A new on-line observer-based controller for leader-follower formation of multiple nonholonomic mobile robots

In this paper, a novel on-line observer-based trajectory tracking strategy for leader-follower formation of multiple nonholonomic mobile robots is developed. In the proposed strategy, a leader robot follows a certain trajectory whereas a number of followers track the leader as specified by a formation protocol. Unlike other techniques in the literature, a predefined trajectory is not required, and it can be changed on-line. Moreover, this strategy aims to have a fast transient response without showing undesired overshoots. To achieve this feature, a new observer is introduced. Based on the output of that observer, a control strategy with two components is derived. The first control component is responsible for tracking the desired trajectory, whereas the second control component is used to regulate the robot to its desired steady state position. The stability of the closed loop control system is investigated. Applications of the proposed observer-based controller to different case studies are presented to illustrate the effectiveness, robustness and applicability of the developed technique. To show the superiority of proposed controller, its performance in a trajectory tracking application is compared to that of a Lyapunov-based controller.     

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.     

Output feedback control for a class of high-order nonholonomic systems with complicated nonlinearity and time-varying delay

This paper considers the output feedback control problem for high-order nonholonomic time-delay system. Remarkably, the studied system allows the polynomial time-delay growing conditions. Moreover, the applicable power ranges of nonlinear drift and diffusion terms are further relaxed to be a interval rather than a fix point. By choosing a new Lyapunov–Krasovskii (L–K) functional, and by modifying the adding a power integrator method, a delay-independent output feedback controller is designed such that the system is globally asymptotically stable. A simulation example is given to show the validity of the proposed theory.     

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.     

Finite-time sliding mode control of Markovian jump systems subject to actuator nonlinearities and its application to wheeled mobile manipulator

This work is concerned with the finite-time sliding mode control for a class of Markovian jump systems subject to actuator nonlinearities, where the elements in the transition rate matrix are uncertain or even completely unknown. A suitable sliding mode controller is designed such that the finite-time stochastic boundedness of state trajectories is attained during a given finite-time interval, in which two different robust terms are introduced for the known and unknown modes to deal with the effect of uncertain transition rates. Moreover, the connections among sliding functions under Markovian jumping for SMC systems are analyzed. Finally, some simulation results with a wheeled mobile manipulator are provided.     

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.     

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.     

Event-triggered backstepping control for attitude stabilization of spacecraft

To decrease the communication frequency between the controller and the actuator, this paper addresses the spacecraft attitude control problem by adopting the event-triggered strategy. First of all, a backstepping-based inverse optimal attitude control law is proposed, where both the virtual control law and the actual control law are respectively optimal with respect to certain cost functionals. Then, an event-triggered scheme is proposed to realize the obtained inverse optimal attitude control law. By designing the event triggering mechanism elaborately, it is guaranteed that the trivial solution of the closed-loop system is globally exponentially stable and there is no Zeno phenomenon in the closed-loop system. Further, the obtained event-triggered attitude control law is modified and extended to the more general case when the disturbance torque cannot be ignored. It is proved that all states of the closed-loop system are bounded, the attitude error can be made arbitrarily small ultimately by choosing appropriate design parameters and the Zeno phenomenon is excluded in the closed-loop system. In the proposed event-triggered attitude control approaches, the control signal transmitted from the controller to the actuator is only updated at the triggered time instant when the accumulated error exceeds the threshold defined elaborately. Simulation results show that by using the proposed event-triggered attitude control approach, the communication burden can be significantly reduced compared with the traditional spacecraft control schemes realized in the time-triggered way.     

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.     

Iterative learning control for boundary tracking of uncertain nonlinear wave equations

This work presents a framework of iterative learning control (ILC) design for a class of nonlinear wave equations. The main contribution lies in that it is the first time to extend the idea of well-established ILC for lumped parameter systems to boundary tracking control of nonlinear hyperbolic distributed parameter systems (DPSs). By fully utilizing the system repetitiveness, the proposed control algorithm is capable of dealing with time-space-varying and even state-dependent uncertainties. The convergence and robustness of the proposed ILC scheme are analyzed rigorously via the contraction mapping methodology and differential/integral constraints without any system dynamics simplification or discretization. In the end, two examples are provided to show the efficacy of the proposed control scheme.     

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.     

Necessary and sufficient criteria for asynchronous stabilization of Markovian jump systems

This paper is concerned with asynchronous stabilization for a class of discrete-time Markovian jump systems. The mode of designed controller is considered to be not perfectly synchronous with the activated mode of the Markovian jump system. In order to achieve the asymptotic stability with asynchronous controller, a conditional probability is introduced to describe the asynchronism of system and controller modes, which is dependent on the active system mode. Besides, due to the difficulty in acquiring all the mode transition probabilities in practice, the transition probabilities of the Markovian jump system and the controllers are supposed to be partially unknown. A necessary and sufficient condition is developed to guarantee the stochastic stability of the resultant closed-loop system and further extended to asynchronous stabilization with partially known transition probabilities. Finally, the effectiveness and advantages of the proposed methods are demonstrated by two illustrative examples.     

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.     

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.