This study investigates the output synchronisation of a heterogeneous network of agents affected by parameter perturbations. The authors use the output regulation theory to solve this problem. The main result is the construction of the reference generators and the robust regulators, which can guarantee the outputs of the non-identical agents track the output of the leader. The designed method includes two steps, which can be seen as a kind of separation principle. Firstly, the outputs of a network of local identical reference generators are forced to asymptotically follow the output of the leader. Secondly, under the control of an individual regulator, the output of each agent is forced to track the output of the corresponding local reference generator, via robust output regulation theory.

This article studies the problem of adaptive control for a class of singular Markovian jump systems, upper bound of uncertain transition rates of which is unknown. Firstly, an adaptation law is developed to estimate the unknown parameter. Then, a new class of adaptive state feedback controller is proposed such that not only is the estimated error bounded in probability but also the states of the closed-loop system are asymptotically stable in probability. Moreover, the presented method is successfully extended to another general case that the transition rate matrix is partially unknown. Similarly, the bound of the unknown elements is not necessarily given beforehand. Finally, a numerical example is given to show the validity and superiority of the developed results.

This study proposes a distributed model predictive control (MPC) strategy to achieve consensus of sampled-data multi-agent systems with double-integrator dynamics. On the basis of the error of state between each agent and the centre of its subsystem, a novel distributed MPC strategy (Algorithm 1) is obtained with the exchange of current states only. Then, a reverse iterative algorithm (Algorithm 2) is specially designed for the receding horizon optimisation of sampled-data double-integrator dynamics. Illustrative examples are finally displayed to verify the effectiveness and advantage of the distributed MPC consensus strategy and the impact of sampling period on consensus.

This study presents a practical method of neural network (NN) adaptive tracking control of uncertain port-controlled Hamiltonian (PCH) systems. NN is used to compensate for parametric uncertainties and unlike the previous studies, the dynamics of the NN tuning law is driven by both the position as well as the velocity errors owing to the introduction of the information preserving filtering of the Hamiltonian gradient. In addition, the proposed controller achieves the ℒ₂ disturbance attenuation objectives as well as preserves the PCH structure of the system in closed loop. Simulation examples demonstrate the efficacy of the proposed approach.

In this study, a neural-network-based online learning algorithm is established to solve the finite horizon linear quadratic tracking (FHLQT) problem for partially unknown continuous-time systems. An augmented problem is constructed with an augmented state which consists of the system state and the reference trajectory. The authors obtain a solution for the augmented problem which is equivalent to the standard solution of the FHLQT problem. To solve the augmented problem with partially unknown system dynamics, they develop a time-varying Riccati equation. A critic neural network is used to approximate the value function and an online learning algorithm is established using the policy iteration technique to solve the time-varying Riccati equation. An integral policy iteration method and an online tuning law are used when the algorithm is implemented without the knowledge of the system drift dynamics and the command generator dynamics. A simulation example is given to show the effectiveness of the established algorithm.

In this study, a novel boundary control algorithm is designed for a class of Timoshenko beams with unknown external disturbance and uncertain input backlash non-linearity. The Timoshenko beam is described by partial differential equations as governing equations and ordinary differential equations as boundary conditions. The representation of input backlash is modified as a desired control input associated with input non-linear error, which will be transformed into the external disturbance. The unknown ‘disturbance-like’ item is defined based on input error and disturbance, and the boundary of this unknown ‘disturbance-like’ item will be estimated by a disturbance observer. The unknown backlash can be handled and the ultimately uniform stability of the closed-loop system is ensured. Numerical simulations are provided to verify the performance of the author's proposed control.

A novel approach to fuel–air ratio (FAR) control for spark ignition (SI) engines is presented in this study. The FAR dynamics are modelled as a first-order plus time-varying delay system. Time delay in the control plant is not approximated using the Pade formula. For controller design purposes, it is described as a time-varying delay in the measurement output. A gain-scheduled delay-dependent controller, regarding the time delay as a time-varying parameter, is then designed to track FAR reference and minimise the effects of disturbances on FAR regulation. The proposed controller guarantees the stability of closed-loop system and induced L ₂ norm performance using Lyapunov–Krasovskii functional. The design method is then formulated in terms of linear matrix inequalities that leads to a convex optimisation problem and can be solved by parameter gridding technique. Simulation results validate the FAR regulation of proposed controller over the large range of time delay, which covers most engine operating conditions in practice.

A multivariable non-singular terminal sliding mode control (NTSMC) strategy is developed combining disturbance observer (DO) technique for the six-degree-of-freedom reusable launch vehicles. First of all, the control-oriented model is established, followed by a design of finite-time DO, which is to estimate the model uncertainties and external disturbances. Second, based on the disturbance estimation, a composite controller combining multivariable NTSMC is developed to make the attitude tracking errors converge to zero in finite time. And then the stability of the closed-loop system is guaranteed through Lyapunov theory. Finally, the simulation results illustrate that the comprehensive control strategy performs well in tracking the guidance commands in spite of model uncertainties and external disturbances.

This study presents a high-performance wind generation system with induction machine (IM), specifically devised with the target of maximising the efficiency of the electromechanical conversion, and contemporary minimising the number of the system sensors and their cost. To this aim, the control system has been integrated, from one side, with an intelligent maximum power point tracking (MPPT) technique, so to make the generator track the power available in the wind, from the other side with techniques for the minimisation of the electrical losses (ELMT). Particularly, the power converters’ switching losses have been reduced adopting a discontinuous pulsewidth modulation, while the IM overall losses have been reduced by a suitable electric losses minimisation technique. Contemporary, to reduce costs and increase the reliability of the system, the system has been devised as a fully sensors-less generation unit, meaning that both the wind speed and the machine speed sensors are not present. The anemometer has been substituted by the wind speed estimator integrated in the MPPT, based on the growing neural gas (GNG) network. The encoder has been substituted with an intelligent IM speed estimator, the so called MCA EXIN + reduced order observer (ROO). The performance of the adopted technique has been verified experimentally on a suitably devised test set-up.

Two recent Lyapunov-based methods: free weighting matrix approach and Jensen inequality approach, have reduced the conservatism and the complexity of the stability result for one-dimensional (1D) time-delay systems, respectively. In this study, the authors further concern the analysis of delay-dependent stability and stabilisation for two-dimensional (2D) discrete systems with interval time-varying delays. By applying a new Lyapunov functional combining with the approaches of 2D Jensen inequalities and free weighting matrices, a new delay-dependent stability criterion is derived in terms of linear matrix inequalities (LMIs). Compared with the existing result, less decision variables are involved in the stability condition, so the burden of numerical computation is reduced greatly. It is also rigorously proved that the author's result is less conservative than some recent ones. On the basis of the stability criterion, state feedback is considered to realise the stability control and the state feedback gain can be solved by LMIs. Numerical examples show the effectiveness and advantage of their results.

This study discusses an intelligent digital redesign (IDR) for observer-based output-feedback controllers in Takagi–Sugeno form under immeasurable premise variables. A delta operator that asymptotically connects an analogue control system with its discrete-time model is adopted. The condition for the IDR problem with stability is proposed in terms of matrix inequality. The authors show that the proposed IDRed sampled-data controller recovers the analogue control performance from the perspective of the closed-loop trajectory under the fast sampling limit.

This study presents a new approach, which integrates the input shaping technique and the constrained adaptive backstepping control method, for constrained adaptive tracking and elastic vibration control of flexible hypersonic vehicles. Specifically, the input shaper as a feedforward part is implemented outside the feedback loop, which can achieve vibration suppression for flexible dynamics; whereas for the feedback loop, the vehicle model is decomposed into functional subsystems, and the decentralised approach is used. Then, by introducing the command filter and the auxiliary system, constrained adaptive control law is formulated to make the closed-loop system follow the shaped reference trajectory and attenuate the elastic vibration in the presence of parameter uncertainties, external disturbances and input saturation constraints. An attractive feature of this proposed control algorithm is that all signals of closed-loop system are uniformly ultimately bounded and the tracking error converges to a residual set which can be made sufficiently small by properly designing control parameters. Moreover, by applying the shaped commands, flexible dynamics with less elastic deformation and vibration can be achieved without destroying the closed-loop performance. Simulation results are provided to verify the effectiveness of the proposed control method.

A robust control approach with the inverse backlash compensation is presented for a class of non-linear systems preceded by unknown asymmetric backlash non-linearity. Firstly, the analytical expressions of the inverse compensation error for an asymmetric backlash are obtained by introducing new indicator functions, which make it possible to design a corresponding controller for the asymmetric input backlash. With the developed compensation error expression, conventional robust control approaches can be utilised to deal with such a non-smooth non-linear system. As an illustration, a robust adaptive control strategy is applied to demonstrate the approach. The developed control laws ensure the robust inverse compensation and achieve tracking within a desired accuracy. Finally, simulations performed on an unstable and uncertain non-linear system illustrate and clarify the effectiveness of the developed approach.

The problem of robust stochastic stability and delayed-state-feedback stabilisation of uncertain Markovian jump linear systems with random Markov delays is investigated. Based on the Lyapunov stability theory and robust analysis techniques, some robust stochastic stability criteria are derived in terms of linear matrix inequalities. Robust delayed-state-feedback controllers that stochastically stabilise the uncertain Markovian jump linear systems is also proposed. The state variable on the controller is assumed to be dependent on the Markov delay that has uncertain transition probabilities. Finally, numerical examples are provided to illustrate the feasibility and effectiveness of the proposed methods.

This study is devoted to the problem of state estimate of discrete-time stochastic systems with Markov jump parameters. A robust algorithm – diagonal interacting multiple model algorithm based on H _∞ filtering is presented for manoeuvering target tracking when noise of measurement is of unknown statistics. Extensive Monte Carlo simulations show the effectiveness and superiority of the proposed algorithm.

In this study, a novel enhanced anti-swing control method is proposed for underactuated overhead crane systems, which shows superior anti-swing control performance than most existing control methods. In particular, to increase the anti-swing control performance of overhead crane systems, an improved damping anti-swing signal is investigated based on a swing-related storage function. Subsequently, based on the anti-swing signal, a constructive Lyapunov function candidate is introduced and a new non-linear anti-swing control method is proposed straightforwardly, and the equilibrium point of the overall closed-loop system is proven to be asymptotically stable by Lyapunov techniques and LaSalle's invariance theorem. Some experimental results are provided to demonstrate the feasibility and effectiveness of the proposed control method. In addition, to illustrate the superior control performance of the proposed method, a comparison study between the designed control method and the existing control methods is provided as well.

This study deals with the problem of robust stabilisation for a class of state-constrained high-order uncertain switched non-linear systems in lower triangular form. Bounded state-feedback controllers are designed to assure that asymptotic stabilisation is achieved in a domain without violation of the state constraint, and all closed-loop signals are bounded, when an appropriate requirement on the initial condition is imposed. Simulation results are provided to show the efficiency of the developed method.

This study investigates the asymptotic bounded consensus tracking problems of double-integrator multi-agent systems (MASs) with an asymptotically-unbounded-acceleration and bounded-jerk target via sampled-data control. An improved sampled-data consensus tracking protocol is proposed to guarantee that double-integrator MASs track an asymptotically-unbounded-acceleration and bounded-jerk target available to only a portion of agents. The augmented matrix method and the eigenvalue analysis method are employed for deriving the necessary and sufficient conditions given in the form of the allowable scope of the sampling period. The existence of the non-empty allowable scope of the sampling period theoretically shows the effectiveness of the proposed protocol and comparison simulations numerically illustrate the effectiveness of the proposed protocol.