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To solve the control problem of time-varying state-scale nonlinear systems whose initial state is not affected by settling time, fixed-time convergence algorithms are proposed for first-order systems and higher-order systems in this paper. First, a scalar model is used to illustrate how the time-varying feedback parameter can guarantee that the system achieves asymptotic stability while achieving finite-time convergence, and it is proved that the settling time obtained in this paper is only related to the prescribed boundary. This allows us to design the settling time with an appropriate parameter based on the prescribed boundary. To exhibit the effectiveness and extensibility of the proposed algorithm for first-order scalar systems, the results are subsequently extended to general higher-order systems based on the backstepping method. By introducing numerical simulation results, this paper verifies that the proposed algorithm will make the system achieve asymptotic stability and its output can converge to a given boundary, regardless of the system's initial states. [ABSTRACT FROM AUTHOR]

This paper designs event-triggered delay compensation and disturbance attenuation control algorithms for multi-agent systems. First, through predictor-based model reduction method, an unknown large input delay can be compensated and delay-free dynamics is obtained. Based on the transformed systems, predictive descriptor observers are proposed to obtain estimated system states and sensor faults. Then, an event-triggered fault-tolerant algorithm is designed to save energy resources for communication among the agents while attenuating the disturbances. Moreover, it is validated that the systems do not exhibit Zeno behaviour using the designed event-triggering sequence. Numerical simulations are conducted to demonstrate the effectiveness of designed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, an encryption scheme, based on a double deterministic phase mask and the Yang-Gu algorithm, is proposed. It consists of double random phase encoding and the Yang-Gu algorithm in Fresnel and Fourier transform, respectively. The random phase masks are replaced with deterministic phase masks that are constructed using chaotic logistic and cubic maps. The encrypted image obtained by the scheme in one-way binary modulation is real and more convenient for storage and transmission. The validation of the proposed cryptosystem is carried out using numerical simulations on greyscale images of Man, DICOM and CUH binary images. The proposed scheme is investigated for its robustness through statistical analysis using histograms and correlation plots. The scheme is also tested for its resistance against occlusion, noise and cryptographic attacks. The proposed cryptosystem is found to be highly sensitive to its private key and Fresnel parameters and therefore secure. [ABSTRACT FROM AUTHOR]

Grammatical knowledge has often been investigated in wug tests, in which participants inflect pseudo-words. It was argued that in inflecting these pseudo-words, speakers apply their knowledge of word formation. However, it remains unclear what exactly this knowledge is and how it is learned. According to one theory, the knowledge is best characterised as abstractions that specify how units are combined. Another theory maintains that it is best characterised by memory-based analogy. In both cases the knowledge is learned by association based on positive evidence alone. In this paper, we model the classification of pseudo-words to Maltese plurals using a shallow neural network trained with an error-driven learning algorithm. We demonstrate that the classifications mirror those of Maltese speakers in a wug test. Our results indicate that speakers rely on gradient knowledge of a relation between the phonetics of whole words and plural classes, which is learned in an error-driven way. [ABSTRACT FROM AUTHOR]

This paper investigates the data-driven consensus tracking problem for heterogeneous multi-agent systems. All of the heterogeneous dynamics, disturbances and measurement noises are considered for the output tracking consensus of nonlinear multi-agent network under a data-driven scheme. A dynamic linearisation method is introduced to deal with the nonlinear nonaffine structures of the agents and a heterogeneous linear data model for the agent is obtained due to the heterogeneous dynamics of the agent itself. On this basis, a distributed model-free adaptive predictive control (DMFAPC) algorithm is constructed by the use of consensus errors and the robustness analysis is conducted in the presence of disturbances. Further, the results are modified by using measured outputs to replace the system outputs, and thus a measured output-based DMFAPC is presented to deal with the omnipresent measurement noises in practical applications. The two proposed DMFAPC methods are data driven since no mechanistic model information is required wherein. And thus, no unmodelled dynamics affects the consensus performance. Instead, they can improve the control performance by utilising additional predictive information. The two proposed DMFAPC methods are verified using numerical simulations. [ABSTRACT FROM AUTHOR]

Finding feasible solutions to motion planning and control problem of robotic systems in different environments with various applications is an active area of research. This article presents a new solution to the motion planning and control problem of a three-dimensional articulated mobile manipulator comprising a car-like mobile platform and a three-dimensional n-link articulated arm using the Lyapunov-based control scheme. The motion of the system is described as twofold: first, the car-like mobile platform moves from an initial position to its pseudo-target, and second, when the mobile platform is within some predefined distance from the pseudo-target, the end-effector of the robot arm is attracted to its designated target. Therefore, presenting a new 2-Step Algorithm in this paper for dual movement of the articulated mobile manipulator in 3D. In addition, a workspace cluttered with fixed spherical and rod obstacles of random sizes and positions is considered in this research. For the mobile manipulator to avoid an obstacle, the Minimum Distance Technique is adapted where a point on the robot that is closest to an obstacle will avoid the obstacle. The convergence of the two bodies and the stability of the mechanical system are guaranteed by the Lyapunov's direct method. The continuous nonlinear control laws proposed from the control scheme also take into account all mechanical singularities and velocity limitations associated with the system. Theoretical proofs and computer simulations validate the new continuous, acceleration-based, nonlinear, time-invariant control laws. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive generalised function projective synchronisation scheme of fractional-order chaotic systems with different dimensions and orders and fully unknown parameters is presented. On the basis of the Lyapunov method of fractional-order systems, a stability theorem of the fractional-order system with non-identical orders is proven. Using the fractional-order controller and adaptive control theory, sufficient conditions for synchronisation and unknown parameters update rules are obtained. Theoretical analysis and numerical simulations are provided to verify the validity of the proposed scheme. Moreover, synchronisation results are applied to secure communication via modified chaotic masking (MCM) method. The unpredictability of the scaling function matrix and the use of fractional-order systems with different orders can increase the security of the cryptosystem. The security analysis shows that the introduced algorithm has large key space, high sensitivity to encryption keys, higher security and the acceptable encryption speed. [ABSTRACT FROM AUTHOR]

This paper proposes a new hybrid robust control method for control of an autonomous underwater vehicle. Fractional sliding mode control (FSMC) is robust against external disturbances. The main drawback of the FSMC method is creating a chattering phenomenon. Therefore, a compound control method is applied, which benefits in both robustness of the FSMC method and chattering elimination by the new control algorithm. A random noise is applied in order to verify the robustness of the proposed control method. The stability of FSMC and proposed compound control method has been verified by Lyapunov theory. The effectiveness of the proposed control method is compared with FSMC, which numerical simulation results confirm the best performance of the proposed control method. [ABSTRACT FROM AUTHOR]

In this paper, a dimension reduction method via general orthogonal polynomials and multiorder Arnoldi algorithm is proposed, which focuses on the topic of structure-preserving for k-power bilinear systems. The main procedure is using a series of expansion coefficient vectors of each state variables in the space spanned by general orthogonal polynomials that satisfy a recurrence formula to generate a projection based on multiorder Arnoldi algorithm. The resulting reduced-order model not only matches a desired number of expansion coefficients of the original output but also retains the topology structure. Meanwhile, the stability is well preserved under some certain conditions and the error bound is also given. Finally, two numerical simulations are provided to illustrate the effectiveness of our proposed algorithm in the views of accuracy and computational cost. [ABSTRACT FROM AUTHOR]

Monte Carlo computation has been widely applied in the field of dynamic systems. This paper focuses on the general framework in the implementation of sequential importance sampling by combining redrawing, resampling and rejection control simultaneously. The proposed algorithm is named as Redrawing Resampling Rejection Controlled Sequential Importance Sampling (RR-RC-SIS). It can reduce sampling computation and meanwhile maintain the diversity of random samples. Theoretical basis is given to prove that RR-RC-SIS has advantages in comparison with Rejection Controlled Sequential Importance Sampling. It also has practical value as illustrated in numerical simulation on blind deconvolution problem in digital communications. [ABSTRACT FROM AUTHOR]

In this paper, the distributed convex optimisation problem of the multi-agent system over an undirected network is investigated, in which the local objective function of each agent is only known by itself. To reduce the communication consumption between agents, a state-based dynamic event-triggered algorithm with positive minimum inter-event time (MIET) is provided, where the aperiodic information communication only occurs at some discrete triggering time instants. Moreover, the sampling control technology is combined into the previous event-triggered algorithm for verifying the event-triggered condition at every sampling time, instead of continuous access. Finally, several numerical simulations are presented for illustrating and verifying the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper has discussed the study of the performance of dynamic voltage restorer (DVR) taking different voltage sag conditions in the supply voltage of the distribution system for linear and non-linear load. DVR is employed to mitigate the voltage sag. Proportional and integral (PI) controller is used for the control of DVR to mitigate the voltage sag. Synchronous reference frame theorybased control algorithm has been implemented for generating reference voltages. In this paper, an efficient improved particle swarm optimisation technique for optimizing the gain parameters of the PI controller has been proposed. The performance and suitability of DVR is validated through MATLAB simulation results and use of Sim Power Systems Blocksets. Integral squared error is implemented to check the performance and suitability of DVR. Extensive results of the proposed method are presented and compared with conventional Ziegler Nichols and genetic algorithm methods of tuning gain parameters of PI controllers. [ABSTRACT FROM AUTHOR]

In this paper, the mechanism of the human–computer game is investigated with the help of multi-agent systems (MASs) and reinforcement learning (RL). The game is formulated as a bipartite consensus problem while the interactions among humans and computers are modelled as a multi-agent system over a coopetition network. The coopetition network associated with the multi-agent system is represented by a signed graph, where positive/negative edges denote cooperative/competitive interactions. We assume the decision mechanism of the agents are model free and each agent has to make a distributed decision by learning the input and output data from himself/itself and his/its neighbours. The individual decision is developed with the neighbours' state information and a performance index function. A policy iteration (PI) algorithm is proposed to solve the Hamilton-Jacobi-Bellman equation and obtain the optimal decision strategy. Furthermore, an actor-critic neural network is adopted to approximate the performance index and the optimal decision strategy in an online manner. The simulation results are finally given to validate the proposed reinforcement learning approach. [ABSTRACT FROM AUTHOR]

The impulse response function (IRF) identification of linear mechanical systems is important in many engineering applications. This paper proposes a novel IRF identification method of linear systems based on Kautz basis expansion with multiple poles. In order to reduce the parameters to be identified, the IRF is expanded in terms of orthogonal Kautz functions with multiple poles, and the poles in Kautz functions should be optimised. This allows the identification of IRF for linear mechanical systems operated under more than one mode, such as systems under the white noise excitation or the swept frequency excitation with a wide range of frequency, and can improve the identification accuracy. Furthermore, based on the backpropagation through-time technique and the expectation maximisation algorithm, a pole optimisation algorithm is presented in this paper. The simulation studies verify the effectiveness of the proposed IRF identification method. [ABSTRACT FROM AUTHOR]

This paper is concerned with the distributed optimisation problem over a multi-agent network, where the objective function is described by a sum of all the local objectives of agents. The target of agents is to collectively reach an optimal solution while minimising the global objective function. Under the assumption that the information exchange among agents is depicted by a sequence of time-varying undirected graphs, a distributed optimisation algorithm with uncoordinated time-varying step-sizes is presented, which signifies that the step-sizes of agents are not always uniform per iteration. In light of some reasonable assumptions, this paper fully conducts an explicit analysis for the convergence rate of the optimisation method. A striking feature is that the algorithm has a geometric convergence rate even if the step-sizes are time-varying and uncoordinated. Simulation results on two numerical experiments in power systems show effectiveness and performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper is concerned with the distributed secure estimation problem for a cyber-physical system (CPS) with fading measurements and false data injection (FDI) attacks, where the fading probability of measurements is assumed to be individual for each sensor, and the FDI attacks occur at the physical system layer to intensively modify the system's state. For improving security of the system, an attack detection mechanism is proposed based on an FDI attack observer constructed by minimising the upper bound of observation error covariance. Then a complete distributed state estimation algorithm is given to provide locally reliable estimation. A sufficient condition is provided to guarantee boundedness of the estimation error covariance. Finally, the effectiveness of the proposed method is illustrated by numerical simulation and practical example simulation. [ABSTRACT FROM AUTHOR]

In this paper, direction of arrival (DOA) estimation of multiple signals with coprime array is investigated and signal subspace fitting (SSF) method is linked to the coprime array, which achieves a better DOA estimation performance than the traditional uniform array. While the SSF method requires expensive computational cost in the case of multiple signals due to the multidimensional global angular searching, we propose a successive SSF (S-SSF) algorithm from a computationally efficient perspective. In the proposed algorithm, we employ rotational invariance and coprime property to obtain the initial estimates. Then, via a successive scheme, we transform the traditional multidimensional global angular searching problem into one-dimensional partial angular searching one. Consequently, the computational complexity has been significantly reduced. Specifically, the proposed S-SSF algorithm can obtain almost the same DOA estimation performance as SSF but with remarkably lower complexity. Finally, Cramer-Rao Bound (CRB) is provided and numerical simulations demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

A log-likelihood ratio (LLR) test on the single Mth check node of an LDPC decoder is utilised to develop an improved low complexity stopping criterion that overall outperforms well-known stopping criteria. The average number of iterations required at low SNRs is significantly reduced without sacrificing the BER performance. At high SNRs, the proposed algorithm can be modified to adaptively switch off to minimise its impact on the BER. Simulation results are presented for the rate 1/2 (288, 576) and rate 1/2 (1152, 2304) WiMAX 802.16e LDPC codes with binary phase shift keying (BPSK) over an AWGN channel for well-known criteria and the proposed algorithms in this paper. The performance of a Genie algorithm that would with hindsight stop the decoder to minimise the BER is used to compare all algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes an image encryption scheme using the combination of chaos, hyper-chaos, and DNA sequence operation. The proposed scheme performs three stages of encryption operations. Those are selection-level hyper-chaotic sequence based DNA-shuffling operation, key-image based DNA-diffusion operation, and hyper-chaotic sequence based DNA-shuffling operation. The advantages of this scheme are higher key space, higher confusion or randomness of pixels, higher sensitivity to the keys and plaintext pixels, stronger resistivity to the noises, and lossless encryption and decryption. Moreover, selection-level hyper-chaotic sequence based DNA-shuffling operation generates more complexities in the confusion process which increases the strength of encryptions and decryptions. The computer simulation and security analyses confirm the good encryption results of the proposed scheme and strong resistivity to the commonly used attacks. [ABSTRACT FROM AUTHOR]

This paper investigates the parameter estimation problem for multivariate output-error systems perturbed by autoregressive moving average noises. Since the identification model has two different kinds of parameters, a vector and a matrix, the gradient algorithm cannot be used directly. Therefore, we decompose the original system model into two sub-models and proceed the identification problem by the collaboration between the two sub-models. By employing the gradient search and determining the optimal step-sizes, we present an auxiliary model based two-stage projection algorithm. However, in order to alleviate the sensitivity to the noise, we reselect the step-sizes and derive the auxiliary model based two-stage stochastic gradient (AM-2S-SG) algorithm. Based on the AM-2S-SG algorithm, an auxiliary model based two-stage multi-innovation stochastic gradient algorithm is proposed to generate more accurate estimates. Finally, numerical simulations are provided to demonstrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to develop a detection algorithm for the first jump point in sampling trajectories of jump-diffusions which are described as solutions of stochastic differential equations driven by α-stable white noise. This is done by a multivariate Lagrange interpolation approach. To this end, we utilize computer simulation algorithm in MATLAB to visualize the sampling trajectories of the jump-diffusions for various combinations of parameters arising in the modeling structure of stochastic differential equations. [ABSTRACT FROM AUTHOR]

Improving tractive effort is a very complex task in locomotive design. It requires the development of not only mechanical systems but also power systems, traction machines and traction algorithms. At the initial design stage, traction algorithms can be verified by means of a simulation approach. A simple single wheelset simulation approach is not sufficient because all locomotive dynamics are not fully taken into consideration. Given that many traction control strategies exist, the best solution is to use more advanced approaches for such studies. This paper describes the modelling of a locomotive with a bogie traction control strategy based on a co-simulation approach in order to deliver more accurate results. The simplified and advanced modelling approaches of a locomotive electric power system are compared in this paper in order to answer a fundamental question. What level of modelling complexity is necessary for the investigation of the dynamic behaviours of a heavy-haul locomotive running under traction? The simulation results obtained provide some recommendations on simulation processes and the further implementation of advanced and simplified modelling approaches. [ABSTRACT FROM AUTHOR]

Quick and agile walking is indispensable for humanoid robots in the RoboCup soccer competition. So, this paper presented a walking motion design of humanoid robot soccer team called Apollo3D. In this paper, we employed a model which is based on double linear inverted pendulum with a predictive control to generate a motion trajectory of the robot’s trunk in the premise of keeping dynamic balance of robots. Parameters for walk are optimized for maximum speed through an approach of Q-learning algorithm. Then, the cerebellar model articulation controller method is employed to optimize the walking parameters to keep dynamic balance of robots. With this walking skill, ultimately, we realized rapidly and stably omnidirectional walk of biped robot in complex and dynamic environment. Experimental results have revealed Our team Apollo3D employed this walking skill, and won the first place in RoboCup 2013 3D simulation competition by winning 53 goals. [ABSTRACT FROM AUTHOR]

In this paper we introduce a new method for detecting outliers in a set of proportions. It is based on the construction of a suitable twoway contingency table and on the application of an algorithm for the detection of outlying cells in such table. We exploit the special structure of the relevant contingency table to increase the efficiency of the method. The main properties of our algorithm, together with a guide for the choice of the parameters, are investigated through simulations, and in simple cases some theoretical justifications are provided. Several examples on synthetic data and an example based on pseudo-real data from biological experiments demonstrate the good performances of our algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a new robust adaptive controller is investigated to force an underactuated surface marine vessel to follow a predefined parameterised path at a desired speed, despite actuator saturation and the presence of model uncertainties as well as environmental disturbances induced by waves, wind and sea-currents. To ensure robustness of the path-following controller, time-varying constraint on the off-track error (i.e. the maximal distance from the ship to the reference path) is considered. To address the off-track error constraint the tan-barrier Lyapunov function is incorporated with the control scheme, where the idea of auxiliary design system introduced in Chen, Sam, and Ren (2011) is adopted and its states are used in combination with backstepping and Lyapunov synthesis to adaptive tracking control design with guaranteed stability. Furthermore, the command filters are adopted to implement physical constraints on the virtual control laws so that analytic differentiation of the virtual control laws is avoided. We show that the proposed robust adaptive control law is able to guarantee semi-global uniform ultimate bounded stability of the closed-loop system. Numerical simulations and experimental results are carried out to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper considers the attitude tracking problem for the generic hypersonic vehicle (GHV). First, the second-order nonlinear GHV attitude model is introduced. Second, by utilising the direct parametric approach, a modified parametric controller composed of the parametric state proportional plus derivative feedback controller and the feedforward controller is proposed. With application of the first part, the closed-loop system is a constant linear system with desired eigenstructures, and the second part is used to ensure the asymptotic tracking property of the outputs of the GHV attitude system. Since the control input of the established GHV attitude model is moment command, an algorithm that transforms the moment commands into the fins deflection command is proposed. Finally, the effectiveness of the proposed parametric controller and transform algorithm is illustrated by the numerical simulation results. [ABSTRACT FROM AUTHOR]

This paper focuses on the resource allocation problem(RAP) with constraints under a fixed general directed topology by using the distributed sub-gradient algorithm with event-triggered scheme in multi-agent systems, where each agent owns a cost function and its state value is bounded. The distributed sub-gradient algorithm aims to minimise the total cost by a distributed manner while achieving an optimal solution. Unlike centralised methods, the triggering condition and algorithm for each agent are fully decentralised. At each instant of time, each agent updates its state by employing the states which are collected from itself and its neighbouring agents at their last triggering time. In order to illustrate the effectiveness of the proposed sub-gradient algorithm with event-triggered control law, one simulation example is presented before the conclusion. [ABSTRACT FROM AUTHOR]

Stochastic media problems present a severe impact on the properties of the electromagnetic fields that usually deterministic methods tend to overlook. In this paper, we present a finite-difference time-domain (FDTD) technique, which is free from time step restrictions and manages to efficiently calculate the statistics of the involved fields in one realization. The proposed algorithm is a variation of the Stochastic FDTD method and exhibits satisfactory outcomes in high density grids. Numerical results verify the validity of our scheme compared to other methods, such as the Stochastic FDTD and the Monte Carlo techniques. [ABSTRACT FROM AUTHOR]

Both the lack of the downward regulation capacity and the transmission capacity limit have led to considerable wind power curtailments in China. The battery energy storage system (BESS) provides a new solution to reduce the wind power curtailments due to its relatively high energy density and flexible installed location. In this paper, a new bi-level programming model is proposed to optimize the size and location of BESS for reducing the wind curtailments. The revenues of BESS, when it is located either at the wind farm or the load center, are considered comprehensively. Moreover, a numerical optimization algorithm is developed to solve the proposed model. Simulation results demonstrate the validity of the proposed model and developed algorithm. They also reveal that the BESS located at the load center offers more net profit compared to the one located at the wind farm. Subsequently, sensitivity analysis is performed to assess the effect of key parameters on the optimal values of BESS. [ABSTRACT FROM AUTHOR]

The Multilevel inverter topologies (MLIs) are increasingly being used, which is the preferred choice in many industrial Medium-voltage (MV) applications. The inherent low switching frequency in medium voltage neutral point clamped MLI (NPC-MLI) fed industrial drives represent the power quality issues and filter-design challenges. In this paper, two multilevel pulse width modulation (PWM) methods; multi-carrier sine PWM (MCSPWM) and space vector PWM (SVPWM) has been considered for NPC-MLI. Both the methods are compared and analyzed based on different constraints such as voltage profile, total harmonic distortion (THD), common mode voltage (CMV) and neutral point fluctuation (NPF). The results convey that the SVPWM method is superior when compared with the MCSPWM algorithms. Analytical and simulation results achieved by using the MATLAB/Simulink for control systems are experimentally verified using FPGA-SPARTAN - III generation - 3AN -XC3S400 with a 2KW NPC-MLI fed adjustable speed - drive system. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid modeling approach for granular flow-like applications is presented. The approach allows to switch for a priori fixed points in time between the different levels of description which are the microscopic and macroscopic scale, respectively. Based on the numerical discretization of the models, the switching procedure is able to interpret information on individual objects as density distributions and vice versa. In particular, the reverse direction, i.e. from a macroscopic to a microscopic perspective, requires the solution of a nonlinear least squares problem subject to further constraints. Simulation results are given and demonstrate the good performance of the algorithm in the case of material and pedestrian flow models. [ABSTRACT FROM AUTHOR]

A robust time delay estimation (TDE)-based direction-finding method for multipath signals in the presence of impulsive noise is proposed in this paper. Compared to traditional algorithms, developed for Gaussian distribution, this method performs more robust in the presence of non-Gaussian noise. The robust TDE is first obtained by utilising the matched filtering in-space which is less sensitive to outliers. Then direction-of-arrival (DOA) is estimated by ‘steering vector fitting’ approach based on the delay estimates. An iterative approach is employed to estimate each multipath component separately and improve the estimation accuracy. The method is developed in-space, which minimises the-norm of residual fitting error vector instead of the Frobenius norm. The number of antennas required can be less than that of the sources. Simulation results show that the method outperforms several outliers-resistant algorithms both in cases of impulsive noise and Gaussian distribution noise. [ABSTRACT FROM AUTHOR]

In this paper, we propose an algorithm for simultaneous indoor self-localization and Sound Source Localization (SSL) using a swarm of microphone-embedded-micro-quadrocopters (size10 cm). Micro-quadrocopters are extremely noisy, have low CPU power and cannot lift heavy equipment: the small payload of each micro-quadrocopter (5 g) only allows us to equip it with one microphone in addition to the inbuilt motion sensors. To perform robust SSL despite these issues, we propose three functions: (1) Self-localization of the quadrocopters using sound landmarks placed in the environment, and simultaneous localization of unknown sound sources; (2) Sound source detection; (3) Distributed data fusion based on noisy information from all members of the swarm. To achieve these, we propose three algorithms that are robust to noise, can perform with a varying number of quadrocopters, and do not rely on GPS nor motion capture to allow indoor operations: (1) A sound-based Unscented Kalman Filter (UKF) for self-localization of each quadrocopter; (2) A peak-based algorithm for sound source detection; (3) A distributed SSL algorithm for swarms with consensus-based integration using a new filter termed Unscented Kalman Consensus Filter (UKCF). We evaluated the proposed methods in real world and in simulated environments. The preliminary results show that the sound-based UKF represents an improvement of 37% on position estimation precision compared to basic dead reckoning approaches, even when the theoretical assumptions are violated; the distributed UKCF gives an improvement of 85% on SSL compared to a single-sensor approach in simulation. [ABSTRACT FROM PUBLISHER]

The initial value problems (IVPs) of Caputo fractional-order differential equations are very important in control systems modelling and simulation. A series of numerical algorithms are proposed in the paper in solving systematically various kinds of Caputo equations. For linear Caputo equations, the divergent problems of the existing Taylor auxiliary function are pointed out, and two effective algorithms are presented to transform the nonzero IVPs into zero ones, where the closed-form solutions are available. Furthermore, algorithms for nonlinear Caputo equation are also presented, aiming at finding numerical solutions to all kinds of Caputo equations. Error analysis for the proposed algorithms is provided, and numerical examples are presented to illustrate the accuracy and effectiveness of the algorithms. [ABSTRACT FROM AUTHOR]

Selection of appropriate predictors for right censored time to event data is very often encountered by the practitioners. We consider the ℓ1penalized regression or “least absolute shrinkage and selection operator” as a tool for predictor selection in association with accelerated failure time model. The choice of the penalizing parameter λ is crucial to identify the correct set of covariates. In this paper, we propose an information theory-based method to choose λ under log-normal distribution. Furthermore, an efficient algorithm is discussed in the same context. The performance of the proposed λ and the algorithm is illustrated through simulation studies and a real data analysis. The convergence of the algorithm is also discussed. [ABSTRACT FROM AUTHOR]

In this paper, we present an algorithm for identifying two-dimensional (2D) causal, recursive and separable-in-denominator (CRSD) state-space models in the Roesser form with deterministic–stochastic inputs. The algorithm implements the N4SID, PO-MOESP and CCA methods, which are well known in the literature on 1D system identification, but here we do so for the 2D CRSD Roesser model. The algorithm solves the 2D system identification problem by maintaining the constraint structure imposed by the problem (i.e. Toeplitz and Hankel) and computes the horizontal and vertical system orders, system parameter matrices and covariance matrices of a 2D CRSD Roesser model. From a computational point of view, the algorithm has been presented in a unified framework, where the user can select which of the three methods to use. Furthermore, the identification task is divided into three main parts: (1) computing the deterministic horizontal model parameters, (2) computing the deterministic vertical model parameters and (3) computing the stochastic components. Specific attention has been paid to the computation of a stabilised Kalman gain matrix and a positive real solution when required. The efficiency and robustness of the unified algorithm have been demonstrated via a thorough simulation example. [ABSTRACT FROM AUTHOR]

This paper presents a composite control strategy integrating adaptive sliding-mode control and the linear quadratic regulator (LQR) technology for a wheeled inverted pendulum (WIP) vehicle system. The system can be partitioned into an actuated rotational subsystem and an underactuated longitudinal subsystem based on the different control input in the mathematical model. In particular, the instability analysis of zero dynamic for the underactuated longitudinal subsystem is investigated in detail using the feedback linearisation technology. Then, an adaptive sliding-mode control is designed for the trajectory tracking, where an adaptive algorithm is developed to handle with the parameter uncertainties. In addition, the LQR technique is employed to guarantee zero dynamics stability so as to achieve simultaneously the vehicle body stabilisation at the upright position. Simulation results show the good performance and strong robustness of the proposed control schemes. [ABSTRACT FROM AUTHOR]

This paper presents the cooperative control of a team of autonomous underwater vehicles (AUVs) in the presence of obstacles under environmental disturbance. A leader–follower formation control based on optimisation algorithms using communication topology is designed to navigate towards the target in the presence of obstacles. CLONAL selection optimisation algorithm may be employed as team controller by providing the optimal position to hierarchical control strategy for each AUV incorporating learning, memory and affinity. But unfortunately CLONAL selection optimisation algorithm fails to avoid obstacles because some of the lymphocytes become memory cells of the system. The above prematurity may be overcome by using artificial potential fields and ant colony optimisation technique combined with CLONAL selection optimisation algorithm. The efficacy of the proposed optimisation is verified through MATLAB simulation and the result confirmed the robustness and proficiency of proposed technique over CLONAL selection optimisation algorithm. [ABSTRACT FROM PUBLISHER]

For an uncontrollable system, adding leaders and adjusting edge weights are two methods to improve controllability. In this paper, the controllability of multi-agent systems under directed topologies is studied, especially on the leader selection problem and the weight adjustment problem. For a multi-agent system, necessary and sufficient algebraic conditions for controllability with fewest leaders are proposed. To improve controllability by adjusting edge weights, the system is supposed to be structurally controllable, which holds if and only if the communication topology contains a spanning tree. It is also proved that the number of the fewest edges needed to be assigned on new weights equals the rank deficiency of controllability matrix. In addition, a leader selection algorithm and a weight adjustment algorithm are presented. Simulation examples are provided to illustrate the theoretical results. [ABSTRACT FROM AUTHOR]

This paper considers the minimum-energy control problem for a class of time-varying systems with multiple state and input delays. First, a state transform matrix is presented. By using the transform matrix, a system with multiple state and input delays is converted to a formal equivalent one without delay. Then, the optimal problem of the novel system is solved by using the maximum principle. Analytical expressions of the optimal control law and optimal performance are given by two formulas with respect to the state transform matrix. At last, an algorithm is given to solve the analytical expression of the state transform matrix. Simulation results show that the design algorithm is efficient and easy to implement. [ABSTRACT FROM AUTHOR]

In this paper, we deal with dual problem of a class of non-classical parabolic equations in which the boundaries are time-dependent function instead of fixed values, which arise from Ductal carcinomain situ(DCIS). In the direct problem part, on using the several transformation and heat potential theory, we established the integral form of solution and proved the existence and uniqueness of solution. Then we consider the inverse problem of finding the control parameter of known moving boundaries, which means determining the potential function of model from incisional biopsy information in the view of DCIS. Algorithm and numerical simulation for both problems are included to demonstrate the validity and applicability of solutions. [ABSTRACT FROM AUTHOR]

In this paper, an energy-based control methodology is proposed to satisfy the Reynolds three rules in a flock of multiple agents. First, a control law is provided that is directly derived from the passivity theorem. In the next step, the Number of Neighbours Alignment/Repulsion algorithm is introduced for a flock of agents which loses the cohesion ability and uniformly joint connectivity condition. With this method, each agent tries to follow the agents which escape its neighbourhood by considering the velocity of escape time and number of neighbours. It is mathematically proved that the motion of multiple agents converges to a rigid and uncrowded flock if the group is jointly connected just for an instant. Moreover, the conditions for collision avoidance are guaranteed during the entire process. Finally, simulation results are presented to show the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

The present paper develops a procedure for simulating multivariate data with count and continuous variables with a pre-specified correlation matrix. The count and continuous variables are assumed to have Poisson and normal marginals, respectively. The data generation mechanism is a combination of the normal to anything principle and a newly established connection between Poisson and normal correlations in the mixture. A step-by-step algorithm is provided and its performance is evaluated using two simulated and one real-data scenarios. [ABSTRACT FROM PUBLISHER]

In this paper, a blind signal separation (BSS) methodology for simultaneously received multisystem frequency-overlapped signals in a single-channel (SC) electromagnetic surveillance system is proposed using fast independent component analysis (FastICA) in a dynamical embedding (DE) framework. Firstly, an appropriate DE matrix is constructed out of a series of delay vectors from the SC recording. The lag-time and the dimensional of embedding matrix setting principal are introduced in details. Next, multiple independent components (ICs) are calculated by decomposing the embedding matrix through FastICA algorithm, and ICs can be regarded as a convenient expansion basis of the original signals. Then, these ICs are projected back into the measurement space. After that, these projected ICs are classified and used for recovering the sources of interest based on their independent nature and their power density spectrum. Numerical simulation results obtained in evaluating the proposed methodology’s performance confirmed the effectiveness of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

In this paper, we introduce a new powerful scientific paradigm to understand natural and cultural processes. This new paradigm is based on two fundamental keywords: Data, as representative sample of the process we need to analyze, and Artificial Adaptive Systems, as a new mathematical technique able to make explicit the nonlinearity embedded in the process. We will try to make explicit these concepts analyzing how the distribution of events into the physical space may reveal the hidden logic connecting these events together. [ABSTRACT FROM PUBLISHER]

Railway vehicles adopt mechatronic devices to maximize the traction/braking effort. These systems often use complex algorithms that require long experimental validation tests on track. The possibility to perform the same tests on a roller-rig gives the opportunity to simplify the validation activity, under safe conditions, and to reduce the costs. The main challenge is to be able to reproduce the same adhesion conditions on a roller-rig with respect to the track, especially in degraded conditions. First, the paper shows experiments performed to reproduce degraded adhesion on a conventional roller-rig for a single wheelset. Then, an innovative roller-rig is proposed in order to reproduce the effect of the passage of several wheelsets on the track. [ABSTRACT FROM AUTHOR]

In this paper we introduce the sequential precedence test (SPT) for testing the equality of two continuous distribution functions, against the stochastically ordered alternative. This procedure replaces the classical precedence test (PT), used in life-testing experiments, by a sequence of tests which are applied at the failure times of one of the samples. This allows the possibility of stopping the experiment earlier than the PT. By means of extensive Monte Carlo simulations and real data, we show that the proposed methodology results in substantial saving of experimental time and cost, without compromising in power. Algorithms for the implementation of the SPT are included. [ABSTRACT FROM AUTHOR]

We focus on the control and the synthesis of secure timed systems which are modelled as timed automata. The security property that the system must satisfy is anon-interferenceproperty. Intuitively, non-interference ensures the absence of any causal dependency from a high-level domain to a lower level domain. Various notions of non-interference have been defined in the literature, and in this paper, we focus onstrong non-deterministic non-interference(SNNI) and two (bi)simulation-based variants thereof (cosimulation-based SNNI and bisimulation-based SNNI). These properties and their extensions have been mostly studied in the context of discrete event systems, while it is now well-known that time is an important attack vector against secure systems. At the same time, there is an obvious interest in going beyond simple verification to control problems: to be able to automatically make systems secure. We consider non-interference properties in the challenging setting of control of dense-time systems specified bytimed automataand we study the two following problems: (1) check whether it is possible to find a sub-system so that it is non-interferent; if yes, (2) compute a (largest) sub-system which is non-interferent. We exhibit decidable sub-classes for these problems, assess their theoretical complexities and provide effective algorithms based on the classical framework of timed games. [ABSTRACT FROM AUTHOR]

In this paper, a hierarchical PO-MOESP subspace identification algorithm for directed acyclic graphs (DAGs) is presented. The state of every node and the structure of the graph are assumed to be unknown. The method computes a hierarchical partition of the DAG by using projection matrices typically used in subspace identification methods applied in cascade. Then, the column space of the observability matrix of every node is sequentially estimated by projecting away the input-output data from the previous levels. A past input–output instrumental variable approach is adopted to deal with the noise. The topology of multilevel DAGs is revealed by dedicated projections applied on every level. Moreover, we provide a brief study of a more general class of DAGs that can be accurately represented by multilevel DAGs of reduced interconnection structure. Finally, three simulation examples are provided to show the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

This paper investigates a multiple-vacation M/M/1 warm-standby machine repair problem with an unreliable repairman. We first apply a matrix-analytic method to obtain the steady-state probabilities. Next, we construct the total expected profit per unit time and formulate an optimisation problem to find the maximum profit. The particle swarm optimisation (PSO) algorithm is implemented to determine the optimal number of warm standbysS* and the service rate μ* as well as vacation rate ν* simultaneously at the optimal maximum profit. We compare the searching results of the PSO algorithm with those of exhaustive search method to ensure the searching quality of the PSO algorithm. Sensitivity analysis with numerical illustrations is also provided. [ABSTRACT FROM AUTHOR]