Showing papers presented at "Mediterranean Conference on Control and Automation in 2009"

Activity recognition using a wrist-worn inertial measurement unit: A case study for industrial assembly lines

Abstract: As wearable sensors are becoming more common, their utilization in real-world applications is also becoming more attractive. In this study, a single wrist-worn inertial measurement unit was attached to the active wrist of a worker and acceleration and angular speed information was used to decide what activity the worker was performing at certain time intervals. This activity information can then be used for proactive instruction systems or to ensure that all the needed work phases are performed. In this study, the selected activities were basic tasks of hammering, screwing, spanner use and using a power drill for screwing. In addition, a null activity class consisting of other activities (moving around the post, staying still, changing tools) was defined. The performed activity could then be recognized online by using a sliding window method to divide the data into two-second intervals and overlapping two adjacent windows by 1.5 seconds. Thus, the activity was recognized every half second. The method used for the actual recognition was the k nearest neighbor method with a specific distance boundary for classifying completely new events as null data. In addition, the final class was decided by using a majority vote to classifications of three adjacent windows. The results showed that almost 90 percent accuracy can be achieved with this kind of setting; the activity-specific accuracies for hammering, screwing, spanner use, power drilling and null data were 96.4%, 89.7%, 89.5%, 77.6% and 89.0%, respectively. In addition, in a case with completely new null events, use of the specific distance measure improved accuracy from 68.6% to 82.3%.

Simultaneous state and unknown inputs estimation with PI and PMI observers for Takagi Sugeno model with unmeasurable premise variables

Abstract: In this paper, a proportional integral (PI) and a proportional multiple integral observer (PMI) are proposed in order to estimate the state and the unknown inputs of nonlinear systems described by a Takagi-Sugeno model with unmeasurable premise variables. This work is an extension to nonlinear systems of the PI and PMI observers developed for linear systems. The state estimation error is written as a perturbed system. First, the convergence conditions of the state estimation errors between the system and each observer are given in LMI (Linear Matrix Inequality) formulation. Secondly, a comparison between the two observers is made through an academic example.

Input-output finite-time stability of linear systems

Abstract: When only the input-output behavior of a dynamical system is of concern, usually Bounded-Input Bounded-Output (BIBO) stability is studied, for which several results exist in literature. The present paper investigates the analogous concept in the framework of Finite Time Stability (FTS), namely the Input-Output FTS. A system is said to be IO finite time stable if, assigned a bounded input class and some boundaries in the output signal space, the output never exceeds such boundaries over a prespecified (finite) interval of time. FTS has been already investigated in several papers in terms of state boundedness, whereas this is the first work dealing with the characterization of the input-output behavior. Sufficient conditions are given, concerning the class of L 2 and L ∞ input signals, for the analysis of IO-FTS and for the design of a static state feedback controller, guaranteeing IO-FTS of the closed loop system. Finally, the applicability of the results is illustrated by means of two numerical examples.

Adaptive optimal controllers based on Generalized Policy Iteration in a continuous-time framework

Abstract: In this paper we present two adaptive algorithms which offer solution to the continuous-time optimal control problem for nonlinear, affine in the inputs, time-invariant systems. Both algorithms were developed based on the Generalized Policy Iteration technique and involve adaptation of two neural network structures namely Actor, providing the control signal, and Critic, performing evaluation of the control performance. Despite the similarities, the two adaptive algorithms differ in the manner in which the adaptation takes place, required knowledge on the system dynamics, and formulation of the persistence of excitation requirement. The main difference is that one algorithm uses sequential adaptation of the actor and critic structures, i.e. while one is trained the other one is kept constant, while for the second algorithm the two neural networks are trained synchronously in a continuous-time fashion. The two algorithms are described in detail and proof of convergence is provided. Simulation results of applying the two algorithms for finding the optimal state feedback controller of a nonlinear system are also presented.

Control design for bilinear systems with a guaranteed region of stability: An LMI-based approach

Abstract: This paper deals with the problem of stabilizing a bilinear system with unstable open-loop part by means of state feedback control. The implicit objective is to provide an estimate of the region of stability of the closed-loop system. The proposed procedure can be decomposed into two convex optimization problems described in terms of LMIs: i) Given a polytope which bounds the values of the state, containing the origin, find a stabilizing state feedback control law and an associate region of stability as large as possible inside the polytope. ii) For a solution of the first problem, find the largest polytope containing the ellipsoid such that the stability conditions hold. By iterating these two steps, constructive conditions are given to compute a state feedback control that maximizes the estimate of the region of stability. The results are illustrated by means of examples.

On Iterative Learning Control of parabolic distributed parameter systems

Abstract: The Iterative Learning Control (ILC) technique is extended to distributed parameter systems governed by parabolic partial differential equations (PDEs). ILC arises as an effective method to approach constrained optimization problems in PDE systems. We discuss both P-type and D-type ILC schemes for a distributed parameter system formulated as a general linear system ∑(A,B,C,D) on a Hilbert space, in which the system operator A generates a strongly continuous semigroup. Under the assumption of identical initialization condition (IIC), conditions on the learning parameters are obtained to guarantee convergence of the P-type and D-type ILC schemes. Numerical simulations are presented for a 1D heat conduction control problem solved using ILC based on semigroup analysis. The numerical results show the effectiveness of the proposed ILC schemes.

Game theory model for selective forward attacks in wireless sensor networks

Abstract: The problem of detecting malicious nodes in wireless sensor networks is considered. Since current security mechanisms are inadequate for wireless sensor networks, we must develop a new framework to detect malicious nodes using Zero-Sum game approach and selective node acknowledgements in the forward data path. The first few sections of the paper provide a game model with probability of cost required to defend the nodes and the subsequent sections derives the model to detect malicious nodes using the probability of acknowledgment at source.

State and unknown input estimation for nonlinear systems described by Takagi-Sugeno models with unmeasurable premise variables

Abstract: This paper presents a new method to synthesize observers for continuous time nonlinear systems described by Takagi-Sugeno (TS) model with unmeasurable premise variables. First, convergence conditions are established in order to guarantee the convergence of the state estimation error. These conditions are given in Linear Matrix Inequality (LMI) formulation. Secondly, a classical Proportional Integral Observer (PIO) is extended to the considered nonlinear systems in order to estimate the state and the unknown inputs (UI).

Detecting coverage holes in wireless sensor networks

Abstract: Wireless sensor network coverage completeness is an important Quality of Service measure. It is frequently assumed that events occurring in the sensor field will always be detected. However, this is not necessary the case, particularly if there are holes in the sensor network coverage. This paper introduces a novel method for detection and relative localization of sensor network coverage holes in coordinate-free networks assuming availability of a network communication graph. We identify sensor nodes that bound coverage holes, called “hole boundary nodes”, by processing information embedded in a communication graph, which is non-planar in general. We create a hole-equivalent planar graph preserving a number and position of holes. Finally, we build a planar simplicial complex, called maximal simplicial complex, which contains the information regarding coverage holes. The proposed method is applicable for both coordinate-available and coordinate-free networks. Two implementation strategies for hole detection are provided, and they are each analyzed to compare runtime and accuracy. Simulation results show effectiveness of the hole detection algorithms.

Functional observers for linear descriptor systems

Abstract: This paper is concerned with the design of functional observers for linear time-invariant descriptor systems. The order of these observers is equal to the dimension r of the functional to be estimated. Sufficient conditions for the existence and stability of these observers are given. The obtained results extend those given in [8] for the standard systems. Continuous and discrete time systems are considered.

Optimal control of affine nonlinear discrete-time systems

Abstract: In this paper, direct neural dynamic programming techniques are utilized to solve the Hamilton Jacobi-Bellman equation in real time for the optimal control of general affine nonlinear discrete-time systems. In the presence of partially unknown dynamics, the optimal regulation control problem is addressed while the optimal tracking control problem is addressed in the presence of known dynamics. Each design entails two portions: an action neural network (NN) that is designed to produce a nearly optimal control signal, and a critic NN which evaluates the performance of the system. Novel weight update laws for the critic and action NN's are derived, and all parameters are tuned online. Lyapunov techniques are used to show that all signals are uniformly ultimately bounded (UUB) and that the output of the action NN approaches the optimal control input with small bounded error. Simulation results are also presented to demonstrate the effectiveness of the approach.

A distributed parameter control approach to optimal filtering and smoothing with mobile sensor networks

Abstract: In this paper we present a framework to address filtering and smoothing problems for distributed parameter systems when mobile (dynamic) sensors are used to provide system measurements. This framework can be used for systems governed by parabolic and hyperbolic partial differential equations and hence has application to a diverse set of problems such as estimating locations of biological and chemical sources, target tracking and estimation.We formulate the problems as hybrid systems on infinite dimensional spaces (coupled systems of partial, ordinary and delay differential equations) and use infinite dimensional theory to develop computational algorithms for the problems. A simple numerical example illustrates the approach.

Design and implementation of a structured flight controller for a 6DoF quadrotor using quaternions

Abstract: This paper describes a practical implementation of simple control algorithms on a 6DoF quadrotor flying in an uncontrolled environment and being equipped with inexpensive sensors. A significant number of control algorithms that apply dynamic inversion or backstepping techniques on simplified state variable models of the vehicle dynamics are present in the literature, but they are only tested in simulations where real-life issues like sensor noise and precision, vibrations, measurement reference frames, and modeling errors are not included completely or even partially. Here it is shown that if the practical implementation is done correctly, even simple PD controllers can ensure the stability of the quadrotor platform in hover.

The importance of control in wind turbine design and loading

Abstract: The importance of the controller in determining the design loads of a wind turbine has been recognised for many years. This paper will discuss this topic from the following perspectives:

Non-local memory hysteresis in a pneumatic artificial muscle (PAM)

Abstract: In a system using PAMs, a big research effort has been carried out to solve the control problem, in which the nonlinear dynamics of a PAM were left behind as a disturbance to that system. The inherent dynamics in a PAM is due to its constitutional materials which cause hysteresis during cyclic contraction/extension. Prior knowledge of the hysteresis behavior in a PAM may simplify the associated control system. In this paper, the hysteretic behavior of a PAM is investigated and the results show much similarity to the presliding regime in the friction of mechanical contacting elements. The PAM hysteresis is thus generalized and represented by a lumped-parameter model, which is useful for control design.

Hybrid control of quadrotor

Abstract: This paper presents the solution to a quadrotor control problem based on a discrete automaton that, in combination with classical PID controllers, creates a hybrid control system. Some modifications to the known and widely used mathematical model of quadrotor aircrafts are also discussed in this paper. The paper shows an open loop control concept that flies the aircraft into looping. Finally, the performance of the suggested control scheme is tested with simulations.

Tuning fuzzy PID controllers using ant colony optimization

Abstract: Ant colony optimization (ACO) is one of the swarm intelligence (SI) techniques. It is a bio-inspired optimization method that has proven its success through various combinatorial optimization problems. This paper proposes an ant colony optimization algorithm for tuning fuzzy PID controllers. First, the design of typical Takagi-Sugeno (TS) fuzzy PID controllers is investigated. The tuning parameters of these controllers have physical meaning which makes its tuning task easier than conventional PID controllers. Simulation examples are provided to illustrate the efficiency of the proposed method.

A novel approach to structural load control using intelligent actuators

Abstract: The recent trend towards large multi-MW wind turbines resulted in the role of the control system becoming increasingly important. The extension of the role of the controller to alleviate structural loads has motivated the exploration of novel control strategies, which seek to maximise load reduction by exploiting the blade pitch system. The reduction of blade fatigue loads through individual blade pitch control is one of the examples. A novel approach to reduction of the unbalanced rotor loads by pitch control is presented in this paper. Each blade is equipped with its own actuator, sensors and controller. These local blade control loops operate in isolation without a need of communication with each other. The single blade control approach to regulation of unbalanced rotor loads presented in this paper has an important advantage of being relatively easy to design and tune. Furthermore, it does not affect the operation of the central controller and the latter need not be re-designed when used in conjunction with the single blade controllers. Their performance is assessed using BLADED simulations.

Scheduling independent tasks: Bee Colony Optimization approach

Abstract: The problem of static scheduling of independent tasks on homogeneous multiprocessor systems is studied in this paper. The problem is solved by the Bee Colony Optimization (BCO). The BCO algorithm belongs to the class of stochastic swarm optimization methods. The proposed algorithm is inspired by the foraging habits of bees in the nature. The BCO algorithm was able to obtain the optimal value of objective function in all small to medium size test problems. The CPU times required to find the best solutions by the BCO are acceptable.

Diagnosis of an Inertial Measurement Unit based on set membership estimation

Abstract: This paper compares two diagnostic techniques applied to MEMS sensors of an Inertial Measurement Unit (IMU). The sensors considered in this study are a triaxis accelerometer and a triaxis magnetometer. Note that rate gyros measurements are not considered here. The goal of the IMU is to sense the attitude of a rigid body on which it is embedded. The measurement equations are non-linear in the attitude parameters which are first estimated from five over six measurements. Then, each measurement equation together with the estimated parameters, provides an predicted measurement. Lastly, the residual is computed and analyzed with a signature table to detect and isolate the fault. Two estimation techniques are implemented. The first approach is based on parameter estimation with nonlinear optimization technique while the second one makes use of set membership estimation. Both techniques are applied to the detection of faults in the IMU fixed on a quadrotor in quasi-static mouvement.

Adaptive regulation - Rejection of unknown multiple narrow band disturbances

Abstract: The paper presents a methodology for feedback adaptive control of active vibration systems in the presence of time varying unknown multiple narrow band disturbances. A direct adaptive control scheme based on the internal model principle and the use of the Youla-Kucera parametrization is proposed. This approach is comparatively evaluated with respect to an indirect adaptive control scheme based on the estimation of the disturbance model. The evaluation of the methodology is done in real time on an active suspension system and on an active vibration control system using an inertial actuator.

Bisection algorithm of increasing algebraic connectivity by adding an edge

Abstract: For a given graph (or network) G, consider another graph G′ by adding an edge e to G. We propose a computationally efficient algorithm of finding e such that the second smallest eigenvalue (algebraic connectivity, λ 2 (G′)) of G′ is maximized. Theoretically, the proposed algorithm runs in O(4mnlog(d/∈)), where n is the number of nodes in G, m is the number of disconnected edges in G, d is the difference between λ 3 (G) and λ 2 (G), and ∈ ≪ 0 is a sufficiently small constant. However, extensive simulations show that the practical computational complexity of the proposed algorithm, O(5.7mn), is nearly comparable to that of a simple greedy-type heuristic, O(2mn). This algorithm can also be easily modified for finding e which affects λ 2 (G) the least.

Benchmark problems of adaptive control revisited by L 1 adaptive control

Abstract: In this paper we revisit two benchmark problems of adaptive control, Rohrs' example and the two-cart example, with the recently developed L 1 adaptive controller. The paper includes an overview of two L 1 architectures, one in a state-feedback setting and another one in an output-feedback setting, which are suitable for the control of these two systems respectively. Also, we analyze fundamental differences between conventional adaptive control and the L 1 adaptive control, and show, purely from qualitative considerations, some of the advantages of this new control philosophy.

A unifying specification logic for cyber-physical systems

Abstract: The varieties of possible interaction between computational systems and physical environments is at the heart of a new modeling paradigm called cyber-physical systems. In order to model and control these interactions it necessary to present the fundamental properties of physical environments in a formalism compatible with the computational structures, usually in a formal logic or an algebraic calculus. In this paper, we propose a model as a step towards reasoning about the problems of uncertainty and surprise in the context of cyber-physical systems operating under mixed human/autonomous control. In controlling embedded devices, human operators are, in most cases, assisted by automated controllers (like driving assistance systems and automatic pilots). A new issue appeared in many applications is to model the automatic controllers which are user centric, i.e. the controllers are carrying a runtime monitoring of the system behaviour in its environment, they inform and warn the user on safety hazardous situation and they take action only when the user fails to react. A robust controller should be able to operate in open, random environments and to assist the human operator in case of appearance of surprising, possible catastrophic situations.

Investigation on the choice of the initial temperature in the Simulated Annealing: A mushy state SA for TSP

Abstract: It is a long time that the Simulated Annealing (SA) procedure is introduced as a non-derivative based optimization for solving NP-hard problems. Improvements from the original algorithm in the recent decade mostly concentrate on combining its initial algorithm with some heuristic methods. This is while modifications to the method are rarely happened to the initial conditions from which the annealing schedule starts. There are several parameters in the process of annealing the adjustment of which affects the overall performance. This paper focuses on the initial temperature and proposes a lower temperature with low energy to speed up the process. Such an annealing indeed starts from a mushy state rather than a quite liquid molten material. The mushy state characteristics depends on the problem that SA is being applied to solve. In this paper the Mushy State Simulated Annealing (MSSA) is applied to the Traveling Salesman Problem (TSP). The mushy state may be obtained by some simple methods like crossover elimination. A very fast version of The Wise Experiencing Traveling Salesman is also applied to initiate SA by a low-energy-low-temperature state. This fast method results in quite accurate solutions compared to other recent novel methods.

Adaptive backstepping control of a wheeled mobile robot

Abstract: This paper proposes an adaptive nonlinear controller to stabilize an autonomous wheeled mobile robot. The controller equations are obtained following a backstepping approach. The robot model is divided into two parts: a state space model with intermediate control inputs and algebraic nonlinear equations relating the true and the intermediate control inputs. The robot parameters are assumed unknown. First, a suitable change of variable is applied to the traditional robot dynamics to reveal the strict feedback structure of this state space model. ext, a three-step adaptive backstepping control design method is applied to obtain the intermediate control input expressions. Finally the true control inputs are found by solving iteratively the nonlinear equations that relates intermediate and true control inputs. The adaptation algorithms are based on the projection method and guarantee that estimated parameters converge and remain inside predefined domains. The proposed design strategy is tested in simulation. The results show good tracking performances despite large parameter variations.

Vision-based autonomous navigation and landing of an unmanned aerial vehicle using natural landmarks

Abstract: This paper presents the design and implementation of a vision-based navigation and landing algorithm for an autonomous helicopter. The vision system allows to define target areas from a high resolution aerial or satellite image to determine the waypoints of the navigation trajectory or the landing area. The helicopter is required to navigate from an initial position to a final position in a partially known environment using GPS and vision, to locate a landing target (a helipad of a known shape or a natural landmark) and to land on it. The vision system, using a feature-based image matching algorithm, finds the area and gives feedbacks to the control system for autonomous landing. Vision is used for accurate target detection, recognition and tracking. The helicopter updates its landing target parameters owing to vision and uses an on board behavior-based controller to follow a path to the landing site. Results show the appropriateness of the vision-based approach that does not require any artificial landmark (e.g., helipad) and is quite robust to occlusions, light variations and seasonal changes (e.g., brown or green leaves).

Testing controls to mitigate fatigue loads in the controls Advanced Research Turbine

Abstract: Wind turbines are complex, nonlinear, dynamic systems forced by aerodynamic, gravitational, centrifugal, and gyroscopic loads. The aerodynamics of wind turbines is nonlinear, unsteady, and complex. Turbine rotors are subjected to a complicated three-dimensional (3-D) turbulent wind inflow field with imbedded coherent vortices that drive fatigue loads and reduce lifetime. Design of control algorithms for wind turbines must account for multiple control objectives. Future large multi-megawatt turbines must be designed with lighter weight structures, using active controls to mitigate fatigue loads, maximize energy capture, and add active damping to maintain stability for these dynamically active structures operating in a complex environment. Researchers at the National Renewable Energy Laboratory are designing, implementing, and testing advanced controls to maximize energy extraction and reduce structural dynamic loads. These control designs are based on a linear model of the turbine that is generated by specialized modeling software. This paper describes testing of a control algorithm to mitigate blade, tower, and drivetrain loads using advanced state-space control methods. The controller uses independent blade pitch to regulate the turbine's speed in Region 3, mitigate the effects of shear across the rotor disk, and add active damping to the tower's first fore-aft bending mode. Additionally, a separate generator torque control loop is designed to add active damping to the tower's first side-side mode and the first drivetraintorsion mode. This paper discusses preliminary implementation and field tests of this controller in the Controls Advanced Research Turbine at the National Renewable Energy Laboratory. Also included are preliminary comparisons of the performance of this controller to results from a typical baseline Proportional-Integral-Derivative controller designed with just Region 3 speed regulation as the goal.

Nonlinear superheat and capacity control of a refrigeration plant

Abstract: This paper proposes a novel method for superheat and capacity control of refrigeration systems. A new low order nonlinear model of the evaporator is developed and used in a backstepping design of a nonlinear controller. The stability of the proposed method is validated theoretically by Lyapunov analysis and experimental results shows the performance of the system for a wide range of operating points. The method is compared to a conventional method based on a thermostatic superheat controller.

Total Mass TCI driven by parametric estimation

Abstract: This paper presents the Total Mass Target Controlled Infusion algorithm. The system comprises an On Line tuned Algorithm for Recovery Detection (OLARD) after an initial bolus administration and a Bayesian identification method for parametric estimation based on sparse measurements of the accessible signal. To design the drug dosage profile, two algorithms are here proposed. During the transient phase, an Input Variance Control (IVC) algorithm is used. It is based on the concept of TCI and aims to steer the drug effect to a predefined target value within an a priori fixed interval of time. After the steady state phase is reached the drug dose regimen is controlled by a Total Mass Control (TMC) algorithm. The mass control law for compartmental systems is robust even in the presence of parameter uncertainties. The whole system feasibility has been evaluated for the case of Neuromuscular Blockade (NMB) level and was tested both in simulation and in real cases.