Showing papers in "Transactions of the Institute of Measurement and Control in 2022"

Design and robustness analysis of fuzzy PID controller for automatic voltage regulator system using genetic algorithm

Abstract: In this paper, a Fuzzy proportional–integral–derivative (Fuzzy PID) controller design is presented to improve the automatic voltage regulator (AVR) transient characteristics and increase the robustness of the AVR. Fuzzy PID controller parameters are determined by a genetic algorithm (GA)-based optimization method using a novel multi-objective function. The multi-objective function, which is important for tuning the controller parameters, obtains the optimal solution using the Integrated Time multiplied Absolute Error (ITAE) criterion and the peak value of the output response. The proposed method is tested on two AVR models with different parameters and compared with studies in the literature. It is observed that the proposed method improves the AVR transient response properties and is also robust to parameter changes.

Analysis of fractional-order dynamics of dengue infection with non-linear incidence functions

Abstract: The infection of dengue is a devastating mosquito-borne infection around the globe that affects human health, social and economic sectors in low-income areas. Therefore, policymakers and health experts are trying to point out better policies to reduce these losses and provide better information for the development of vaccination and medication. Here, we formulated a compartmental model for the transmission phenomena of dengue fever with nonlinear forces of infection through fractional derivative. We established several results related to the solution of our dengue model by using the basic properties of fractional calculus. We determined the basic reproduction number of our fractional-order system, symbolized by R 0 . We established the local asymptotic stability of the infection-free equilibrium of our dengue system for R 0 < 1 , and proved that the infection-free equilibrium is globally asymptotically stable without vaccination. The threshold dynamics R 0 is tested through partial rank correlation coefficient method to notice the importance of parameters in the transmission of dengue infection. In addition, we have shown the impact of memory on the basic reproduction number numerically with the variation of different parameters. We conclude that the biting rate, recruitment rate of mosquitoes and index of memory are the most sensitive factors, which can effectively lower the level of dengue fever. The dynamical behavior of the proposed fractional system is presented through a numerical scheme to explore the overall transmission process. We predict that the fractional-order model can explore more accurately and preciously the intricate dengue disease transmission model rather than the integer-order derivative.

Computation of parametric uncertainty margin using stability boundary locus: An application to load frequency control

Abstract: In this paper, computation of parametric uncertainty margin (PUM) of a system is explored. In the presence of parametric uncertainty, maintaining the stability of the system is a daunting task. In view of this, in this paper, a new methodology is proposed to determine the maximum parametric uncertainty or PUM that can be tolerated in an interval type proportional–integral (PI) controlled system so that the whole system remains robustly stable. In order to show the validity of the proposed approach, an example of interval type load frequency control (LFC) system is considered to determine the PUM numerically. The proposed approach utilizes the concept of Kharitonov’s theorem for the modeling of interval type systems and stability boundary locus (SBL) technique for the designing of PI controller. The PUM obtained using the proposed approach is compared by that achieved by Kharitonov’s rectangles and zero exclusion principle.

Prescribed performance–based tracking control for quadrotor UAV under input delays and input saturations

Abstract: In this paper, a semi-global trajectory tracking control scheme is proposed for quadrotor unmanned aerial vehicle (UAV) under time-varying input delays, input saturations, and bounded disturbances. Initially, in order to ensure the desirable control performance that tracking error is limited in a prescribed bound, an error transformation function is presented to transform the constrained problem into a stabilization one. Then, under backstepping control scheme, a state prediction method combined with a dynamic surface control (DSC) is presented to compensate for the effects of input delays, and an auxiliary system is designed to tackle the input saturations, which are further to deal with input delays and input saturations in both position system and attitude system of quadrotor UAV. Particularly, the Euler angle is constrained to avoid the singular points of the attitude system. Besides, based on Lyapunov stability theory, it can be verified that all signals in the closed-loop system of the quadrotor UAV are semi-globally ultimately uniformly bounded (UUB) and the tracking errors are restrained in the prescribed bounds. Finally, by choosing some appropriate parameters, the simulation results based on quadrotor UAV model are given to verify the effectiveness of the proposed control scheme.

Multi-objective risk-based optimal power system operation with renewable energy resources and battery energy storage system: A novel Hybrid Modified Grey Wolf Optimization–Sine Cosine Algorithm approach

Abstract: This research work proposes a Hybrid Modified Grey Wolf Optimization–Sine Cosine Algorithm for the multi-objective optimal scheduling of hybrid power system taking into consideration the risk factor arising due to the intermittent/uncertain nature of the renewable power generation sources. The hybrid power system is modelled considering the thermal generation units, wind energy system, solar photo voltaic system, electric vehicle and battery energy storage system. The multi-objective optimization problem is proposed based on the simultaneous minimization of the total operating cost and system risk. The conditional value at risk is introduced as the risk index to analyse the system risk due to uncertainties in power deliveries by the renewable energy resources, electric vehicle and battery energy storage system during the scheduling process. The integral contribution of this research work focuses on the establishment an optimal generation schedule based on the combined optimization of the total operating cost and system risk. The simultaneous minimization of the operating cost and the risk index is performed with the multi-objective Hybrid Modified Grey Wolf Optimization–Sine Cosine Algorithm and has been used to develop a Pareto-optimal front. The implementation of the fuzzy min–max technique is opted to fetch the best compromised solution. The standard test systems of IEEE-30 bus and Indian-75 bus system are used to validate the potency of the proposed approach. Comparative analysis has been established to highlight the results obtained with the proposed approach is appreciable than other optimization techniques.

Consensus of piecewise time-varying multi-agent systems with switching topologies

Abstract: In this paper, a consensus problem is first investigated for piecewise time-varying multi-agent systems with switching topologies. Due to the piecewise time-varying characteristics of system matrix, it is challenging to design an appropriate controller to stabilize the error state within each piecewise time period. To overcome this difficulty, a piecewise time-varying Lyapunov function (PTVLF) approach is proposed to analyze the piecewise time-varying systems. Then, a useful lemma guaranteeing the negative definiteness of matrix polynomials is first derived, which is utilized to prove the negative definiteness of the derivative for the PTVLF. Based on this, a novel controller with time-varying gain is presented to stabilize the error state within each piecewise time period. Then, by selecting the dwell time of each topology larger than a positive threshold, the overall consensus of such systems is guaranteed. Finally, a numerical simulation is shown to illustrate the theoretical results.

A compensation sliding mode control for machining robotic manipulators based on nonlinear disturbance observer

Abstract: The machining robotic manipulators (MRMs) find a broad range of applications due to their high efficiency, wide range of processing, and strong flexibility. High tracking accuracy and strong anti-interference ability are required to trajectory tracking control of machining robot during processing. In view of the above characteristics, this paper proposes a compensation sliding mode control (CSMC) based on nonlinear disturbance observer (NBO-CSMC) for MRM. First, we deduced the dynamic model of MRM considering the uncertainties and disturbances. Then, for improving the trajectory tracking accuracy, a compensation sliding mode controller is designed based on the traditional sliding mode control (TSMC) strategy. Finally, in order to reduce the chattering in sliding mode control, the NBO-CSMC is designed for MRM, NBO is used to estimate the external composite uncertain interference existing in the system, and compensate the system control input in real time. The Lyapunov’s theory proved the stability of the proposed algorithm, and simulation experiments verified the effectiveness of the proposed control strategy.

Adaptive consensus control for a class of nonlinear multi-agent systems with unknown time delays and external disturbances

Abstract: This paper investigates a class of unknown nonlinear multi-agent systems with unknown time delays and external disturbances. The external disturbance considered in this paper is completely unknown, and the accurate mathematical expression cannot be obtained. To eliminate the external disturbance, a disturbance observer is constructed by the Disturbance-Observer-Based-Control (DOBC) method. By utilizing Young’s inequality scaling technique, the problem of time delay is solved. By assuming that the communication graph among the agents is directed and connected, a novel distributed adaptive consensus control protocol is established based on the relative output information to achieve the goal of consensus control. Furthermore, the stability of the multi-agent system is analyzed by selecting an appropriate Lyapunov function; it is certified that with the proposed control protocol, all signals are guaranteed to be globally uniformly bounded, and the considered multi-agent systems can reach consensus even in the presence of time delay and external disturbance. Finally, two simulation examples are given to support the proposed control strategy.

Q-Learning-based fuzzy energy management for fuel cell/supercapacitor HEV

Abstract: A novel fuzzy energy management strategy (EMS) based on improved Q-learning controller and genetic algorithm (GA) is proposed for the real-time power split between fuel cell and supercapacitor of hybrid electric vehicle (HEV). Different from driving pattern recognition–based method, Q-Learning controller takes actions by observing the driving states and compensates to fuzzy controller, that is, no need to know the driving pattern in advance. Aimed to prolong the fuel cell lifetime and decrease its energy consumption, the initial values of Q-table are optimized by GA. Moreover, to enhance the environment adaptation capability, the learning strategy of Q-learning controller is improved. Two adaptive energy management strategies have been compared, and simulation results show that current fluctuation can be reduced by 6.9% and 41.5%, and H2 consumption can be saved by 0.35% and 6.08%, respectively. Meanwhile, state of charge (SOC) of supercapacitor is sustained within the desired safe range.

Distributed fixed-time average-tracking for multi-agent systems with mismatched and matched disturbances

Abstract: In this paper, distributed fixed-time average-tracking problem is discussed for second-order multi-agent systems (MASs) with mismatched and matched disturbances. To address this issue, we first propose a novel distributed fixed-time average estimator for each agent to estimate the average value of multiple time-varying reference signals (TVRSs). Then, to tackle with the case in the presence of external mismatched and matched disturbances, a disturbance observer is constructed to estimate them in a fixed time. Finally, based on the disturbance observer, a fixed-time average-tracking protocol is designed to drive each agent to track the average value of multiple TVRSs within a fixed time. Simulation experiments are conducted to verify the effectiveness of the proposed fixed-time anti-disturbance average-tracking algorithm.

Sampled-data-based dynamic event-triggered formation control for nonlinear multi-agent systems

Abstract: This paper studies the distributed formation control of multi-agent systems with nonlinear dynamics. In view of practical digital microprocessor and limited network resources, the sampled-data-based dynamic event-triggered control strategy is developed. First, each agent synchronously samples its states and monitors the event-triggered function periodically. Each agent broadcasts its states to neighbors only when the function is triggered which can greatly reduce communication times. Meanwhile, the Zeno behavior is excluded due to periodic sampling. Moreover, the dynamic parameter in event-triggered function updates in accordance with a dynamic rule which helps achieve a trade-off between communication frequency and formation performance. The formation problem is transformed into the stability analysis of a time-delay system. Finally, numerical simulations are shown to illustrate the effectiveness of the proposed strategy.

Event-triggered-based adaptive sliding mode control for networked linear control systems

Abstract: This paper investigates the robust stabilization of the adaptive sliding mode control for a class of linear systems subjected to external disturbance via event-triggered communication (ETC) scheme. First, in order to reduce the bandwidth utilization, a discrete ETC scheme is proposed and the networked sliding mode function is derived using the ETC scheme. Based on the derived sliding mode function, a reduced-order networked sliding mode dynamics with communication delay is established. Second, by constructing a Lyapunov–Krasovskii functional (LKF), asymptotic stability and stabilization criteria of the reduced-order sliding mode dynamics are given in the form of linear matrix inequalities. According to the stabilization result, a novel event-triggered-based adaptive sliding mode controller is designed while guaranteeing the reachability of the sliding surface. Finally, simulation results illustrate the effectiveness and merit of the developed method.

Vibration control of a translational coupled double flexible beam system using sliding mode neural network fuzzy control

Abstract: A sliding mode neural network fuzzy control (SMNNFC) method is investigated to suppress the vibration of a translational coupled double flexible beam system, equipped with an AC servomotor and several piezoelectric actuators. Adjacent beams and slider moving frame are connected at the tip by elastic springs. Based on the finite element method, the system model is established to recognize the vibration characteristics. Furthermore, two laser displacement sensors are used to decouple the first two bending modes of the double flexible beam system. In the applied SMNNFC strategy, a neural-fuzzy framework is designed to obtain robust control performance and alleviate the chattering phenomenon. Considering unknown and varying system uncertainty, a parameter updating algorithm is adopted. The stability of SMNNFC is analyzed. The experimental setup is constructed and experiments are conducted, including set-point vibration control and simultaneous translation and vibration control under trapezoidal and sinusoidal trajectories. The experimental results demonstrate that the SMNNFC scheme has advantages in suppressing both the large and low amplitude vibrations of the coupling double flexible beam system.

A Sigmoid-plane adaptive control algorithm for unmanned surface vessel considering marine environment interference

Abstract: This article presents an application of a Sigmoid-plane (S-plane) adaptive control algorithm to an automatic steering system in the presence of uncertain parametric of the unmanned surface vessel (USV) and the unknown disturbance of the marine environment. Due to technical difficulties such as sensor noise, the marine environment disturbance is assumed to be unmeasured. To overcome this problem, an S-plane control is designed to resist marine environment disturbances by the improved adaptive term. Based on the gradient method, the USV heading model reference adaptive controller of the USV is designed, so that the USV has a certain ability to resist model parameter changes. Considering the uncertainty of marine environment interference, a model-reference-based S-plane adaptive controller for the USV is designed. After the reference of the USV course model, the S-plane adaptive control approach is employed to reduce the effect of the marine environment. It is proved that the USV course control system is stable, despite the adverse bad sea conditions. Finally, the course control simulation experiment for the USV with the unknown marine environmental interference is carried out. To demonstrate the benefits of the S-plane adaptive controller, the results are presented in comparison with a Lyapunov method controller. The results show that the S-plane adaptive controller is robust to the changed model parameter and external disturbances.

A framework for battery internal temperature and state-of-charge estimation based on fractional-order thermoelectric model

Abstract: In recent years, the rapid development of electric vehicles has raised a wave of innovation in lithium-ion batteries. The safety operation of lithium-ion batteries is one of the major bottlenecks restraining the development of the energy storage market. The temperature especially the internal temperature can significantly affect the performance and safety of the battery; therefore, this paper presented a novel framework for joint estimation of the internal temperature and state-of-charge of the battery based on a fractional-order thermoelectric model. Due to the nonlinearity, coupling, and time-varying parameters of lithium-ion batteries, a fractional-order thermoelectric model which is suitable for a wide temperature range is first established to simulate the battery’s thermodynamic and electrical properties. The parameters of the model are identified by the electrochemical impedance spectroscopy experiments and particle swarm optimization method at six different temperatures, and then the relationship between parameters and temperature is obtained. Finally, the framework for joint estimation of both the cell internal temperature and the state-of-charge is presented based on the model-based state observer. The experimental results under different operation conditions indicated that, compared with the traditional off-line prediction method, the model-based online estimation method not only shows stronger robustness under different initial conditions but also has better accuracy. Specifically, the absolute mean error of the estimation of state-of-charge and internal temperature based on the proposed method is about 0.5% and 0.3°C respectively, which is about half of that based on the off-line prediction method.

Load frequency control in the presence of simultaneous cyber-attack and participation of demand response program

Abstract: Simultaneous investigation of demand response programs and false data injection cyber-attack are critical issues for the smart power system frequency regulation. To this purpose, in this paper, the output of the studied system is simultaneously divided into two subsystems: one part including false data injection cyder-attack and another part without cyder-attack. Then, false data injection cyber-attack and load disturbance are estimated by a non-linear sliding mode observer, simultaneously and separately. After that, demand response is incorporated in the uncertain power system to compensate the whole or a part of the load disturbance based on the available electrical power in the aggregators considering communication time delay. Finally, active disturbance rejection control is modified and introduced to remove the false data injection cyber-attack and control the uncompensated load disturbance. The salp swarm algorithm is used to design the parameters. The results of several simulation scenarios indicate the efficient performance of the proposed method.

Finite-time sliding mode control of underwater vehicles in 3D space

Abstract: In this paper, trajectory tracking control of an underwater vehicle in three-dimensional (3D) space has been addressed. The assumed underwater vehicle has 6 degrees of freedom and the aim is to control all system rotations and displacements. In this paper, a finite-time sliding mode controller as a robust control method is proposed for an underwater vehicle with 6 degrees of freedom in 3D space using the method without simplifications or decouplings. Therefore, both system positions and orientations are controlled in the presence of disturbances and uncertainties. In previous research works, control of two-dimensional underwater vehicles is commonly studied. In this paper, a novel stable control algorithm is proposed for an underwater vehicle with 6 degrees of freedom. The stability of the closed-loop system is analyzed using the Lyapunov theory. The designed algorithm can cover 3D complicated tasks. Also, the designed algorithm as a robust control approach can attenuate external disturbances. The performance and stability of this approach are compared with the sliding mode controller. The numerical comparison results show that the proposed approach is effective and applicable in practice.

A diversity-based parallel particle swarm optimization for nonconvex economic dispatch problem

Abstract: The economic dispatch (ED) problem aims to minimize the total generation cost while satisfying certain constraints, such as valve-point effects, multi-fuel options, prohibited operating zones, transmission losses, and ramp rate limits. In this paper, these constraints are considered simultaneously for the first time, resulting in a complex nonconvex ED problem. A diversity-based parallel particle swarm optimization (DPPSO) is proposed to solve the nonconvex ED problem, where the implementation details—such as evaluation function design, particle definition, and equality and inequality handling strategies—have been carefully discussed. In our approach, the population of DPPSO is divided into different groups to maintain diversity in particles so that the optimization capacity can be enhanced. An asynchronous information–sharing mechanism (AISM) helps decrease the population size. Hence, the computational burden is reduced. Moreover, information in different groups is calculated parallelly and updated asynchronously to improve computational efficiency. Benchmark functions are employed to demonstrate the effectiveness of the proposed method. Furthermore, three nonconvex ED problems are resolved by the proposed method, and state-of-the-art performance has been achieved. In addition, the proposed algorithm is highly modular, making it easy to unite other salient variants of particle swarm optimization (PSO) to improve its performance.

Stabilization of a class of switched systems with state constraints via time-varying Lyapunov functions

Abstract: This paper studies the stabilization problem of a class of continuous-time switched linear systems with state constraints under pre-specified dwell-time switchings. Such systems are defined on a closed hypercube as all state variables are constrained to the unit hypercube. The dwell time in this paper is an arbitrarily pre-specified rather than a calculated constant, which is independent of any parameters. First, a class of multiple time-varying Lyapunov functions is introduced to study the stability analysis, and sufficient conditions on stability of the studied switched systems without control input are derived in the framework of pre-specified dwell-time switchings. The distinguishing feature of the proposed Lyapunov functions is that this type of delicately constructed Lyapunov functions can efficiently eliminate the “jump” phenomena of adjacent Lyapunov functions at switching times. Second in the same framework of the dwell time, sufficient conditions for stabilization are proposed for that of the switched systems with state constraints by further designing state feedback controllers. Finally, two examples are provided to demonstrate the effectiveness of the proposed results. The results of this paper do not require to calculate the total time of each subsystem during which the state is saturated or non-saturated separately, which makes the pre-specified dwell-time switchings easy to apply.

Leakage detection method of natural gas pipeline combining improved variational mode decomposition and Lempel–Ziv complexity analysis

Abstract: With the continuous development of pipeline transportation industry, pipeline leakage often occurs, posing a great threat to people’s lives and property safety. In order to improve the detection accuracy of natural gas pipeline leakage, a pipeline leakage detection method based on improved variational mode decomposition algorithm and Lempel–Ziv complexity analysis is proposed. In this work, the normalized mutual information is used to determine the decomposition level K of variational mode decomposition, and the Lempel–Ziv complexity analysis algorithm is used to extract pipeline signal feature. The results show that the proposed leakage detection method has higher classification accuracy than other methods, which verifies the effectiveness of this method in the process of pipeline leakage detection.

Wavelet-based multi-class support vector machine for stator fault diagnosis in induction motor

Abstract: This work proposes a novel online detection scheme to diagnose incipient inter-turn short circuit fault and estimate the failure severity in induction motor. Incipient detection of the stator failure during the machine running, as well as identification of its intensity can reduce the risk of additional damage to the phase winding, improve the operational efficiency, and ensure machine availability. Hence, the incipient fault diagnosis provides a safe operating area for the motor. This work aims to specify the percentage of defective turns in the shorted winding by proposing a new mathematical parameter based on wavelet analysis, in addition to employ a multi-class support vector machine to perform the classification task. Discrete Wavelet Transform is used to analyze the stator currents after modeling the motor utilizing Clarke-Concordia transformation. From the detailed coefficients, Max and L2 norms are calculated. The adopted parameter is computed depending on the previous norms, which form the input vector to feed the classifier. The multi-class support vector machine–based one versus one algorithm is used to determine accurately the defect intensity. The acquired outcomes prove that the proposed approach, depending on the novel parameter along with multi-class support vector machine can give a robust and accurate indication about the machine status, which enables the estimation of the fault severity. To verify the competency of the methodology, various hardware experiments are carried out on the motor. The experimental results demonstrate the validity and practicability of the method, with a higher level of correctness, exceeding 96%.

Discrete indirect adaptive sliding mode control for uncertain Hammerstein nonlinear systems

Abstract: The robustness issue of uncertain nonlinear systems’ control has attracted the attention of numerous researchers. In this paper, we propose three techniques to deal with the uncertain Hammerstein nonlinear model. First, a discrete sliding mode control (SMC) is developed, which is based on converting the original nonlinear system into a linearized one in the vicinity of the operating region using Taylor series expansion. However, the presence of relatively high nonlinearities and parameter variations leads to the deterioration of the desired performances. In order to overcome these problems and to improve the performance of classical SMC, we propose two solutions. The first one is based on the synthesis of a discrete SMC, taking into account the presence of nonlinearity. The second solution is a new discrete adaptive SMC for input–output Hammerstein model. In order to show the effectiveness of the proposed controllers, a detailed robustness analysis is clearly developed. Simulation examples are reported at the end of the paper.

Multi-objective H2/H∞ saturated non-PDC static output feedback control for path tracking of autonomous vehicle

Abstract: This paper presents a new fuzzy output feedback control design for autonomous vehicle steering under actuator saturation, unavailability of the sideslip angle measurement, unknown road curvature, and lateral wind force. To take into account the actuator constraint, the saturation effect is transformed into dead-zone nonlinearity. A static output controller based on non-compensation parallel distributed technic and a Takagi-Sugeno (T-S) model of vehicle lateral dynamics is proposed to consider the unavailability of some vehicle states. To avoid the problem of imposing bounds on membership functions time derivatives resulting from the use of the fuzzy Lyapunov approach, a proper integral structure based on the non-quadratic Lyapunov approach is investigated. The mixed H 2 / H ∞ stabilization conditions of the augmented closed-loop system are expressed in terms of linear matrix inequalities (LMIs). Finally, the robustness and the advantages of the proposed approaches are demonstrated through different tests.

Stability and performance comparison analysis for linear active disturbance rejection control–based system

Abstract: This paper investigates the stability and performance of the linear active disturbance rejection control (LADRC)–based system with uncertainties and external disturbance via transfer functions and a frequency-domain view. The performance of LADRC is compared with the state-observer-based state feedback control (SOSFC) and state feedback control (SFC). First, the transfer functions and the error transfer functions for LADRC, SOSFC, and SFC are studied using the state-space method. It is proven that the LADRC-, SOSFC-, and SFC-based closed-loop systems have the same transfer function from the reference input to the output and achieve the same control effects for the nominal system. Then, it is proven for the first time that the LADRC has a better anti-interference ability than the SOSFC and SFC. Besides, the asymptotic stability condition of LADRC-based closed-loop system considering large parameter perturbations is given first. Moreover, the sensitivity analysis of the closed-loop system is carried out. The results show that the LADRC has stronger robustness under parameter perturbations. According to the results, we conclude that the LADRC is of great disturbance rejection ability and strong robustness.

A soft-sensing method for product quality monitoring based on particle swarm optimization deep belief networks

Abstract: A novel soft-sensing method for quality parameters of aviation kerosene in atmospheric distillation column based on least absolute shrinkage and selection operator and particle swarm optimization deep belief network (LASSO-PSO-DBN) is proposed. First, to reduce the dimension of the input variables, the least absolute shrinkage and selection operator (LASSO) algorithm is used to select the input variables that are irrelevant to the soft sensor of aviation kerosene quality parameters. Then, to improve the generalization of soft sensor model, a deep learning algorithm, deep belief network (DBN), is proposed for soft sensing of aviation kerosene quality parameters. Considering that the structure characteristics and parameters of DBN algorithm have a great impact on the learning and prediction results, the parameters of DBN are optimized based on particle swarm optimization (PSO) algorithm. The benchmark data sets and the industrial atmospheric distillation column data are used for simulation analysis and evaluation of the soft-sensing performance. The simulation results show that the novel proposed algorithm can effectively reduce the dimension of the input variables and simplify the structure of the soft sensor model. It also has good generalization ability and the predicted value is in good agreement with the actual measured value.

Adaptive quantized controller design for synchronization of uncertain fractional-order nonlinear systems satisfying incremental quadratic constraints

Abstract: This paper proposes the adaptive quantized controller design for the synchronization of a class of fractional-order nonlinear systems satisfying incremental quadratic constraints governed by an incremental multiplier matrix. The incremental quadratic constraints can describe many commonly encountered nonlinearities in existing literature. The adaptive quantized controller are designed and formulated in terms of matrix inequalities to make the error system asymptotically stable. Meanwhile, the sufficient conditions can be obtained via solving linear matrix inequalities. Moreover, an algorithm is presented to illustrate the steps of designing adaptive quantized controllers. Finally, examples about fractional-order Lorenz chaotic system and fractional-order Bloch system are provided to illustrate the effectiveness of the designed controller.

Research on cyber-physical system control strategy under false data injection attack perception

Abstract: This paper investigates the security control problem of the cyber-physical system under false data injection attacks. A model predictive switching control strategy based on attack perception is proposed to compensate for the untrusted sequence of data caused by false data injection attacks. First, the binary attack detector is applied whether the system has suffered the attack. If the attack occurs, multistep correction is carried out for the future data according to the previous time data, and the waiting period [Formula: see text] is set. The input and output sequence of the controller is reconstructed, and the system is modeled as a constant time-delay switched system. Subsequently, the Lyapunov methods and average-dwell time are combined to provide sufficient conditions for the asymptotical stability of closed-loop switched system. Finally, the simulation of the networked first-order inverted pendulum model reveals that the control technique can efficiently suppress the influence of the attacks.

Three-dimensional piecewise guidance strategy for multi-UAVs guaranteeing simultaneous arrival and field-of-view constraint

Abstract: This paper presents a three-dimensional (3D) piecewise guidance scheme for multiple UAVs) guaranteeing simultaneous arrival even under the field-of-view (FOV) constraint. The guidance law does not require to consider the estimation of the time-to-go and can be divided into two stages: cooperative stage and proportional navigation guidance (PNG) stage. In the cooperative stage, a decentralized consensus control approach is proposed for multi-UAV systems and does not rely on global information about the communication topography. Moreover, the neighbors’ new auxiliary states are introduced to ensure the states achieving consensus under FOV constrain. The guidance strategy transfers to the PNG stage while the UAV formation is close enough to the target and the simultaneous arrival to hit the target is guaranteed for UAVs via the PNG guidance law. Numerical simulation results demonstrate the effectiveness of the proposed cooperative guidance law.

Dynamic surface control for formation control of quadrotors with input constraints and disturbances

Abstract: In this paper, a novel formation control scheme is presented by integrating the dynamic surface control for multi-quadrotor subject with model uncertainties and external disturbances. First, according to requirements of formation control, the target trajectories of followers are obtained by the designed virtual quadrotors. Then, the formation control problem is transformed into the trajectory tracking control problem. Second, the saturation function and the auxiliary system are developed to make up for nonlinear terms arising from input saturation. A nonlinear extended state observer (ESO) is proposed to estimate and compensate for model uncertainties and external disturbances, and a dynamic surface controller based on the nonlinear ESO is constructed for the desired formation performance. In addition, the amount of communications between the quadrotors is decreased by the constructed distributed speed estimator. And the uniformly ultimately bounded is proved by using the Lyapunov method. Finally, the numerical example is used to demonstrate that the designed controller is effective.

Iterative learning control of fractional-order linear systems with nonuniform pass lengths

Abstract: Most previous studies about fractional-order iterative learning control (FOILC) assume fixed pass lengths in iteration domain and identical initial condition. These fundamental preconditions may be violated in practical applications. This paper introduces a novel FOILC strategy for tracking control of fractional-order linear systems. To relax the fixed pass lengths assumption, redefined tracking error is applied to formulate control input. Meanwhile, an initial state learning algorithm is introduced to relax the identical initial condition assumption. Strict convergence analysis of the tracking error in iteration domain is given. Finally, two illustrative simulation examples are applied to verify the efficiency and applicability of the proposed algorithm.