This study exploits an optimal control approach for solving the general robust control problem of active pantograph suspension systems with actuator delays and time-varying contact force such that both the stabilisation and optimal performance are achieved. On the basis of Bellman's optimality principle and Razumikhin theorem, the general robust control design problem can be equivalently transformed into an optimal control problem with the amount of matched uncertainties involved in the performance index. A stability criterion has been developed under which the time varying stiffness of contact force and time-delayed actuation force can not only achieve stability, but also acquire the guaranteed level of performance for regulation. A suitable linear state feedback control law is characterised via Lyapunov stability theory to ensure quadratic stability and performance robustness of the closed-loop systems. The effectiveness of the proposed design is demonstrated through simulation studies.

In this study, a novel adaptive neural network (NN)-based leader-following consensus approach is proposed for a class of non-linear second-order multi-agent systems. For the existing NN consensus approaches, to obtain the desired approximation accuracy, the NN-based adaptive consensus algorithms require the number of NN nodes to must be large enough, and thus the online computation burden often are very heavy. However, the proposed adaptive consensus scheme can greatly reduce the online computation burden, because the adaptive adjusting parameters are designed in scalar form, which is the norm of the estimation of the optimal NN weight matrix. According to Lyapunov stability theory, the proposed approach can guarantee the leader-following consensus behaviour of non-linear second-order multi-agent systems to be obtained. Finally, a numerical simulation and a multi-manipulator simulation are carried out to further demonstrate the effectiveness of the proposed consensus approach.

Identification and composite control of Hammerstein system with unknown order linear dynamics and a static hysteresis non-linearity modelled by Preisach operator is investigated. The order of linear dynamics is firstly determined by Hankel matrix approach and then a blind identification is implemented to identify the linear dynamics based on the over-sampling output measurements only. Then a novel deterministic approach is proposed to identify the Preisach model for hysteresis non-linearity, which is devoted to identify a triangle matrix. This novel approach needs less dimensions to obtain Preisach density function than other existing methods. Finally, a composite control consisting of discrete inverse model-based controller (DIMBC) and discrete adaptive sliding mode controller (DASMC) is developed to achieve tracking control. The composite control can reduce the reaching time of DASMC and improve the robustness of DIMBC. Experiments based on a turntable servo system demonstrate the effectiveness of the proposed identification and control methods.

This study is concerned with the event-triggered distributed ℋ_∞ state estimation problem for a class of discrete-time stochastic non-linear systems with packet dropouts in a sensor network. An event-triggered communication mechanism is adopted over the sensor network with hope to reduce the communication burden and the energy consumption, where the measurements on each sensor are transmitted only when a certain triggering condition is violated. Furthermore, a novel distributed state estimator is designed where the available innovations are not only from the individual sensor, but also from its neighbouring ones according to the given topology. The purpose of the problem under consideration is to design a set of distributed state estimators such that the dynamics of estimation errors is exponentially mean-square stable and also the prespecified ℋ_∞ disturbance rejection attenuation level is guaranteed. By utilising the property of the Kronecker product and the stochastic analysis approaches, sufficient conditions are established under which the addressed state estimation problem is recast as a convex optimisation one that can be easily solved via available software packages. Finally, a simulation example is utilised to illustrate the usefulness of the proposed design scheme of event-triggered distributed state estimators.

In this study, the authors address the leaderless consensus problem of multiple robotic manipulators with communication delays. Taking dynamic uncertainties of the robotic agents into consideration, they perform the distributed adaptive position feedback control scheme design for strongly connected information topology, where the communication is subject to constant and bounded time delays. The novel distributed sliding observer is first proposed such that the joint velocities can be estimated online. Then, the distributed adaptive controller with dynamic parameter updating is constructed based on the observed information. The convergence of consensus errors is proved with Lyapunov stability analysis tool. Finally, simulations are performed on networked robotic manipulators to validate the effectiveness of the theoretical approach.

This study investigates the issue of mean square consensus for multiple agents connected by a directed network. The graph of the network is supposed to have a spanning tree and each agent is taken as a Markovian jumping system. By utilising the event-triggered strategy, some sufficient conditions for consensus are presented whether the transition rates for the Markov chain being completely known or not. Furthermore, the event-triggered function designed in this study is dependent on the stochastic sampled-data from neighbouring agents. Theoretical results are provided according to the graph theory, Lyapunov functional and linear matrix inequality approach. Finally, a numerical example is given to demonstrate the effectiveness of the theoretical analysis.

This study presents a state estimation algorithm for the stochastic hybrid system with event-based sampling. In event-based sampling, sensors transmit their measurements to an estimator only when predefined events happen, to reduce the communication cost. On the basis of the event-based sampling, the hybrid state estimation problem is formulated as to compute the probability density of the hybrid state with the sequence of noisy measurements generated at certain events. This hybrid state estimation problem is challenging since it requires computation of the exponentially increasing number of probabilities of the discrete state histories and evaluation of the multivariate integration. The proposed event-based hybrid state estimation algorithm utilises the interacting multiple model approach and pseudo measurement generation method to overcome these difficulties. The algorithm is then demonstrated with an illustrative aircraft tracking example.

In this study, an integral-based event-driven mechanism is proposed for a general class of non-linear systems. The proposed scheme is less conservative than earlier work on the subject and achieves asymptotic stability without forcing the derivative of the Lyapunov function to be negative between samples. A rigorous proof is given, showing that the proposed triggering condition is more effective than the corresponding traditional approaches. Simulation results are provided to illustrate the effectiveness of the proposed solution.

As distributed parameter systems, dynamics of freeway traffic are dominated by the current traffic parameter and boundary fluxes from upstream/downstream sections or on/off ramps. The difference between traffic demand–supply and boundary fluxes actually reflects the congestion level of freeway travel. This study investigates simultaneous traffic density and boundary flux estimation with data extracted from on-road detectors. The existing studies for traffic estimation mainly focus on the traffic parameters (density, velocity etc.) of mainline traffic and ignore flux fluctuations at boundary sections of the freeway. The authors propose a stochastic hybrid traffic flow model by extending the cell transmission model with Markovian multi-mode switching. A novel interacting multiple model filtering for simultaneous input and state estimation is developed for discrete-time Markovian switching systems with unknown input. A freeway segment of Interstate 80 East (I-80E) in Berkeley, Northern California, is chosen to investigate the performance of the developed approach. Traffic data is obtained from the performance measurement system.

The interconnection of regional power grids has resulted in inter-area low-frequency oscillation, which has become a serious problem that threatens the security of power systems. Wide-area power system stabiliser (WAPSS) is an effective device to damp this oscillation. Given that the interconnected multi-machine power system is a strongly non-linear and complex system with time-varying structure and operating conditions, satisfactory control performance of WAPSS is difficult to obtain by using conventional model-based control methods. In this study, a data-driven control method called model-free adaptive control (MFAC) is introduced to WAPSS design. The MFAC algorithm is improved to meet the wide-area damping control requirements in consideration of system disturbances. The stability of the closed-loop system with the improved MFAC-WAPSS is also analysed, and the parameter settings of the improved MFAC algorithm are provided in detail. The improved MFAC-WAPSS can adjust its parameters adaptively only based on the input and output data and avoid the complex system modelling process. The proposed adaptive WAPSS is evaluated on a simplified New England power system. Simulation results show the effectiveness of this new technique.

By using piecewise constant control, the authors embed planar bilinear system into switching control model. On the basis of interpreting switching stabilisation mechanism in terms of return ratio, they develop a systematic approach to solve the piecewise constant control from the switching law that minimises return ratio. The existence and construction of stabilisation control in a strong way rely on an invariant that characterises the interrelation of the system matrices.

This study is concerned with the problem of finite-time stochastic bounded (FTSB) control for discrete-time stochastic system with communication constraint, in which there may exist external bounded disturbance and state-dependent noise. In particular, the state of system may loss when transmitted via communication networks. In this study, a compensating strategy is employed to cope with the packet dropout. Moreover, the concepts on FTSB are introduced and relevant issues are discussed. By constructing suitable Lyapunov–Krasovskii functional, sufficient conditions dependent on both dropout probability and specified work time are derived to ensure the FTSB of the closed-loop system. Meanwhile, a prescribed H _∞ performance is also achieved. Finally, an F-18 aircraft model is employed to illustrate the proposed approach.

The aim of this study is to develop a new feedback stabilisation strategy involving a non-linear potential feedback control (PFC) for scalar discrete-time systems with disturbances by using Lyapunov functions. The main interest of the PFC is to achieve the stabilisation with a constraint on the state space. The authors apply this strategy for the uplink power control in a real wireless sensor network and compare with the adaptive transmission power control (ATPC) usually used in telecommunications. A study of the energy saving is also provided.

An analytical expression for the exact identification of two-input two-output (TITO) process using hysteresis relay is presented in this study. The identified transfer matrix consists of four independent first-order plus delay time (FOPDT) models and the time constants, delays and the gains of the FOPDT models are obtained from the limit cycle measurements. In this study, analytical expressions are derived using state-space analysis to identify the exact transfer matrix of the TITO process (TITO system). Problem of chattering and measurement noise is attenuated using relay with hysteresis. The simulation examples and real-time experiment show the effectiveness of the proposed method.

In this study, the problem of robust stabilisation is considered for a class of uncertain dynamical systems with state delays. Here, it is assumed that the upper bounds of the delayed state perturbations, uncertainties and external disturbances are completely unknown. For such a class of uncertain time-delay dynamical systems, a novel design method is presented in which a class of continuous memoryless robust stabilising controllers is constructed. In particular, by making use of the presented method, it is not required to introduce some adaptive schemes to update the unknown upper bounds, which makes the control schemes simpler than adaptive robust control schemes reported in the control literature. Moreover, such a control scheme proposed in this study will be called adaptation-free robust control scheme. It is also shown that the solutions of the resulting closed-loop time-delay dynamical systems can be guaranteed to be uniformly ultimately bounded. Finally, the simulations of some numerical examples are provided to demonstrate the validity of the theoretical results.

This study deals with the H _∞ filtering problem for a class of singular biological systems with stochastic exogenous disturbance. Singular system theory is utilised to build a stochastic singular food chain system. The authors’ purpose is to estimate the unmeasurable state variables of the food chain system by the available measurements. The general condition is established for a class of non-linear singular systems to achieve input-to-state stable in mean and the prescribed H _∞ performance level. A sufficient condition of the solvability of filtering problem is proposed for the singular food chain system. Simulation example is provided to illustrate the effectiveness of the proposed filtering approach developed in this study.