This study investigates a signal difference-based deadband control approach as a solution to reduce data transmission in networked control systems (NCSs). A new modelling method for the NCSs with time-varying delays, time-varying sampling intervals and signal transmission deadbands is presented. The Lyapunov functional with discontinuity is exploited, which takes full advantages of the NCSs’ characteristic information including the bounds of network delay (BND), the bounds of sampling interval (BSI) and the bounds of transmission deadband (BTD). In addition, it has been shown that Lyapunov functional decreases at the jump instants. Furthermore, the new stability analysis and stabilisation conditions for the NCSs are proposed, which describes the relationship of BND, BSI, BTD and the system stability. Three examples are used to demonstrate the effectiveness of the proposed methods. The simulation results have shown that the proposed approach could guarantee the system asymptotically stable and effectively reduce the data transmission in network channel.

This study is concerned with the robust H _∞ static output feedback control of discrete Markov jumps systems with norm bounded uncertainties on system input and output matrices. With resorting to a new separating technique, the coupling relationship among Lyapunov variables, controller gains, input and output matrices is eliminated. Based on this new technique, sufficient conditions are developed to guarantee that the closed loop system is robust stochastically stable with the prescribed H _∞ performance level. Moreover, these conditions are expressed in terms of linear matrix inequalities. A numerical example is further given to demonstrate the effectiveness of the proposed method.

Bilateral teleoperation technology has caused wide attentions because of its applications in various remote operation systems. However, there exist some challenging control issues such as communication delay, unmeasurable environmental force, and various manipulator modelling uncertainties. In this study, the disturbance observer is designed based on the slave manipulator dynamics to observe the unmeasurable environmental force. When the environmental force is modelled as a general linear regression form, its unknown parameters can be estimated online by the least square adaptation law. A novel communication structure is proposed where only the master trajectory is transmitted to the slave side, and the transmission signal from the slave to the master is replaced by those estimated environmental parameters. Since these parameters are not power signals, the passivity problem of the communication channel and the trade-off limitation between the transparency performance and robust stability in traditional teleoperation control are essentially avoided. The sliding mode control and the force compensation of disturbance observer are integrated subsequently to deal with various manipulator modelling uncertainties, so that the excellent synchronisation performance can be realised. Thus, the proposed control algorithm can guarantee the robust stability and the good control performance simultaneously under arbitrary time delays. The simulation and experiment on two single degree-of-freedom manipulators are carried out and the results show the effectiveness of the proposed control algorithm.

This study addresses the finite-time attitude tracking control for rigid spacecraft with external disturbances and inertia uncertainties. A novel adaptive-gain super-twist algorithm (STA) improves the control performance of standard STA, and the dynamically adapted control gains can resolve non-overestimating problem. The presented controllers do not require any knowledge on inertial uncertainties and external disturbances, and are anti-chattering and anti-singularity. The closed-loop spacecraft system under the proposed controllers can provide rapidity, robustness, accuracy and anti-wasting energy simultaneously, which is largely ignored in the existing literatures. The finite-time rigorous convergence, an estimation of the convergence time and accurate expression of convergence region are also provided. Finally, comparison results demonstrate that the presented controllers can achieve higher control performance than existing methods. Furthermore, digital simulations utilising the physical parameters of Uosat-12 verify the effectiveness of the proposed controllers.

This study is concerned with modelling and observer-based H _∞ controller design for a continuous-time networked control system with network-induced delays and packet dropouts. A new model for an observer-based networked control system is first established by proposing a linear estimation-based delay compensation method. Then some controller design criteria are obtained by constructing an interval time-varying delay decomposition-based Lyapunov functional. A new bounding inequality is introduced to transfer non-linear matrix inequalities into a solvable optimisation problem. A numerical example is given to illustrate the merits and effectiveness of the obtained results.

Fault is an undesired and unexpected event that changes the system behaviour resulting in performance degradation or even instability, so how to detect and diagnose fault become a great deal in engineering community. In this study, an adaptive fuzzy wavelet network-based fault detection and diagnosis (AFWN-FDD) scheme is proposed for non-linear systems subject to unstructured uncertainty. The proposed scheme is composed of a diagnostic estimator and an adaptive fuzzy wavelet network (AFWN). Diagnostic estimator is designed for residual generation and fault detection and AFWN based on multi-resolution analysis of wavelet transform and fuzzy concept is proposed to approximate the model of fault. Learning algorithm of the proposed AFWN-FDD scheme is derived in the Lyapunov stability sense. The proposed scheme can simultaneously detect and estimate multiple incipient and abrupt faults in the presence of uncertainty. Stability analysis for the presented fault detection and diagnosis (FDD) scheme is provided. Furthermore, an extension of the proposed scheme for a class of non-linear systems with unmeasured states is presented. The efficiency and performance of the proposed scheme is evaluated through simulations that are performed for two well-known case studies. Comparison results highlight the superiority and capability of the proposed scheme.

In this study, the authors address the design of the multi-loop controllers for multi-input and multi-output (MIMO) processes for disturbance rejection. Three types of disturbance rejection are analysed, and they demonstrate that those disturbance responses are directly related to the integral terms of the controllers. A concept of non-parametric effective model is introduced here to decompose a MIMO control system into several equivalent single-input and single-output control systems. Based on the effective model in each loop, a non-convex optimisation problem is established to obtain the optimal controller parameters with certain robustness indices. Considering the drawbacks of using random search algorithm, they develop a numerical algorithm to solve this non-convex optimisation problem. Simulation studies demonstrate that the proposed method improves the disturbance rejection compared with other decentralised controllers.

Robust control design is a central problem for quantum systems in practical implementation and applications. In this study, the authors present a systematic methodology of sampling-based learning control via path planning for state transfer of quantum systems with uncertainties and bounded controls. The authors formulate the control problem of a quantum system with bounded uncertainties as the problem of steering this system to a target state with bounded controls via an optimised evolution path to achieve a satisfactory level of fidelity. To find the optimised path (controls), the authors present a combined design method of sampling-based learning control and path planning. The numerical results on an example of a four-level quantum system show the effectiveness of the proposed learning control design method. The method provides a useful design approach for learning control of quantum systems with uncertainties.

This study is concerned with the robust extended Kalman filtering problem for non-linear attitude estimation systems with multiplicative noises and unknown external disturbances. The multiplicative noises are modelled by random variables with bounded variance. The unknown external disturbances are described to lie in bounded set. The objective of the addressed attitude estimation problem is to design a filter such that, in the presence of both the multiplicative noises and unknown external disturbances, an optimised upper bound on the state estimation error variance can be guaranteed. Thus, a robust extended Kalman filter (REKF) is presented for attitude estimation with multiplicative noises and unknown external disturbances. Compared with the traditional extended Kalman filter in attitude estimation, the proposed algorithm takes into consideration the effects of multiplicative noises and unknown external disturbances. Moreover, the stability of the proposed REKF can be proved under certain conditions by utilising the stochastic stability theory. Finally, the simulation results demonstrate the effectiveness of the proposed REKF.

This study addresses the problem of exponential stability for a class of impulsive positive systems with mixed time-varying delays. A delayed impulsive positive system model is introduced for the first time and a necessary and sufficient condition guaranteeing the positivity of this kind of system is proposed. By using a copositive Lyapunov–Krasovskii functional and the average impulsive interval method, a sufficient criterion of global exponential stability for delayed impulsive positive systems is established in terms of linear programming problems. A numerical example is given to show the effectiveness of the proposed method.

This study investigates the problem of distributed estimation for non-linear system of sensor networks with unknown inputs affecting both the system state and outputs. A novel ‘information filtering algorithm’ is derived by reconstructing the non-linear version of the extended recursive three-step filter (NERTSF) into the information filter architecture, which simultaneously estimates the state and the unknown input, denoted as non-linear version of the extended recursive three-step information filter (NERTSIF). Afterwards the information filter is extended to the ‘derivative-free’ version with the help of the cubature Kalman filter (CKF) according to the linear error propagation methodology. A distributed filtering algorithm, based on the derivative-free version of the NERTSIF is proposed in which each sensor node only fuses the local observation instead of the global information and updates the local information state and matrix from its neighbours’ estimates using the dynamic average-consensus strategy. The efficacy of the proposed distributed algorithm is demonstrated by simulation examples on target tracking problem and is compared with existing algorithms such as centralised fusion filter and distributed CKF, which lack in tracking the true dynamics of the unknown input.

Flight performances of unmanned helicopters can be affected by wind gusts especially when they are carrying out aggressive mission. In this study, robust position control problem under aggressive manoeuvres is investigated in the presence of time-varying wind disturbances for a lab helicopter. A robust controller is designed in two steps: first, a nominal static linear feedback controller is applied to obtain desired tracking for the nominal system; then, a robust compensator is introduced to restrain the influences of parameter uncertainties, non-linear uncertainties and external wind disturbances. It is proven that the position tracking errors of the closed-loop system can be guaranteed to converge to a neighbourhood of the origin with designated boundary. Experimental results on the 3-degrees of freedom helicopter confirm the effectiveness of this method.

In this study, a novel two-step methodology is applied in designing static output-feedback controllers for a class of vehicle suspension systems. Following this approach, an effective synthesis of static output-feedback controllers can be carried out by solving two consecutive linear matrix inequality optimisation problems. To illustrate the main features of the proposed design strategy, two different static output-feedback H _∞ controllers are designed for a quarter-car suspension system. The first of those controllers uses the suspension deflection and the sprung mass velocity as feedback information, whereas the second one only requires the sprung mass velocity to compute the control actions. Numerical simulations indicate that, despite the restricted feedback information, the proposed static output-feedback H _∞ controllers exhibit a good behaviour in terms of both frequency and time responses, when compared with the corresponding state-feedback H _∞ controller.

This study is concerned with the problem of finite-time boundedness analysis for switched delay systems with the average dwell time. By combining the Jensen inequality and the reciprocally convex approach, a sufficient condition is derived to guarantee the finite-time boundedness of switched linear systems subject to time-varying delays. It is shown that the obtained result can achieve better performance comparing with the previous ones. Finally, simulation examples are provided to demonstrate the theoretical results.

This study studies the problem of setpoint output tracking for networked control systems with network-induced delay, packet disorder and packet dropout. Based on the input–output difference equation model, a novel networked predictive control (NPC) method via static output feedback integral controller (SOFIC) is proposed to actively compensate for the round-trip time (RTT) delay resulting from the aforementioned communication constraints. It is analysed that the resulting NPC system can achieve a similar tracking performance as that of the corresponding local control system. Based on the performance analysis, a necessary and sufficient condition for the stability of the closed-loop NPC system is obtained, which is independent of the RTT delay. Both numerical simulations and practical experiments on an internet-based servo motor system illustrate the effectiveness of the proposed method.

By constructing an objective function and using the gradient search, a gradient-based iteration is established for solving the coupled matrix equations A _i XB _i  = F _i , i = 1, 2, …, p. The authors prove that the gradient solution is convergent for any initial values. By analysing the spectral radius of the iterative matrix, the authors obtain an optimal convergence factor. An example is provided to illustrate the effectiveness of the proposed algorithm and to testify the conclusions established in this study.

This study is concerned with the problem of stabilisation for a class of two-dimensional (2D) nonlinear systems with intermittent measurements and sector nonlinearities. The intermittent measurement is modelled by a stochastic variable satisfying the Bernoulli random binary distribution. Our attention is focused on the design of a state feedback controller for such 2D stochastic system described by the Roesser model, such that the closed-loop system is mean-square asymptotically stable. A sufficient condition is established by means of linear matrix inequalities technique, and formulae can be given for the control law design. The result is also extended to more general cases where the system matrices contain uncertain parameters. Numerical examples are also given to illustrate the effectiveness of proposed approach.

This addendum is concerned with a remark about the conversion of interval uncertainties into a polytope.