configurations. The feasibility and convergence analysis of the NMPC are explicitly provided. Finally, simulation results illustrate the validity and efficiency of the proposed method.

Constrained trajectory generation for UAV systems using a B-spline parametrization

Abstract: This paper extends some previous work on trajectory generation for UAV (Unmanned Aerial Vehicles) using differential flatness in combination with B-splines parametrization. The originality of this work resides in the geometrical interpretations of the B-splines properties and their use in generating feasible flat trajectories for nonlinear UAV dynamics while ensuring continuous constraint validation. Of particular interest (and difficulty) are constraints involving system inputs since often the mapping between the input and the flat output space is strongly nonlinear. The tools used and the results obtained are exemplified over a particular UAV dynamical system and can be generalized to any nonlinear system admitting a flat description.

On the optimization of energy storage system placement for protecting power transmission grids against dynamic load altering attacks

Abstract: In this paper a power system protection scheme based on energy storage system placement against closed-loop dynamic load altering attacks is proposed. The protection design consists in formulating a non-convex optimization problem, subject to a Lyapunov stability constraint and solved using a two-step iterative procedure. Simulation results confirm the effectiveness of the approach and the potential relevance of using energy storage systems in support of primary frequency regulation services.

Hybrid controller approach for an autonomous ground vehicle path tracking problem

Abstract: This paper proposes a geometric model based lateral control system for a ground vehicle. Lateral control algorithm takes the advantages of two different path tracking methods at different path geometries. Two of the well-known geometric path tracking methods, namely Pure-Pursuit method and Stanley method, are combined with a simple and easy to implement approach. Pure-Pursuit method is very good at low speeds. However, as speed increases cutting corner behavior occurs and vehicle tends to converge to path relatively slow. In contrast, Stanley method convergence to the road is very fast and there is no cutting corner behavior. However since there is no look ahead behavior in Stanley method, it tends to overshoot from the desired path for sharp turns. In this work we propose a hybrid method to improve convergence to the path, prevent cutting corner and overshoot from the desired path.

Cloud computing for big data analytics in the Process Control Industry

Abstract: The aim of this article is to present an example of a novel cloud computing infrastructure for big data analytics in the Process Control Industry. Latest innovations in the field of Process Analyzer Techniques (PAT), big data and wireless technologies have created a new environment in which almost all stages of the industrial process can be recorded and utilized, not only for safety, but also for real time optimization. Based on analysis of historical sensor data, machine learning based optimization models can be developed and deployed in real time closed control loops. However, still the local implementation of those systems requires a huge investment in hardware and software, as a direct result of the big data nature of sensors data being recorded continuously. The current technological advancements in cloud computing for big data processing, open new opportunities for the industry, while acting as an enabler for a significant reduction in costs, making the technology available to plants of all sizes. The main contribution of this article stems from the presentation for a fist time ever of a pilot cloud based architecture for the application of a data driven modeling and optimal control configuration for the field of Process Control. As it will be presented, these developments have been carried in close relationship with the process industry and pave a way for a generalized application of the cloud based approaches, towards the future of Industry 4.0.

Model-free fuzzy control of twin rotor aerodynamic systems

Abstract: This paper suggests data-driven Model-Free Control (MFC) algorithms based on the combination of Takagi-Sugeno fuzzy (TSF) and intelligent proportional-integral (iPI) controllers. The new MFC algorithms are referred to as mixed TSF-iPI controllers, which are designed to control Single Input-Single Output (SISO) control structures for azimuth and pitch position control of a nonlinear twin rotor aerodynamic system (TRAS). The mixed TSF-iPI controllers are designed by fuzzifying the proportional-derivative term in the iPI controller structure. The performance of the SISO control structures with mixed TSF-iPI controllers and iPI controllers is compared by means of real-time experiments conducted on a TRAS laboratory equipment using controllers tuned in terms of a metaheuristic Gravitational Search Algorithm optimizer.

Synchronous interval observer design for switched LPV systems using multiple quadratic ISS-Lyapunov functions

Abstract: This paper deals with the problem of robust state estimation for switched LPV continuous-time systems with measurable and unmeasurable scheduling parameters. The switching law is assumed to be uncontrollable but online available, while the unmeasurable varying parameters are assumed to be bounded with a priori known bounds. The proposed approach is based on switched interval observers which provide guaranteed lower and upper bounds allowing to evaluate the set of admissible values of the real state vector. The stability and positivity conditions of the switched interval error are expressed in terms of linear matrix inequalities (LMIs), which have been established using multiple quadratic ISS-Lyapunov functions (MQLF) and average dwell-time concept. In order to enhance estimation accuracy and robustness an explicit bound of the interval error is guaranteed. The proposed approach is applied to robust estimation of vehicle lateral dynamics. Tests are conducted on experimental data in order to prove the effectiveness of the proposed switched interval observer.

Novel considerations on the negative pressure adhesion of electric ducted fans: An experimental study

Abstract: In this article, the potential of utilizing an Electric Ducted Fan (EDF) as an adhesion actuator is investigated in detail, where an experimental setup is implemented for evaluating the EDF's ability to adhere to a test surface through negative pressure generation. Different design variables and modifications to the original EDF structure are tested, while their impact on the adhesion efficiency is experimentally evaluated. The presented investigation acts as a preliminary study to the goal of incorporating the resulting optimized negative pressure-based actuation method in a wall-climbing robot for inspection of aircraft fuselages.

A distributed wardrop control algorithm for load balancing in smart grids

Abstract: This paper presents a distributed strategy for load balancing in a smart grid, modeling demand and supply as a networked dynamical system. The algorithm, which is characterized by point-to-point communications among agents implemented at the level of local energy management systems, is proven to converge to a Wardrop equilibrium. Numerical simulations of realistic scenarios are reported to show the effectiveness of the proposed approach.

Steady state evaluation of distributed secondary frequency control strategies for microgrids in the presence of clock drifts

Abstract: Secondary frequency control, i.e., the task of restoring the network frequency to its nominal value following a disturbance, is an important control objective in microgrids. In the present paper, we compare distributed secondary control strategies with regard to their behaviour under the explicit consideration of clock drifts. In particular we show that, if not considered in the tuning procedure, the presence of clock drifts may impair an accurate frequency restoration and power sharing. As a consequence, we derive tuning criteria such that zero steady state frequency deviation and power sharing is achieved even in the presence of clock drifts. Furthermore, the effects of clock drifts of the individual inverters on the different control strategies are discussed analytically and in a numerical case study.

Fuzzy logic-based direct torque control for induction machine drive

Abstract: Direct torque control (DTC) of induction machines presents an acceptable tracking scheme for both electromagnetic torque and stator flux. However, conventional DTC scheme, based on hysteresis comparators and the switching table, suffers from large torque and flux ripples, requiring high switching frequency operation for the voltage source inverter. In this paper, a modified DTC scheme based on fuzzy logic rules is proposed. A fuzzy controller (FC) is designed in order to give the appropriate inverter voltage vector. The FC could judge the deviation degree of the torque and flux errors to select the optimum voltage vector according to the fuzzy logic inference. The effectiveness of the proposed FC-based DTC for induction machine (IM) drive is verified at several operating conditions and highlighted by comparing to the conventional DTC. The obtained results illustrate low switching frequency, considerable ripples mitigation of torque, flux and the stator currents and improving the system performance.

Micro-algae productivity optimization using extremum-seeking control

Abstract: This study investigates a real-time optimization strategy for micro-algae continuous processes. The objective is to determine the optimal dilution rate maximizing the biomass productivity on the basis of the on-line measurement of biomass concentration and minimum prior process knowledge. Two different micro-algae strains (with different growth rates) are considered in this study: Dunaliela tertiolecta and Isochyris galbana. The extremum seeking control shows good performances in both cases. Practical tuning guidelines can be derived based on the strain growth dynamics.

Regularized moving-horizon piecewise affine regression using mixed-integer quadratic programming

Abstract: This paper presents a novel two-stage regularized moving-horizon algorithm for PieceWise Affine (PWA) regression. At the first stage, the training samples are processed iteratively, and a Mixed-Integer Quadratic-Programming (MIQP) problem is solved to find the sequence of active modes and the model parameters which best match the training data, within a relatively short time window in the past. According to a moving-horizon strategy, only the last element of the optimal sequence of active modes is kept, and the next sample is processed by shifting forward the estimation horizon. A regularization term on the model parameters is included in the cost of the formulated MIQP problem, to partly take into account also the past training data outside the considered time horizon. At the second stage, linear multi-category discrimination techniques are used to compute a polyhedral partition of the regressor space based on the estimated sequence of active modes.

A fast model predictive control algorithm for linear parameter varying systems with right invertible input matrix

Abstract: Nowadays, Linear Parameter Varying (LPV) Model Predictive Control (MPC) represents a consolidated approach to optimally regulate multivariable nonlinear systems imposing constraints on inputs and outputs. The crucial drawback, in particular in embedded LPV-MPC, is represented by the required computational effort. The Quadratic Programming (QP) problem solved by MPC changes at each iteration, and its reconstruction increases drastically the time required to compute the control law. This paper proposes an algorithm to reduce the LPV-MPC computational complexity for a particular class of linear systems. By exploiting a coordinate transformation, the QP reconstruction can be avoided. The effectiveness of the novel method is shown on a benchmark set of random MPC problems, as well as on a real-world case study regarding the control of an heat exchanger cell.

Learning models of human-robot interaction from small data

Abstract: This paper offers a new approach to learning discrete models for human-robot interaction (HRI) from small data. In the motivating application, HRI is an integral part of a pediatric rehabilitation paradigm that involves a play-based, social environment aiming at improving mobility for infants with mobility impairments. Designing interfaces in this setting is challenging, because in order to harness, and eventually automate, the social interaction between children and robots, a behavioral model capturing the causality between robot actions and child reactions is needed. The paper adopts a Markov decision process (MDP) as such a model, and selects the transition probabilities through an empirical approximation procedure called smoothing. Smoothing has been successfully applied in natural language processing (NLP) and identification where, similarly to the current paradigm, learning from small data sets is crucial. The goal of this paper is two-fold: (i) to describe our application of HRI, and (ii) to provide evidence that supports the application of smoothing for small data sets.

On the detection of solar panels by image processing techniques

Abstract: This paper proposes a solution based on computer vision to detect solar panels in images. It is based on the definition of a feature vector that characterizes portions of images that can be acquired with a standard camera and with no lighting restrictions. The proposal has been applied to a set of images taken in an operating photovoltaic plant and the results obtained demonstrate its validity and robustness. These results are meant to be used in later stages of a procedure for the optimization of energy efficiency.

A comparison between advanced model-free PID and model-based LQI attitude control of a quadcopter using asynchronous android flight data

Abstract: In this paper, we compare two control techniques for a DJI F450 quadcopter which is controlled and stabilized by a non-rooted onboard Android smartphone, without the aid of an external IMU. Specifically, we compare an advanced modelfree PID and LQI controller. Since Android is not a realtime system, the control commands and sensor measurements are subject to significant latencies, and hence the PID controller is modified to account for non-trivial measurement asynchronicities. We also show that some features can be added to the widely used baseline PID to obtain better performance in the presence of latencies and noise. Finally, we introduce a LQI controller which is capable of satisfactorily tracking a reference command in the presence of errors, noise, and model uncertainties, and compare the results to the PID controller.

On peak effects in discrete time linear systems

Abstract: It is well known that input-free stable linear systems with nonzero initial conditions may experience large deviations of the trajectory from the origin prior to converging to zero. Analysis of the transients of discrete-time systems is the subject of this paper. Simple LMI-based upper bounds on deviations are presented, and the same-flavor stabilizing feedback design procedure aimed at minimizing the peak is discussed. For companion-form systems, lower bounds are proposed for specific initial conditions; peaking effects for the norms of powers of Schur stable matrices are also analyzed.

Wireless system for occupancy modelling and prediction in smart buildings

Abstract: The dense instrumentation of future smart buildings enables the implementation of advanced control techniques which are aimed at the dual objectives of energy efficiency/cost savings and occupant comfort. One of the essential functions and prerequisite consists of robust dynamic occupancy detection and prediction which allows improved estimation of active thermal zones and internal loads, as compared to static schedule-based approaches. The paper presents the design and preliminary evaluation of a wireless system for embedded monitoring of occupancy states across the thermal zones of the building. The challenges of calibration, detection and prediction algorithms are discussed along with experimental outcomes. The infrared array sensor within the system offers improved detection performance compared to conventional PIR sensors while preserving user privacy in comparison to image processing approaches using security cameras. Several advantages of the proposed solution are also highlighted such as the low cost, flexibility, scalability and integration towards the building-wide automation system.

Modeling and predictive capacity adjustment for job shop systems with RMTs

Abstract: Demand fluctuations along with the requirements of cost-effectiveness, high customization, high product quality lead to a significant increase in manufacturing dynamics and complexity. These fluctuations disturb the work plan and lead to performance deterioration. Capacity adjustment is one of the major approaches to react on the fluctuations in demand. In this paper, we focus on improving the potential of current processes to react flexibly on these fluctuations on short term level by means of reconfigurable machine tools (RMTs). Furthermore, we propose a closed-loop feedback control system based on model predictive control (MPC) to align uncertainties and dynamics in terms of logistic objectives. The proposed method provides a new solution for manufacturers to optimize resources and improve the production performance in a dynamically changing environment. We demonstrate the effectiveness of the proposed method by simulation of a three-product four-workstation job shop system.

New concerns on fuzzy cognitive maps equation and sigmoid function

Abstract: Effective decision making is the fundamental factor for the appropriate operation of any system. Several methods have been developed in recent years, and Fuzzy Cognitive Maps (FCMs) is one of them. Basics of FCMs are briefly presented. They have been used in a variety of applications with a very good degree of success. All these years some of their drawbacks have been detected and discussed by several researchers, who are searching for ways to improve FCMs performance and broaden their implementation area. An effort towards the improvement of FCMs response is made, implementing for first time a new equation for the calculation of concept values and replacing the sigmoid function with Anti Windup control method. Results are presented and discussed, and further thoughts leading towards new research directions are presented.

Simple adaptive control of quadrotor attitude. Algorithms and experimental results

Abstract: In the paper the simple adaptive control approach is employed to designing the adaptive controllers of quadrotor angular motion. The adaptive controllers are synthesized based on the Implicit Reference Model (IRM) design technique. The “shunting method” (parallel feedforward compensation) is used to cope with the unmodelled plant dynamics. Analytical justification of system stability is made by employing the Passification method. Quality of the closed-loop IRM adaptive control system is studied and compared with that of the proportionalderivative (PD) non-adaptive control system experimentally on two degrees of freedom (2DOF) quadrotor testbed and in-flight tests of the QuadRoy quadrotor for nominal and parametrically perturbed cases.

Inverse kinematic control of an industrial robot used in Vessel-to-Vessel Motion Compensation

Abstract: An increased level of complex offshore load handling operations is expected due to an increased amount of floating wind turbines, remote fish farms, and autonomous shipping, and in general more advanced operations to be carried out at sea. A common problem for these applications is that both equipment and personnel have to be transported between two floating vessels at sea. An investigation of the Vessel-to-Vessel Motion Compensation (VVMC) problem may increase the efficiency and safety of such operations in the future. In this paper, a control algorithm has been developed and experimentally tested in the Norwegian Motion Laboratory featuring two Stewart platforms (SPs), an industrial robot, two Motion Reference Units (MRUs), and a Maritime Broadband Radio (MBR) used to establish a real-time wireless communication link. The resulting overall compensation error is measured to be no more than 41.9 mm during the experiments.

Vision based lane reference detection and tracking control of an automated guided vehicle

Abstract: In this works, a novel lane reference calculation approach is developed in order to define a safe and optimal trajectory to be tracked by a road vehicle. In a first step of the design, the lane boundaries are extracted using a vision based lane detection algorithm, which consists in an homography transformation that helps to extract features from the captured image frames in order to map a set of control points needed to reconstruct the lane path edges. In a second step, a real-time safe optimal reference trajectory is generated that explicitly takes into account the kinematics of a four-wheel road vehicle, a feasible trajectory delimited by the estimated lane boundaries is therefore generated, the planned trajectory fed to the vehicle controller enabling the road vehicle to tracked it. In order to have fast tracking of the reference trajectory, a non-singular sliding mode controller is implemented to achieve finite convergence of the tracking errors. The whole algorithm is applied on a four wheels automated guided vehicle model. Numerical simulations are presented to validate the proposed approach.

First-order frequency estimator for a pure sinusoidal signal

Abstract: This paper is devoted to frequency estimation of a pure sinusoidal signal. The new parameterization approach based on applying delay operators to a measurable signal is proposed. The result is the first order linear regression model with one parameter, which depends on the signal frequency. The estimation algorithm is basing on standard gradient approach. It is shown that the frequency estimation error converges to zero exponentially fast. The described method does not require measuring or calculating derivatives of the input signal and uses only one integrator. The efficiency of the proposed approach is demonstrated through the set of numerical simulations.

A cognitive stochastic approximation approach to optimal charging schedule in electric vehicle stations

Abstract: This paper proposes a Cognitive Stochastic Approximation (CSA)-based optimization method for charging an EV (electric vehicle) fleet, using a single, aggregate battery model. The charging station can either utilize the batteries of the parked vehicles to charge the vehicles before they leave, or can use power from the grid. The objective is to optimize the charging task with minimum energy costs, possibly taking into account price variations in the electricity price. The main advantage of the proposed approach is that it provides a nearly to optimal solution in the presence of uncertain charging/discharging dynamics. The method is evaluated through a numerical model of a grid-connected charging station. Four scenarios with different electricity price models are studied. The CSA optimization results are compared with the results obtained by a rule-based charging algorithm and by an open-loop optimal control algorithm: the results illustrate the advantages of the proposed CSA algorithm in minimizing the charging cost, satisfying the aggregate battery charge sustaining conditions and providing robust solutions in the presence of time-varying vehicle schedules.

Application of decaying boundary layer and switching function method thorough error feedback for sliding mode control on spacecraft's attitude

Abstract: Effective operation of small spacecraft implies processors with low cost, energy efficiency and low computational burdens while retaining accurate output tracking. This paper presents the extension of work in [1] on eliminating the chattering for Sliding Mode Control (SMC) using a decaying boundary layer design which is able to achieve these small spacecraft operation needs. The extension is applied on a spacecraft's attitude control, while orbiting the earth with angular velocity, ω 0 . In SMC, chattering is a main drawback as it can cause wear and tear to moving mechanical parts. Earlier work on a decaying boundary layer design was capable of reducing the chattering phenomena for a limited time only and hence this paper proposes a novel decaying boundary layer and switching function to improve the earlier version. The proposed technique is shown to reduce chattering permanently and also retain control output accuracy.

Fault tolerant incremental attitude control using online parameter estimation for a multicopter system

Abstract: This paper presents an attitude tracking incremental controller using a nonlinear reference model for a multicopter. The incremental controller is more robust compared to conventional nonlinear dynamic inversion controller against uncertainties due to its reduced model dependence. In order to further ensure robustness of the controller in the presence of actuator faults, the control effectiveness matrix is estimated online using gradient descent method. Furthermore, to achieve physically feasible trajectories, the gains of the nonlinear reference model are modified to account for the reduced achievable moment space.

Controlled electricity distribution network black start with energy storage system support

Abstract: This paper presents an optimal procedure for controlled electricity black start for electricity distribution grids in reaction to adverse events or malicious cyber attacks resulting in the interruption of the main power supply from transmission network. The islanded operation is supposed to be guaranteed by an electric energy storage system, which covers the network imbalance between demand and supply from distributed generation during the sequential reconnection of medium voltage branches. The objective is to restore the service to the consumers while avoiding violation of technical constraints in terms of storage power flow and battery capacity. The discussion of simulation results assesses the effectiveness of the proposed approach in the context of a simplified network model and gives rise to relevant remarks and requirements for further developments.