Memory-based controller design for neutral time-delay systems with input saturations: A novel delay-dependent polytopic approach

In this paper, a new memory-based control problem is addressed for neutral systems with time-varying delay, input saturations and energy bounded disturbances. Attention is focused on the design of a memory-based state feedback controller such that the closed-loop system achieves the desirable performance indices including the boundedness of the state trajectories, the H_∞ disturbance rejection/attenuation level as well as the asymptotic stability. By using the combination of a novel delay-dependent polytopic approach, augmented Lyapunov–Krasovskii functionals and some integral inequalities, delay-dependent sufficient conditions are first proposed in terms of linear matrix inequalities. Then, three convex optimization problems are formulated whose aims are to, respectively, maximize the disturbance tolerance level, minimize the disturbance attenuation level and maximize the initial condition set. Finally, simulation examples demonstrate the effectiveness and benefits of the obtained results.     

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.     

A noise-filtering event generator for PIDPlus controllers

In this paper we propose a new event generator, which has strong noise-filtering capabilities, to be used in event-based control systems with a PIDPlus controller. An approximate frequency analysis is performed in order to characterize the event generator system and tuning guidelines are provided for its design parameter. Simulation and experimental results obtained with a laboratory setup demonstrate the effectiveness of the methodology in providing a satisfactory performance related to set-point and load disturbance step responses with a total variation that is significantly reduced with respect to the standard cases.     

An algebraic approach to on-line signal denoising and derivatives estimation

In this paper, a new algebraic approach to the on-line signal derivatives estimation is proposed. The proposed approach is based on the conversion of the truncated Taylor series expansion to the set of linearly independent equations regarding the signal derivatives. The nonhomogeneous parts of the obtained set of equations are convolution integrals, which can be transformed to the stable linear state-space filter realization. The proposed algebraic estimator provides stable convergence without the need for periodic re-initialization, as in the case of the conventional algebraic estimators. In contrast to the Taylor series-based tracking differentiators, the proposed estimator also provides an estimation of the arbitrary number of the higher-order signal derivatives. In addition, the tuning of the estimator parameters does not depends on the filter dimension. The efficiency of the proposed estimator is illustrated by the simulation examples and experimental results related to the monitoring of the surgical drilling process.     

A neural network-based detection and mitigation system for unintended acceleration

Modern vehicles are equipped with a growing number of electronic devices, which significantly improve the driving experience. However, the complicated architecture of electronic systems also increases the difficulty of fault diagnosis since process models are often unavailable. This paper presents a novel detection and mitigation system for vehicle related anomalies originating in unintended acceleration (UA), which has become one of the most complained-about vehicle problems in recent history. The detection system consists of several neural network-based models, which are created by analyzing historical vehicle data at specific moments such as acceleration peaks and gear shifting. These data-driven models describe the boundary of normal vehicle behavior in the data space. A priori knowledge of complete vehicle structures is not necessary for building them. The detection system combines these models to decide if a UA event has occurred. When a UA event is detected, a mitigation system cuts the engine power and adjusts the braking force accordingly. The whole system was validated in the Simulink/dSPACE environment. UA errors were simulated so that they occurred randomly when human subjects drove virtual cars in a simulated environment. Random noise of sensors were also considered and incorporated to add realism. Various traffic scenarios were included in tests. Test results show that the integrated system is capable of detecting UA in one second with high accuracy and reducing the risk of accidents.     

Super-twisting observer-based sliding mode control with fuzzy variable gains and its applications to fully-actuated hexarotors

In this paper, we consider the super-twisting observer-based sliding mode control algorithm with fuzzy variable gains (STOSMC) for the fully-actuated hexarotor. Our hexarotor has full actuation due to six titled propellers that allows to control position and orientation (attitude) simultaneously, and resolves the singularity problem of the rotational matrix by using the quaternion modeling framework. We show that the proposed STOSMC for the hexarotor guarantees finite-time convergence of the estimation error and asymptotic stability of the hexarotor. In simulations, we demonstrate the nonsingularity and fully-actuated control performance of the hexarotor by considering extreme position and attitude control scenarios. Moreover, the simulation results show that the hexarotor achieves the fast and precise tracking performance to the desired position and the desired attitude and the chattering phenomenon is reduced compared with the fixed-gains observer-based super-twisting sliding mode control due to the fuzzy mechanism.     

Hierarchical Cucker–Smale flocking under random interactions with time-varying failure probabilities

This paper studies the hierarchical Cucker–Smale flocking model of sampled-data second-order discrete-time multi-agent systems under random interactions with time-varying failure probabilities. More precisely, each agent, at each sampling time point, can fail to see any of its superiors in the hierarchy. The random failures are not independent with varying failure rate probabilities. For this model with random interactions, we prove that the flocking would occur almost surely, i.e., agents’ velocities will converge almost surely to the velocity of the overall leader of the flock which moves with a varying velocity, the relative positions between agents and the overall leader converge almost surely. Finally, several numerical simulations are provided to illustrate the obtained results.     

Parallel access by butterfly networks for any degree permutation polynomial and ARP interleavers

Using more processors for parallel turbo decoding is an important issue to speed up the processing at the receiver of a communication system. Butterfly networks used to map the addresses of extrinsic values represent an elegant and simple solution in parallel turbo decoding. Recently, it has been shown that quadratic permutation polynomial (QPP) interleavers allow an easy way to compute the control bits for a butterfly network. In this paper we show that not only QPP interleavers, but any degree permutation polynomial (PP) interleavers and almost regular permutation (ARP) interleavers also allow the same easy way to compute the control bits required in butterfly networks. As a consequence, it is useful to apply the butterfly networks in parallel turbo decoding when using these performant algebraic interleavers.     

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.     

Terminal iterative learning control for discrete-time nonlinear systems based on neural networks

The terminal iterative learning control is designed for nonlinear systems based on neural networks. A terminal output tracking error model is obtained by using a system input and output algebraic function as well as the differential mean value theorem. The radial basis function neural network is utilized to construct the input for the system. The weights are updated by optimizing an objective function and an auxiliary error is introduced to compensate the approximation error from the neural network. Both time-invariant input case and time-varying input case are discussed in the note. Strict convergence analysis of proposed algorithm is proved by the Lyapunov like method. Simulations based on train station control problem and batch reactor are provided to demonstrate the effectiveness of the proposed algorithms.     

Centralized security-guaranteed filtering in multirate-sensor fusion under deception attacks

In this paper, the centralized security-guaranteed filtering problem is studied for linear time-invariant stochastic systems with multirate-sensor fusion under deception attacks. The underlying system includes a number of sensor nodes with a centralized filter, where each sensor is allowed to be sampled at different rate. A new measurement output model is proposed to characterize both the multiple rates and the deception attacks. By exploiting the lifting technique, the multi-rate sensor system is cast into a single-rate discrete-time system. With a new concept of security level, the aim of this paper is to design a filter such that the filtering error dynamics achieves the prescribed level of the security under deception attacks. By using the stochastic analysis techniques, sufficient conditions are first derived such that the filtering error system is guaranteed to have the desired security level, and then the filter gain is parameterized by using the semi-definite programme method with certain nonlinear constraints. Finally, a numerical simulation example is provided to demonstrate the feasibility of the proposed filtering scheme.     

Data-driven optimal switching of switched systems

Switched systems are complicated due to the switching among the subsystems. When the subsystem models are unknown, control problems on switched systems turn to be more intractable. In this paper, the optimal switching problems are investigated for continuous-time switched autonomous systems with unknown dynamics and a finite-horizon cost function. Firstly, a novel data-driven optimal scheduling approach is proposed based on the estimated insertion gradients. Secondly, aiming at switched systems with a prescribed switching sequence, a data-driven optimal switching time approach is proposed based on the estimated derivatives of the cost with respect to the switching times. The two approaches take advantages of plenty state data containing necessary information instead of the system models. Furthermore, the errors of the approaches are analysed and bounded. Finally, simulation results of two examples are given to show the validity of the two approaches.     

A robust global approach for LPV FIR model identification with time-varying time delays

Robust identification of the linear parameter varying (LPV) finite impulse response (FIR) model with time-varying time delays is considered in this paper. A robust observation model based on Laplace distribution is established to deal with the output data contaminated with the outliers, which are commonly existed in modern industries. A Markov chain model is utilized to model the correlation between the time delays as they do not simply change randomly in reality. A transition probability matrix and an initial probability distribution vector are used to govern the switching mechanism of the time delays. Since it is difficult to optimize the complex log likelihood function directly, the derivations of the proposed algorithm are performed under the framework of Expectation-Maximization (EM) algorithm. A numerical example and a chemical process are utilized to verify the effectiveness of the proposed approach.     

Fixed-time outer synchronization of hybrid-coupled delayed complex networks via periodically semi-intermittent control

In this paper, the fixed-time synchronization between two delayed complex networks with hybrid couplings is investigated. The internal delay, transmission coupling delay and self-feedback coupling delay are all included in the network model. By introducing and proving a new and important differential equality, and utilizing periodically semi-intermittent control, some fixed-time synchronization criteria are derived in which the settling time function is bounded for any initial values. It is shown that the control rate, network size and node dimension heavily influence the estimating for the upper bound of the convergence time of synchronization state. Finally, numerical simulations are performed to show the feasibility and effectiveness of the control methodology by comparing with the corresponding finite-time synchronization problem.