Volume 31 (2021): Issue 2 (June 2021)

We study the problem of active reduction of the influence of a disturbance on the output of a linear control system. We consider a system of linear differential equations under the action of an unknown disturbance and a control to be formed. Our goal is to design an algorithm for reducing the disturbance by means of an appropriate control on the basis of inaccurate measurements of the system phase coordinates. This algorithm should form a feedback control that would guarantee that the trajectory of a given system tracks the trajectory of the reference system, i.e., the system described by the same differential equations but with zero control and disturbance. We present an algorithm for solving this problem. The algorithm, based on the constructions of guaranteed control theory, is stable with respect to informational noises and computational errors.

For general boundary control systems in factor form some necessary and sufficient conditions for generation of an analytic exponentially stable semigroup are proposed in both direct and perturbation forms for comparison. The direct approach is applicable to operators with the numerical range satisfying certain additional conditions. In particular, it applies to operators similar to convexoids and therefore it generalizes previous results known for hyponormal operators. The perturbation result (indirect approach) is derived and formulated as an exponential stability robustness result using the frequence-domain considerations. It is expressed in terms of some estimates of the resolvent growth over the open right complex half-plane and compared with some recent results. The analysis is illustrated in detail with examples of an unloaded and loaded electric RC-transmission line with proportional negative feedback.

A Timoshenko system of a fractional order between zero and one is investigated here. Using a fractional version of resolvents, we establish an existence and uniqueness theorem in an appropriate space. Moreover, it is proved that lower order fractional terms (in the rotation component) are capable of stabilizing the system in a Mittag-Leffler fashion. Therefore, they deserve to be called damping terms. This is shown through the introduction of some new functionals and some fractional inequalities, and the establishment of some properties, involving fractional derivatives. In the case of different wave speeds of propagation we obtain convergence to zero.

A semi-analytical model is presented for the determination of the electric field in reactors used for cold atmospheric pressure plasma (CAPP) jet production, based on the concept of dielectric barrier discharge (DBD). These systems are associated with various applications in contemporary engineering, ranging from material processing to biomedicine, and at the same time they provide many challenges for fundamental research. Here, we consider a simplified system configuration of a single driven electrode, surrounding a thin dielectric tube, which does not contribute to the electric field, since the potential variation is immediate due to its negligible size. By employing the cylindrical coordinate system that perfectly fits the present plasma jet reactor, we separate the area of electric activity into three distinct domains according to the imposed external conditions, while our analysis is restricted to the electrostatic limit of Maxwell’s equations. To this end, cylindrical harmonic field expansions are used for the potential, which produce the corresponding electric fields in each subdomain. Due to the imposed mixed-type boundary value problem, additional linear terms are incorporated, leading to three possible analytical solutions of the physical problem under consideration. The efficiency of the method is demonstrated by comparing the final formulae with a numerical solution, followed by the relevant discussion.

This paper considers the problem of fault-tolerant control (FTC) and fault reconstruction of actuator faults for linear parameter varying (LPV) descriptor systems with time delay. A polytopic sliding mode observer (PSMO) is synthesized to achieve simultaneous reconstruction of LPV polytopic descriptor system states and actuator faults. Exploiting the reconstructed actuator faults and state estimates, a fault-tolerant controller is designed to compensate the impact of actuator faults on system performance by stabilizing the closed-loop LPV delayed descriptor system. Besides, the controller and PSMO gains are obtained throughout the resolution of linear matrix inequalities (LMIs) using convex optimization techniques. The developed PSMO could force the output estimation error to converge to zero in a finite time when the actuators faults are bounded through the reinjection of the output estimation error via a nonlinear switching term. Simulation results applied to a given numerical system are presented to highlight the superiority and effectiveness of the proposed approach.

This paper proposes a variance upper bound based interval Kalman filter that enhances the interval Kalman filter based on the same principle proposed by Tran et al. (2017) for uncertain discrete time linear models. The systems under consideration are subject to bounded parameter uncertainties not only in the state and observation matrices, but also in the covariance matrices of the Gaussian noises. By using the spectral decomposition of a symmetric matrix and by optimizing the gain matrix of the proposed filter, we lower the minimal upper bound on the state estimation error covariance for all admissible uncertainties. This paper contributes with an improved algorithm that provides a less conservative error covariance upper bound than the approach proposed by Tran et al. (2017). The state estimates are determined using interval analysis in order to enclose the set of all possible solutions of the classical Kalman filter consistent with the uncertainties.

Travel time estimation for freeways has attracted much attention from researchers and traffic management departments. Because of various uncertain factors, travel time on a freeway is stochastic. To obtain travel time estimates for a freeway accurately, this paper proposes two traffic sensor location models that consider minimizing the error of travel time estimation and maximizing the collected traffic flow. First, a dynamic optimal location model of the mobile sensor is proposed under the assumption that there are no traffic sensors on a freeway. Next, a dynamic optimal combinatorial model of adding mobile sensors taking account of fixed sensors on a freeway is presented. It should be pointed out that the technology of data fusion will be adopted to tackle the collected data from multiple sensors in the second optimization model. Then, a simulated annealing algorithm is established to find the solutions of the proposed two optimization models. Numerical examples demonstrate that dynamic optimization of mobile sensor locations for the estimation of travel times on a freeway is more accurate than the conventional location model.

This paper proposes a novel autonomous underwater vehicle path planning algorithm in a cluttered underwater environment based on the heat method. The algorithm calculates the isotropic and anisotropic geodesic distances by adding the direction and magnitude of the currents to the heat method, which is named the anisotropy-based heat method. Taking account of the relevant influence of the environment on the cost functions, such as currents, obstacles and turn of the vehicle, an efficient collision-free and energy-optimized path solution can be obtained. Simulation results show that the anisotropy-based heat method is able to find a good trajectory in both static and dynamic clutter fields (including uncertain obstacles and changing currents). Compared with the fast marching (FM) algorithm, the anisotropy-based heat method is not only robust, flexible, and simple to implement, but it also greatly saves time consumption and memory footprint in a time-variant environment. Finally, the evaluation criteria of paths are proposed in terms of length, arrival time, energy consumption, and smoothness.

Real life data often suffer from non-informative objects—outliers. These are objects that are not typical in a dataset and can significantly decline the efficacy of fuzzy models. In the paper we analyse neuro-fuzzy systems robust to outliers in classification and regression tasks. We use the fuzzy c-ordered means (FCOM) clustering algorithm for scatter domain partition to identify premises of fuzzy rules. The clustering algorithm elaborates typicality of each object. Data items with low typicalities are removed from further analysis. The paper is accompanied by experiments that show the efficacy of our modified neuro-fuzzy system to identify fuzzy models robust to high ratios of outliers.

A modified lazy learning algorithm combined with a relevance vector machine (MLL-RVM) is presented to address a data-driven modelling problem for a gasification process inside a united gas improvement (UGI) gasifier. During the UGI gasification process, the measured online temperature of the produced crude gas is a crucial aspect. However, the gasification process complexities, especially severe changes in the temperature versus infrequent manipulation of the gasifier and the unknown noise in collected data, pose difficulties in dynamics process descriptions via conventional first principles. In the MLL-RVM, a novel weighted neighbour selection method is adopted based on the proposed dynamic cost functions. Moreover, the RVM is utilized in the implementation and design of the proposed online local modelling owing to its short test time and sparseness. Furthermore, the leave-one-out cross-validation technique is used for local model validation, by which the modelling performance is further improved. The MLL-RVM is applied to a series of real data collected from a pragmatic UGI gasifier, and its effectiveness is verified.

Beta-thalassemia is an autosomal recessive blood disorder characterized by abnormalities in the synthesis of β globin. Together with α globin, it is a subunit of globin protein, called hemoglobin, located inside our red blood cells to deliver oxygen from the lungs to all of the tissues throughout our body. Thereby, individuals with β-thalassemia will often feel limp due to a lack of oxygen dissolved in their blood. In this paper, a finite state automaton to detect and classify β globin gene mutations using its DNA sequence is constructed. Finite state automata have a close connection to an algebraic structure, that is, a monoid. Together with the theory of the syntactic monoid, we present a methodology to minimize the number of the internal states of an automaton to have minimal state automata. Therefore, a minimal state automaton can be constructed to detect β globin gene mutation causing the β-thalassemia disease. We have developed a MATLAB program to conduct the appropriate simulations.

Group signature schemes play a vital role in protecting identity privacy of a member of a group who signs a message using the group signature. However, in the existing group signature schemes the centralized group manager has control over all the participants, and these managers can be malicious. They may take a biased decision when there is a dispute among the group members or while revealing the identity of a group member. To overcome the trust issues related to centralized group managers and to improve user privacy, a decentralized group signature scheme (DGSS) is proposed by decentralizing the role of the group manager. The proposed scheme will be more suitable for decentralized environments like a blockchain. Security analysis along with the proof of correctness is also provided for the proposed scheme. A framework for a blockchain-based e-auction protocol using the DGSS is also proposed in this paper.