Distributed estimation and control of multiple nonholonomic mobile agents with external disturbances

This paper investigates the tracking control problem of nonholonomic multiagent systems with external disturbances. For this purpose, distributed finite time controllers (DFCs) based on the terminal sliding mode method are proposed to ensure that states of the agents track the states of the target in a finite time. Furthermore, a distributed estimator (DE) is designed for each agent to estimate the target's states. The stability analysis of DFCs and DE is also considered. Simulation examples demonstrate the promising performance of the proposed algorithms.     

Global asymptotic stabilization for input-delay chained nonholonomic systems via the static gain approach

In this paper, a novel control strategy is proposed for asymptotically stabilizing chained nonholonomic systems with input delay. Firstly, by using the input-state-scaling technique and the static gain control method, the stabilization control problem of such systems is transformed into designing two gain parameters to stabilize a class of generalized feedback systems with state delay. Then, based on the Lyapunov–Krasovskii theorem, the stability analysis of the closed-loop systems is achieved by the appropriate selection of the gain parameters, and the state and output feedback controllers are constructed simultaneously. An illustrative example is also provided to demonstrate the effectiveness of the proposed strategy.     

Self-triggered MPC for trajectory tracking of unicycle-type robots with external disturbance

In this paper, a self-triggered model predictive controller (MPC) strategy for nonholonomic vehicle with coupled input constraint and bounded disturbances is presented. First, a self-triggered mechanism is designed to reduce the computation load of MPC based on a Lyapunov function. Second, by designing a robust terminal region and proper parameters, recursive feasibility of the optimization problem is guaranteed and stability of the the closed-loop system is ensured. Simulation results show the effectiveness of proposed algorithm.     

Turing patterns in a diffusive epidemic model with saturated infection force

In this paper, we investigate the complex dynamics of a reaction–diffusion epidemic model with a saturated infection force analytically and numerically. We give the stability of the constant positive steady–states and the nonexistence/existence of nonconstant positive steady–states of the model which shows that, if the diffusion coefficients are properly chosen, the model exhibits stationary Turing pattern as a result of diffusion. Via numerical simulations, we present the evolutionary processes that involve organism distribution and the interaction of spatially distributed infection with local diffusion, and find that the model dynamics exhibits a diffusion-controlled formation growth of holes, stripes and spots pattern replication. In the viewpoint of epidemiology, we must regulate and control the parameters in the special range to control the disease.     

A novel adaptive robust control approach for underactuated mobile robot

Underactuated mobile robot (UMR) is a typical nonlinear underactuated system with nonholonomic and holonomic constraints. Based on the model of UMR, we propose a novel adaptive robust control to control the UMR and compensate the uncertainties from the view of constraint-following. The uncertainties, which are (possibly fast) time-varying and bounded, include modeling error, initial condition deviation, friction force and other external disturbances. However, the bounds are unknown. To estimate the bounds of the uncertainties, we design an adaptive law which is of leakage type. The uniform boundedness and the uniform ultimate boundedness of the proposed control are verified by Lyapunov method. Furthermore, the effectiveness of the control is shown via numerical simulation of a case.     

Integrated design of switching control and mobile actuator/sensor guidance for a linear diffusion process

This paper is concerned with the exponential stabilization problem for a class of diffusion processes described by a linear parabolic partial differential equation (PDE) using mobile collocated actuators and sensors. Each collocated actuator/sensor pair is capable of moving within the respective spatial domain divided in advance and a mode indicator function is employed to indicate the different modes for all actuator/sensor pairs according to whether each actuator/sensor pair is static or mobile. By utilizing Lyapunov direct method, an integrated design of switching controllers and mobile actuator/sensor guidance laws for the diffusion process is developed such that the resulting closed-loop system is exponentially stable. Finally, numerical simulations are presented to illustrate the effectiveness of the proposed design method.     

Denial of service attack and defense method on load frequency control system

The application of the network technology in the power grid makes the Load Frequency Control (LFC) system more vulnerable to various kinds of network attacks. The Denial of Service (DOS) attack can block the data collected by the Phasor measurement unit from being transmitted to the LFC center, thereby affecting the decision of the control center and generation of control signals, and can not adjust the frequency of the power grid timely. Aiming at the DOS attack on LFC, a defense method based on data prediction is proposed. Through the combination of the deep learning algorithm and the Extreme Learning Machine (ELM) algorithm, the Deep auto-encoder Extreme Learning Machine (DAELM) algorithm combines the advantages of the fast speed of the extreme learning machine and the advantages of high accuracy of the deep learning. We can predict and supplement the lost data based on the DAELM algorithm, and ensure the normal operation of the LFC system, thus can prevent DOS attacks. The experiments verified the effectiveness of the proposed method.     

Subset selection for visualization of relevant image fractions for deep learning based semantic image segmentation

Semantic image segmentation is a challenging problem from image processing where deep convolutional neural networks (CNN) have been applied with great success in the recent years. It deals with pixel-wise classification of an input image, dividing it into regions of multiple object classes. However, CNNs are opaque models. Given a trained CNN, it is hard to tell which information encoded in the input image is important for the network to perform segmentation. Such information could be useful to judge whether a trained network learned to segment in a plausible way or how its performance can be improved.For a trained CNN, we formulate an optimization problem to extract relevant image fractions for semantic segmentation. We try to identify a subset of pixels that contain the relevant information for the segmentation of one selected object class. In experiments on the Cityscapes dataset, we show that this is an easy way to gain valuable insight into a CNN trained for semantic segmentation. Looking at the relevant image fractions, we can identify possible limits of a trained network and draw conclusions about possible improvements.     

Control of switch-sharing-based multilevel inverter suitable for photovoltaic applications

Owing to the higher amount and the better quality of power transferred, three-phase multilevel inverters have strengthened their presence in low-power photovoltaic (PV) applications. However, the existing topologies investigated for low-power PV systems are basically meant for medium- and high-power applications, thus making them underutilized for low-power range. In order to address this shortcoming, this paper proposes a three-phase multilevel inverter with a switch-sharing capability and its control solution. It combines the characteristics of the two-level full-bridge topology and the diode-clamped multilevel structure. As a result, the inverter can be used with full capacity and without being underutilized, and significantly improves the output quality. A control strategy that is able to maximize the full potential of the inverter is developed. A digital proportional-integral (PI) controller with a new tuning algorithm is designed to effectively deal with the load changes. The result reveals that the load current is always retained at the intended quality level despite the variations in load. The performance of the inverter is validated from the experimental work conducted on a laboratory prototype under closed-loop conditions.     

Compressed sensing for real measurements of quaternion signals

The paper concerns compressed sensing methods in the quaternion algebra. We prove that it is possible to uniquely reconstruct – by ?₁ norm minimization – a sparse quaternion signal from a limited number of its real linear measurements, provided the measurement matrix satisfies so-called restricted isometry property with a sufficiently small constant. We also provide error estimates for the approximated reconstruction of a non-sparse quaternion signal from noisy and noiseless data.     

Discrete Legendre polynomials-based inequality for stability of time-varying delayed systems

This paper proposes Discrete Legendre Polynomial(DLP)-based inequality by solving the best weighted approximation of a given time series. The proposed inequality could significantly reduce the conservativeness in stability analysis of systems with constant or interval time-varying delays. Also former well-known integral inequities, such as discrete Jensen inequality, discrete Wirtinger-based inequality, are both included in the proposed DLP-based inequality as special cases with lower-order approximation. Stability criterion with less conservatism is then developed for both constant and time-varying delayed systems. Several numerical examples are given to demonstrate the effectiveness and benefit of the proposed method.     

Modification of Mikhailov stability criterion for fractional commensurate order systems

In this paper we present the modification of the Mikhailov stability criterion for linear fractional commensurate order systems. The modification consists in determining the appropriate measure for the total argument change depending on the highest fractional order ${\alpha }_n=n\alpha$ of the system and not only on the integer n as stated in the literature. The validity of the result is illustrated by means of several examples.     

Nonlinear trajectory tracking controller for a class of robotic vehicles

A global state feedback tracking controller for a class of vehicles, namely marine vehicles, hovercrafts and indoor airships is considered in this paper. The control algorithm uses a velocity transformation of the vehicle equations of motion. It is shown that this algorithm is suitable for control of fully actuated systems and leads to fast response. This property arises from the fact that the dynamical couplings in the vehicle are taken into account in the control gain matrix. A Lyapunov-like function is proposed for the stability analysis of the system under the controller. The algorithms robustness issue is considered too. Numerical simulations are given to illustrate effectiveness of the approach.     

Robust observer design by sign-stability for the monitoring of population systems

Monitoring problem in population ecology can be formalized as observer design for the population system in question: Supposing that we observe only certain species considered indicators, we want to recover or estimate the whole state process of the population system, where the state vector is usually composed from the biomasses of the single populations. In the present paper, for stably coexisting population systems, a new approach to the design of the corresponding observer system is proposed which, from the knowledge of the observed indicator(s), estimates the state process with exponential convergence. In the usual observer design, an auxiliary matrix, the so-called gain matrix must be found that guarantees the mentioned exponential convergence. The novelty is in that due to the required sign-stability (or qualitative stability) of the interaction pattern, the designed observer system (i.e. the gain matrix) is robust against quantitative changes in the inter- and intra-specific interactions. (Here the interaction pattern is described by a matrix having the signs as entries, indicating the quality of the interactions within and between the considered species.) In other words, under sign-stability conditions, in the observer design the same gain matrix can be used even if, due to environmental changes, the intensities of certain interactions suffer a quantitative change in the meanwhile. The requirement of sign-stability of the interaction pattern can be considered rather natural, since in a stably coexisting population system, it means for example that a predator–prey relation does not change into a prey–predator interaction, and interactions neither appear nor disappear within the system. Our approach to robust observer design is illustrated on model population systems, such as trophic chains of type resource-producer-primary consumer-secondary consumer and Lotka–Volterra system with vertical structure. For the latter system a Lyapunov function is also constructed that guarantees the global asymptotic stability of the positive equilibrium of the considered model.     

New modeling approach of secondary control layer for autonomous single-phase microgrids

In a microgrid (MG) topology, the secondary control is introduced to compensate for the voltage amplitude and frequency deviations, mainly caused by the inherent characteristics of the droop control strategy. This paper proposes an accurate approach to derive small signal models of the frequency and amplitude voltage at the point of common coupling (PCC) of a single-phase MG by analyzing the dynamics of the second-order generalized integrator-based frequency-locked loop (SOGI-FLL). The frequency estimate model is then introduced in the frequency restoration control loop, while the derived model of the amplitude estimate is introduced for the voltage restoration loop. Based on the obtained models, the MG stability analysis and proposed controllers’ parameters tuning are carried out. Also, this study includes the modeling and design of the synchronization control loop that enables a seamless transition from island mode to grid-connected mode operation. Simulation and practical experiments of a hierarchical control scheme, including traditional droop control and the proposed secondary control for two single-phase parallel inverters, are implemented to confirm the effectiveness and the robustness of the proposal under different operating conditions. The obtained results validate the proposed modeling approach to provide the expected transient response and disturbance rejection in the MG.     

Formation control and collision avoidance for a class of multi-agent systems

In this paper, a control scheme is proposed for a group of elliptical agents to achieve a predefined formation. The agents are assumed to have the same dynamics, and communication among the agents are limited. The desired formation is realized based on the reference formation and the mapping decision. In the control design, searching algorithms for both cases of minimum distance and tangents are established for each agent and its neighbors. In order to avoid collision, an optimal path planning algorithm based on collision angles, and a self-center-based rotation algorithm are also proposed. Moreover, randomized method is used to provide the optimal mapping decision for the underlying system. Two examples and analyses are presented to demonstrate the effectiveness and potential of the new control scheme.     

Dissipative observers for discrete-time nonlinear systems

This paper addresses the problem of designing a state observer for a class of nonlinear discrete-time systems using the dissipativity theory. We show that the dissipative observation methodology, originally proposed by one of the authors for continuous-time nonlinear systems, can be extended to the discrete-time case. For constructing a convergent observer, the methodology is applied to the nonlinear estimation error dynamics, which is decomposed into a discrete-time Linear Time-Invariant (LTI) subsystem in the forward loop, connected to a time-varying static nonlinearity in the feedback loop. In order to assure asymptotic stability of the closed-loop, complementary dissipativity conditions are imposed on each of the subsystems: (i) the static nonlinearity is required to be dissipative with respect to a quadratic supply rate, and (ii) the observer gains are designed such that the LTI system is dissipative with respect to a complementary supply rate. As in the continuous time framework, the proposed method includes as special cases, unifies and generalizes some observer design methods proposed previously in the literature. A great advantage of the Dissipative Observer Design Method proposed here is that it leads to Matrix Inequalities for the design of the observer gains, and these can be usually converted into Linear Matrix Inequalities (LMI’s). The results are illustrated using Chua’s Chaotic system.     

State feedback design for set stabilization of probabilistic Boolean control networks

Set stabilization of probabilistic Boolean control networks (PBCNs) is investigated in this paper and some interesting results are derived. The main results consist of three parts. (1) A definition of set stabilizability with probability one by closed-loop control is proposed for PBCNs, which is not a natural extension from deterministic Boolean control networks to PBCNs due to the random feature of PBCNs. (2) A necessary and sufficient set stabilizability condition is provided for PBCNs. (3) An algorithm for designing a state feedback controller is developed. It is guaranteed that all designed controllers can stabilize a PBCN to a given subset with probability one. The design method is constructive, so it is convenient to use this method in practical application. The results derived above are fundamental and important, since based on them many problems about PBCNs can be solved, for example partial stabilization, synchronization, and so on. Finally, a practical example is employed to show the effectiveness of our results.     

Integrated guidance and control strategy for homing of unmanned underwater vehicles

This paper presents a novel integrated guidance and control strategy for homing of unmanned underwater vehicles (UUVs) in 5-degree-of-freedom (DOF), where the vehicles are assumed to be underactuated at high speed and required to move towards the final docking path. During the initial homing stage, the guidance system is first designed by geometrical analysis method to generate a feasible reference trajectory. Then, in the backstepping framework, the proposed trajectory tracking controller can achieve all the tracking errors in the closed-loop system convergence to a small neighbourhood of zero. It means that the vehicle's dynamics are consistent with the reference trajectory derived in the previous step. To demonstrate the effectiveness of the proposed guidance and control strategy, the complete stability analysis used Lyapunov's method is given in the paper, and simulation results of all initial conditions are presented and discussed.     

A class of fast fixed-time synchronization control for the delayed neural network

This paper deals with the synchronization control of a class of delayed neural networks using a fast fixed-time control theory. By employing Lyapunov stability theory, a novel sufficient criterion is derived such that two neural networks can be synchronized within a fixed-time. Compared with some existing results, the proposed controller can render two neural networks faster synchronized. A numerical example is given to demonstrate the effectiveness of the criterion.