In this study, automated generation of linear parameter-varying (LPV) state-space models to embed the dynamical behaviour of non-linear systems is considered, focusing on the trade-off between scheduling complexity and model accuracy and the minimisation of the conservativeness of the resulting embedding. The LPV state-space model is synthesised with affine scheduling dependency, while the scheduling variables themselves are non-linear functions of the state and input variables of the original system. The method allows to generate complete or approximative embedding of the non-linear system model and also it can be used to minimise the complexity of existing LPV embeddings. The capabilities of the method are demonstrated on simulation examples and also in an empirical case study where the first-principle motion model of a three degrees of freedom (3DOF) control moment gyroscope is converted by the proposed method to an LPV model with low scheduling complexity. Using the resulting model, a gain-scheduled controller is designed and applied on the gyroscope, demonstrating the efficiency of the developed approach.

The flocking of a multi-agent system with dynamic topology is investigated in this study. A switching control law is developed to achieve the flocking by adding the controls from some of type II uninformed agents to their closest informed agent including the non-linear relative velocity feedback. The multiple Lyapunov functions are then constructed and a stable analysis for the flocking is given based on the LaSalle invariance principle. It is proven that the energy is bounded at all time, and asymptotically converges to the state of minimum energy; the velocities of all the agents approach the velocity of the virtual leader asymptotically; no collision happens between the mobile agents. The effects of the number of informed agents and non-linear relative velocity feedbacks on the flocking are also investigated. It is shown that fewer informed agents in comparison with existing methods can guarantee stable flocking, which is very significant in some practical applications for secure communication.

In this study, a novel distributed adaptive controller is provided for consensus control of high-order non-linear multi-agent systems with unknown time-varying delays. The system is subject to uncertain disturbances, and the agents' dynamics are not known. Unlike the existing literature, the proposed method does not require time-delay terms in system dynamics to be bounded. A neural network is used to model the unknown non-linear dynamics. Then, despite the destabilising effect of the unknown delays, some adaptive rules based on the dynamic surface control are designed to achieve the consensus objective. The semi-global uniform boundedness of the resultant closed-loop signals and the convergence of the tracking errors to a neighbourhood of the origin are shown mathematically. Simulations verify the effectiveness of the results.

This study deals with the multiple Lyapunov functions approach for the robust leader-following consensus of one-sided Lipschitz (OSL) multiagents, connected via switching topologies under external disturbances. Disturbances have been accounted for followers, connected via a directed graph with a spanning tree from the leader root. The authors stipulate two consensus protocols, based on the absence or existence of norm-bounded reference input to the leader, and provide matrix inequalities for designing the parameters of consensus controllers. In the presence of bounded disturbances, the proposed protocol ensures consensus criterion for minimisation of the effect of disturbances to the consensus error between the leader and followers. A remedy for striking robustness against external perturbations has been provided in the present work in contrast to the conventional consensus schemes on OSL agents. Switching OSL mobile agents and aircraft carriers consisting of a leader and five followers have been simulated in the existence of reference input and disturbances to demonstrate the empirical proficiency of the consensus protocol schemes.

For Markov switched stochastic functional differential systems (SFDSs), asymptotic property is one of the most desired issues. Recently, a new class of delay-dependent asymptotic stability for non-linear Markov switched SFDSs was investigated. However, the existing references do not take the convergent speed and non-autonomous factor into consideration. Therefore, by means of multiple Lyapunov–Krasovskii functionals, this study is devoted to examine the exponential stability for highly non-linear autonomous Markov switched SFDSs and the exponential stability, polynomial stability and polynomial growth at most for highly non-linear non-autonomous systems, where all the criteria rely on the intervals lengths of continuous delays. In addition, the properties of boundedness and asymptotic stability are as well explored.

This study investigates the neural-network (NN)-based adaptive decentralised controller design issue for a class of large-scale non-linear non-strict-feedback interconnected systems with time-varying asymmetric output constraints and dead-zone inputs. The existences of the non-strict-feedback structure, time-varying asymmetric output constraints, and dead-zone inputs lead to the construction of the controller of each subsystem is very difficult. By applying the inherent property of Gaussian functions employed in radical basis function NNs, the non-strict-feedback structure is skillfully handled, and the adaptive backstepping method is used to construct the desired controllers of all subsystems. Furthermore, Lyapunov stability analysis shows that all the signals of the closed-loop system are ultimately bounded, and each subsystem output can converge to an arbitrarily small and predefined time-varying range with the corresponding constraint is always satisfied by employing a barrier Lyapunov function. Finally, simulation results based on a practical example prove the effectiveness of the proposed design strategy.

This study considers to asymptotically stabilise a switched linear system which transmits its feedback signals through a digital communication network. Such network can only provide a finite bit rate to represent feedback signals. In the concerned switched linear system, the mode mismatch may occur between the sensor and the controller due to the unknown switching time instants. It assumes that the mode switching is slow enough in the sense of combined dwell time and average dwell time, each individual mode is stabilisable and the available feedback bit rate is high enough, but still finite. In order to save network bandwidth resources, novel event-triggering strategies are implemented, i.e. only when pre-defined events are triggered, the system samples and transmits feedback signals. Then under the proposed event-triggering strategies, a stabilising bit rate condition for the concerned switched linear system is derived. Moreover, this study proves that the obtained stabilising bit rate under the proposed event-triggering strategies can be strictly lower than the stabilising bit rate required by the earlier work based on periodic sampling. Simulations are done to illustrate the bit rate superiority of the proposed event-triggering strategies.

In this study, the exponential synchronisation of second-order stochastic complex dynamical networks (SOSCDNs) is considered. A novel control called periodically intermittent pinning discrete observations control is proposed, which is based on discrete-time state observations instead of continuous-time state observations during the control time. To the authors' knowledge, the above control strategy has rarely been investigated in previous researches. Compared with the existing literature, there are no restrictions on the network topology structure in this study. Furthermore, by employing the Lyapunov method, stochastic analysis techniques, and matrix theory, a synchronisation criterion and a corollary about the exponential synchronisation of the SOSCDNs are presented. In particular, when the duration between two consecutive observations tends to zero, periodically intermittent pinning discrete observations control degenerates into periodically intermittent pinning control, and relevant corollaries are also derived. Finally, they applied theoretical results to single-link robot arm systems and oscillator systems. Meanwhile, the numerical simulations are performed to demonstrate the effectiveness of their results.

This study presents a linear parameter varying (LPV) approach for the lateral control of autonomous vehicles, in order to take into account the whole operating domain of longitudinal speeds, as well as the variation of the look-ahead distance. Combining a dynamical vehicle model with look-ahead dynamics, together with an identified actuator model including an input delay, the closed-loop performances can be achieved and the tracking capabilities can be improved for every speed. This is obtained in particular through ad hoc representation of the look-ahead time as a parameter-dependent function. An LPV control problem is formulated considering parameter-dependent weighting functions, allowing the control adaptation for all speeds. The synthesis is performed using the gridding approach, in order to account for varying parameter rate. The proposed steering control system has been implemented on a real electric Renault Zoe car. The performances are therefore assessed experimentally on a real test track with a varying longitudinal speed profile, and compared with a classical LPV polytopic controller, which proves the advanced lane-tracking capabilities of the proposed methodology.

There is a consistent demand for reliable, efficient, small-sized, and light weight power supply for electronics devices. To meet the aforesaid requirements, Quasi Resonant converters are being used due to low switching losses, high efficiency, small size and weight. With these motivations, in this paper, two different methodologies of passivity-based controllers namely (EL-PBC and PCH-PBC) are proposed for Zero Voltage Switching Quasi Resonant (ZVS-QR) boost converter to regulate the output voltage. First, the Euler-Lagrange (EL) formulation is used to derive state-space equations for ZVS-QR boost converter under different operating modes and then, average state-space model is developed using generalized state space average (GSSA) technique. In order to improve the dynamic performance of the ZVS-QR boost converter, the aforesaid passivity-based controllers are designed and its stability analysis is carried out. The entire system is developed with Sim Power System toolbox of MATLAB software and validated through OP-5142 real time simulator. Steady state and dynamic performances are examined under several operating conditions. It is observed that the passivity-based controllers of the paper achieve robustness against the uncertainties at the input and the load side. It is compared with benchmark PI controller as well.

Optimal tracking of continuous-time non-linear systems has been extensively studied in literature. However, in several applications, absence of knowledge about system dynamics poses a severe challenge in solving the optimal tracking problem. This has found growing attention among researchers recently, and integral reinforcement learning based method augmented with actor neural network (NN) have been deployed to this end. However, very few studies have been directed to model-free optimal tracking control that helps in attenuating the effect of disturbances on the system performance without any prior knowledge about system dynamics. To this end, a recursive least square-based parameter update was recently proposed. However, gradient descent-based parameter update scheme is more sensitive to real-time variation in plant dynamics. Experience replay (ER) technique has been shown to improve the convergence of NN weights by utilising past observations iteratively. Motivated by these, this study presents a novel parameter update law based on variable gain gradient descent and ER technique for tuning the weights of critic, actor and disturbance NNs. The presented update law leads to improved model-free tracking performance under -bounded disturbance. Simulation results are presented to validate the presented update law.

Vehicle sideslip angle is a major indicator of dynamics stability for ground vehicles; but it is immeasurable with commercially-available sensors. Sideslip angle estimation has been the focus of intensive research in past decades, resulting in a rich library of related literature. This study presents a comprehensive evaluation of state-of-the-art sideslip angle estimation methods, with the primary goal of quantitatively revealing their strengths and limitations. These include kinematics-, dynamics- and neural network-based estimators. A hardware-in-loop system is purposely established to examine their performance under four typical manoeuvres. The results show that the dynamics-based estimators are suitable at low vehicle velocities when tires operate in the linear region. In contrast, the kinematics-based methods yield superior estimation performance at high vehicle velocities, and the inclusion of the dual GPS receivers is beneficial even when there is large disturbance to the steering angle. Of utmost importance, it is experimentally manifested that the neural network-based estimator can perform well in all manoeuvres once the training datasets are properly selected.

For scanning applications, damping and tracking controllers are employed in a dual-loop fashion. Whilst these damping and tracking controllers are designed sequentially in literature (damping first, tracking later), it has been found that the tracking controller (typically integral or proportional–integral) influences the 'desired' pole locations (and thereby its damping performance) achieved by the positive acceleration, velocity and position feedback (PAVPF) damping controller. This work starts by first highlighting this unwanted effect that results in low positioning bandwidth. To address this drawback, this work presents the design, analysis and experimental validation of the simultaneous design method for the PAVPF control-based combined damping and tracking scheme, aimed at achieving accurate, high-bandwidth nanopositioning. It also details a recursive analytical method to simultaneously optimise the damping and tracking controller parameters resulting in almost a three-fold increase in closed-loop bandwidth when compared with the traditional sequential method. To further confirm the advantages of the proposed simultaneous design method, comparative experimental results conducted on one axis of a piezo-actuated nanopositioner are presented. Significant improvements in the steady-state positioning as well as transient response are noted. These improvements combined, result in significant gains in the raster scanning performance of nanopositioning stages.

This study focuses on position tracking control for the networked predictive motion control system with random communication delay. First, the output feedback controller is designed by networked predictive control law to actively compensate the time delay induced by the random channels of the motion control system. A closed-loop model is established for the networked predictive motion control system with random bounded communication delay, modelled by a Markov chain. Then, the sufficient conditions of stability for the networked predictive motion control system are provided, by constructing the Lyapunov–Krasovskii functional, followed by the theoretical proof. Furthermore, the output feedback controller is constructed and the linear matrix inequality method is applied to obtain the designed controller gain. Last, the simulation and experimental results are presented to prove the effectiveness of the proposed method.

In this study, a novel co-estimation approach is proposed for dynamic state estimation of cyber-physical systems (CPSs) subjected to sensor attacks. In the proposed approach, the sensory measurements are preprocessed to extract some valuable information about the launched cyber-attack. This information is then manipulated to form new state equations, which are augmented with the CPS model. The newly obtained model effectively represents the behaviour of both the CPS states as well as the launched cyber-attack, and can be used for their co-estimation. Two structures are suggested for the preprocessing of the sensory measurements that deploy Kalman filter (KF) and two-step KF for co-estimation of the states and cyber-attacks. To effectively handle the concurrent effects of cyber-attacks and unknown inputs on the CPS co-estimation process, some auxiliary outputs are generated using high-gain approximate differentiators to realise the required matching conditions. The proposed dynamic co-estimation approach improves accuracy via considering the inter-correlation between the CPS states and the cyber-attacks. To demonstrate the viability of the proposed co-estimation method, thorough stability and feasibility analysis are provided. Furthermore, extensive simulations are performed on a well-known CPS-the IEEE 14-bus test system- to demonstrate the effectiveness of the proposed approach.

This study addresses an observer-based model predictive control (MPC) algorithm for a networked control system (NCS) under denial of service (DoS) attacks and actuator saturation via an interval type-2 Takagi–Sugeno (IT2 T–S) fuzzy model. With few studies undertaking the cyber security problems in the research of MPC algorithm, this study considers the data transmission problem when DoS attacks occur. Under DoS attacks, signals in the communication networks will be interfered. The probability of error-free packet reception depends on signal-to-interference-plus-noise ratio of the wireless transmission networks. In order to reduce the on-line computation, an off-line fuzzy observer is devised to estimate the system states. Meanwhile, an on-line MPC algorithm is proposed to minimise the performance objective function and obtain the secure model predictive controller gain. Besides, the recursive feasibility is ensured by refreshing the bound of estimation error. Finally, illustrative examples certificate the effectiveness of the presented method.

This study investigates the speed tracking control problem for the new hydraulic transformer controlled system based on the active regulating common pressure rail with both input delay and strong interference. Firstly, the Pade approximation approach is adopted to approximate and compensate for the input delay of the valve plate rotating controlled by the hydraulic swing motor. Then, the Levant filter is introduced to solve the ‘explosion of complexity’ caused by repeated differentiation of the virtual control signals, and the compensation mechanism is constructed to compensate for the filtering errors. Besides, due to the strong non-linear characteristics of the proposed system, the pi-sigma fuzzy neural network is employed as the function approximator to address the non-linear terms. Based on finite-time command control technology and the barrier Lyapunov functionals, a robust controller is synthesised to ensure a better error convergence performance while without violating the state constraint. Finally, the effectiveness of the controller is verified by comparison with the traditional control method and simulation result.

This study focuses on the finite-time (FT) control problem for linear Itô-type stochastic Markovian jump systems (SMJS). According to the characteristics of SMJS, a mode-dependent approach (MDA) is provided. Through MDA, state feedback and observer-based FT controllers are designed. Compared with the mode-independent approach, the advantages of MDA are clearly shown. In the sequel, two new optimisation algorithms for deriving the optimal FT controller are proposed. Finally, a numerical example is utilised to show the merits of the obtained results.

By using techniques borrowed from algebraic geometry, some tests are proposed to certify structural properties (such as reachability, observability, controllability, constructibility, stabilisability, and detectability) of discrete-time and continuous-time linear time-invariant systems. These results are then generalised for linear time-invariant systems depending polynomially on some real parameters, by exploiting the notion of Gröbner cover. The main innovation of the proposed results with respect to existing techniques is that they provide exact certificates for the structural properties of linear time-invariant systems. Examples of application are given all throughout the study to illustrate and validate the theoretical results.

Fault localisation for distributed parameter systems is as important as fault detection but is seldom discussed in the literature. The main reason is that an infinite number of sensors in the space are needed to construct a distributed residual signal, which is nearly impossible in practice. In this study, a fault detection and localisation filter which only uses a finite number of sensors is initiated based on an approximated ordinary differential equation model. Considering the limitations on computation resources for higher-order models in practice, a novel set of spatial basis functions is applied to the reduced-order fault detection and localisation filter design. Under certain conditions, the novel spatial basis functions obtain smaller state truncation error while the order is lower compared to the mostly used eigenfunctions of the spatial operator.

This study considers the boundary control problem of a time fractional partial differential equation (PDE) – fractional ordinary differential equation (ODE) cascaded system with spatially varying diffusivity and Dirichlet connection. Using the backstepping transformation, a full-state feedback law is obtained. Meanwhile, the well-posedness of kernel functions in this transformation is established theoretically. Subsequently, a Mittag-Leffler convergent boundary observer is derived, which composes with the proposed state feedback law to enable stabilisation by output feedback. With the designed state and output feedback controllers, the Mittag-Leffler stability of the closed-loop system is then proved by the fractional Lyapunov method. Finally, the results of numerical simulations are provided for fractional cascaded plants when neither kernel ODE nor kernel PDE has an explicit solution.

This study is concerned with the design of active queue management (AQM) subjected to quantisation errors. First, the authors will show here that quantisation errors exist when AQM experiences transmission control protocol (TCP) during the congestion. Second, a TCP/AQM model that takes into account quantisation errors is proposed – the considered quantisers are dynamic and have a scale parameter. Based on this model, stabilisation will be analysed in which the controller and the scaling parameters are obtained using the Lyapunov–Krassovskii functional method. Finally, Matlab simulation results are displayed to show the effectiveness of the proposed method.

This study presents an integrated control scheme of a diesel engine which aims to ensure quick torque response while limiting the average NOx emission below the certain specification. To start with, the sensitivity analysis is performed to recognize the key factors determined to balance emission, torque and fuel consumption. Considering the strong nonlinearity of the diesel engine, a multi-input multi-output (MIMO) linear parameter varying (LPV) model is developed. Unlike the conventional state-depended LPV modelling process, the proposed LPV model is built based on a kernel function with a novel form of state-free predictive equation so that the system outputs are directly computed. The control objective of coordinating torque tracking and emission reduction is then accomplished by designing a model predictive controller that regulates the fuel injection quantity, fuel injection angle, exhaust gas recirculation (EGR) rate and the opening degree of variable geometry turbine (VGT) valve. Finally, the developed LPV model and the MPC controller are testified with a high-fidelity commercial diesel engine model. The results show that the LPV-MPC controller is able to satisfy the NOx emission specification and the engine torque tracks the desired reference fast in transients, while the control variables are suitably actuated within the actuator constraints.

This study is dedicated to develop an adaptive output-feedback (OPFB) tracking control scheme for continuous-time linear systems to achieve the infinite-horizon linear quadratic tracking (LQT) solution. The existence conditions of the OPFB control for LQT solution are proposed and an upper bound is found for the discount factor to assure the stability of the OPFB solution. To develop an online learning solution without knowing the system drift dynamics, a novel value iteration (VI) algorithm is presented based on the integral reinforcement learning technique which requires measured augmented system states. Moreover, a convergence analysis is proposed for the VI algorithm. Compared to the policy iteration method, the VI algorithm relaxes the initial stabilising control policy requirement. Specifically, in order to further obviate the knowledge of system states, a neural network-based adaptive observer is used during learning and it is no longer needed after the online learning algorithm converges. Effectiveness of the proposed learning scheme is illustrated through an interesting application into a single-phase grid-connected PV power inverter.

This article studies the problem of event-triggered distributed adaptive bipartite consensus control for multiple autonomous underwater vehicle (AUV) systems with a fixed topology. Different from the existing literature on multiple AUV systems, the competitive relationship among AUVs is taken into consideration. In this situation, the bipartite consensus control is implemented to complete the controller design. Furthermore, combing the relative threshold strategy and the backstepping method, an adaptive event-triggered control scheme is developed to decrease the updating frequency of control input. Through the Lyapunov analysis, the proposed protocol ensures that the position tracking error of the considered system converges to a small neighbourhood near the origin. Finally, a simulation example is given to show the effectiveness of the control scheme.

In this study, the authors are concerned with the long-standing mixed problem for discrete linear systems. The main contribution is to derive the standard solution of the mixed problem by a regular Riccati equation. Firstly, in -optimisation procedure, the optimal strategy is explicitly given where the -norm is maximised by w and -norm is minimised by u at the same time. It is noted that there exists an arbitrary item in the optimal strategy for the positive semi-definite weighting matrices. Secondly, the -optimisation problem is explicitly solved with respect to the arbitrary item. Finally, the standard solution of the mixed problem is explicitly given in terms of regular Riccati equations by combining the optimal strategy in optimisation procedure and the optimal solution of optimisation procedure. Examples are presented to show the effectiveness of the proposed approach.

A novel method for observer-based control of disturbed polytopic linear parameter varying (LPV) systems with inexactly measured scheduling parameters is investigated in this study. Despite the imperfect scheduling parameters knowledge, the proposed control method ensures the closed-loop induced L ₂-norm performance criterion. Unlike the previous methods, the inexact scheduling parameters are not assumed to be proportional to the original scheduling parameters of the LPV system. Employing both cases of parameter-dependent and parameter-independent Lyapunov functions, sufficient design conditions in terms of linear matrix inequalities are provided using Finsler's lemma. Numerical examples are included to demonstrate the efficiency of the proposed method and its superiority over the previous techniques. Moreover, a practical case study, i.e. clutchless automated manual transmission system is considered and controlled by the developed approach numerically.

In this study, a fault-tolerant position tracking control of a quadrotor unmanned aerial vehicle (UAV) is addressed by proposing a model reference interval type-2 (IT-2) fuzzy-model-based sliding mode tracking control methodology. Considering the underactuated characteristic of the quadrotor UAV, first, the authors separate the overall dynamics of the quadrotor into the attitude, altitude, and position subsystems. Moreover, each of them is represented via IT-2 fuzzy model to deal with uncertainties of its membership functions. After then, a linear reference model supposed to be tracked by each subsystem is designed, on which each tracking error dynamics is derived. Given the tracking error dynamics and additive actuator faults, an IT-2 integral fuzzy sliding surface is proposed to enhance the robust tracking performance of the entire system. As a result, a linear matrix inequality-based sufficient condition is derived to guarantee the asymptotic stabilisation as well as satisfying an tracking performance. Furthermore, a reachability condition of the designed sliding surface is also proposed. Finally, the authors provide design examples to demonstrate the effectiveness of the proposed position tracking control methodology.

In this study, the authors present an effective online tube-based model predictive control (T-MPC) solution for autonomous driving that aims at improving the computational load while ensuring robust stability and performance in fast and disturbed scenarios. They focus on reformulating the non-linear original problem into a pseudo-linear problem by transforming the non-linear vehicle equations to be expressed in a linear parameter varying (LPV) form. An scheme composed by a nominal controller and a corrective local controller is proposed. First, the local controller is designed as a polytopic LPV-H controller able to reject external disturbances. Moreover, a finite number of accurate reachable sets, also called tube, are computed online using zonotopes taking into account the system dynamics, the local controller and the disturbance-uncertainty bounds considered. Second, the nominal controller is designed as an MPC where the LPV vehicle model is used to speed up the computational time while keeping accurate vehicle representation. They test the presented scheme and compared the local controller performance against the LQR design as state-of-the-art approach. They demonstrate its effectiveness in a disturbed fast driving scenario being able to reject strong exogenous disturbances and fulfilling imposed constraints at a very reduced computational cost.

The problem of leader–following consensus is investigated for heterogeneous discrete-time linear networked multi-agent systems with data packet losses and network delays. Two kinds of networked protocols are presented to derive the states of followers to reach an agreement on leader's state under the switching topologies, in the meanwhile, the asymptotic stability of all agents are guaranteed. Due to unavailable agents' states and communication constraints, the networked predictive control method is used to compensate network delays and data packet losses actively. Sufficient criteria of leader–following consensus and asymptomatic stability are obtained when at least one follower is connected to the leader by a directed edge. The simulation example is presented to illustrate that states of followers can track leader's state, and all agents' states also asymptotically converge to the equilibrium under the proposed theoretical results.

This study is relevant to the topic of a robust event-triggered mechanism for the Takagi–Sugeno (T–S) fuzzy system with successive time-delay (STD) signals and its application, where the uncertainties satisfy the randomly occurring form. Firstly, an event-triggered communication scheme is introduced, which can adaptively adjust the communication threshold to save limited communication resource. The primary aim of this study is to model an event-triggered mechanism with STD, which ensures that the suggested T–S fuzzy system achieves extended dissipative with permissible uncertainties. Secondly, by using the relaxed integral inequality technique, single and double auxillary function-based integral inequalities to evaluate the derivative of the designed Lyapunov–Krasovskii functional, quadratically stable condition is established for the delayed fuzzy system in terms of linear matrix inequalities and analyse the , passivity, mixed and passivity, -dissipativity execution by choosing the weighting matrices can be solved simultaneously in a standard framework based on the idea of extended dissipative. Finally, simulation studies are given to verify the effectiveness of the derived results, among them one example was supported by the real-life application of the benchmark problem in the sense of STD signals.

In this study, the observer-based output feedback control problem is investigated for the two-dimensional (2D) networked control system (NCS) in form of Fornasini–Marchesini second model. The communication from the controller to the actuator is carried out through a shared network medium, where only one signal gets the network access at each transmission instant. To avoid data collisions, the periodic scheduling protocol is employed to assign the network access priority to the actuator in chronological order. To improve the reliability of data transmission, redundant channels are applied to deliver the measurement observed by the sensor to the controller. The aim of the addressed problem is to design an observer-based output feedback controller so that the considered 2D NCS satisfy a predefined performance index. Sufficient condition is given to ensure the performance of the closed-loop system via the Lyapunov theory and stochastic analysis. Moreover, the observer-based output feedback controller is designed to achieve the desired performance index, under which the controller parameters are determined by solving certain optimisation problems. Finally, a numerical example is given to demonstrate the feasibility of the proposed approach.

An improved robust stability surface analysis method is proposed in this study for fractional-order time-delay systems. Through the stabilisation process, a synthesised fractional-order PD controller can be designed with guaranteed robustness specifications. Firstly, the specification for improved robustness requirements is proposed. In order to find selectable robust controller design parameter combinations for the controlled system, stability region is discussed based on the stability boundary locus, and the robust stability surface is derived straight after. Secondly, the selectable parameter combinations are checked to find the one that best fulfils all the proposed robustness specifications. Finally, numerical simulations are given to demonstrate the effectiveness and flexibility of the presented control algorithm.

This study considers the constrained consensus problem of continuous-time multi-agent networks with hypercube state constraints, non-convex input constraints and non-uniform delays. It is assumed that each agent can only perceive its own constraint sets, the communication graphs are switching over time and the joint communication graphs are strongly connected. By introducing the time-varying gains and the constraint operators, a new distributed algorithm is proposed. Then, it is proved that the constraint consensus can be reached under the proposed algorithm by reduction, while the states and the inputs are constrained to stay at the corresponding constraint sets. Finally, simulation examples are given to examine the effectiveness of the proposed results.

Virtual reference feedback tuning (VRFT) is a data-driven control method that optimises the response of a closed-loop system to reference tracking at the expense of a not optimised response to disturbance rejection. This work presents the application of VRFT to two cascade control structures usually considered in the control literature to improve the overall closed-loop performance for single-input systems. For both structures, the authors derive the optimal and practical filter formulation to cope with the case where the controller to be designed is underparameterised. Also, in the case where the internal controller is at the feedback path, they derive a new approach that uses data from a single experiment and considers a unique reference model. Simulation examples illustrate the importance of using the VRFT filter, along with the performance improvements obtained from both cascade loops in terms of reference tracking and disturbance rejection.