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In this article, a distributed adaptive event-triggered formation control strategy is proposed for unified nonlinear heterogeneous multiagent systems under uncertainties and disturbances to achieve time-varying formations. To reduce the frequency of data transmission, a distributed dual adaptive observer with an event-triggered strategy is developed to estimate the states of a reference exosystem. Without incurring prior global information about a communication graph, a novel robust formation controller, with dynamic distributed compensators for uncertainties and disturbances, is designed based on an observer result and a nonlinear internal control principle. Finally, both simulation and experiment are conducted for tracking and patrolling formation to verify the effectiveness of the proposed formation control strategy and its robustness.

In this paper, we investigate the problems of robust exponential stabilization in mean square and ${\mathcal{H}_{\infty}}$ -based integral sliding mode controller design for uncertain stochastic Takagi-Sugeno (T-S) fuzzy switched time delay systems with both matched and unmatched uncertainties under synchronous switching and asynchronous switching, respectively. In the systems, the control input is dependent on switching signal. Under synchronous switching and asynchronous switching, respectively, sufficient conditions are developed to guarantee that the resulting closed-loop systems are robustly exponentially stable in mean square with a prescribed ${\mathcal{H}_{\infty}}$ -performance. The integral sliding mode surfaces and the sliding mode controllers are designed for the underlying systems under synchronous switching and asynchronous switching, respectively. By two examples stemming from the mass-spring-damper model and the single-link robot arm model under stochastic perturbation, the effectiveness of the proposed new design techniques are verified.

This article studies the issue of stability for complex network control systems, which comprise multisystems and multicontrollers. These systems and controllers are coupled with each other and linked by communication networks with random delays. The system and controller network topologies are subject to jump, and the dynamic is captured by two models, as: 1) dual-Markov model and 2) hidden Markov model. Correspondingly, the stability conditions based on the Lyapunov-functional method are given for closed-loop stability. Then, the stability conditions are derived by transforming into the linear matrix inequality-based constraint minimization problem for solving controller gains. An illustration is carried out to testify the validation and potentials of the proposed new design techniques.

This article proposes, a novel obstacle avoidance algorithm for a mobile robot based on finite memory filtering (FMF) in unknown dynamic environments. To overcome the limitations of the existing dynamic window approach (DWA), we propose a new version of the DWA, called the finite distribution estimation-based dynamic window approach (FDEDWA), which is an algorithm that avoids dynamic obstacles through estimating the overall distribution of obstacles. FDEDWA estimates the distribution of obstacles through the FMF, and predicts the future distribution of obstacles. The FMF is derived to minimize the effect of the measurement noise through the Frobenius norm, and covariance matrix adaptation evolution strategy. The estimated information is used to derive the control input for the robust mobile robot navigation effectively. FDEDWA allows for the fast perception of the dynamic environment, and superior estimation performance, and the mobile robot can be controlled by a more optimal path while maintaining real-time performance. To demonstrate the performance of the proposed algorithm, simulations, and experiments were carried out under dynamic environments by comparing the latest dynamic window for dynamic obstacle, and the existing DWA.

In this paper, the problem of formation control is studied for second-order multi-agent systems with practical issues like mismatched uncertainties and obstacle avoidance. A reference correction algorithm based sliding mode control scheme is proposed to ensure the boundedness of each agent’s position tracking error. Both disturbance observer and artificial potential field are implemented to first tackle the problem of obstacle avoidance with the existence of mismatched uncertainties. The unreachable reference scenarios are then defined to describe the passive correcting behaviours of the multi-agent system when agents are trying to avoid obstacles. A distributed reference correction algorithm is developed for each agent to attenuate the harmful effects of passive correcting behaviours. The effectiveness of obstacle avoidance and boundedness of position tracking error are both verified by Lyapunov stability theory. Finally, multi-robot system based numerical simulations and comparisons are conducted to demonstrate the effectiveness of the proposed algorithm and controller.

In this paper, we discuss the modeling and stabilization problems for a class of discrete-time stochastic networked systems (DSNSs) subject to successive packet losses. The system under consideration is transformed into a stochastic one with bounded stochastic delay. Considering that the stochastic delay is characterized by nonuniform distribution, a new equivalent model is then constructed that enables the DSNS’s controller design to benefit from knowing the probability characteristic of packet losses. Specially, to verify this feature, the probabilities of the delay can be explicitly obtained by utilizing the formula of total probability, which is critical to model transformation and analyze practical problems that exist in DSNSs. Based on the proposed model, sufficient conditions are established under which the globally mean-square asymptotic stability of resulting stochastic delay closed-loop system is guaranteed, and a delay-distribution-dependent design procedure is then proposed. A numerical example with simulation is provided to validate the analytical results and demonstrate the effectiveness of the design procedure.

This article investigates the collision-free cooperative formation control problem for second-order multiagent systems with unknown velocity, dynamics uncertainties, and limited reference information. An observer-based sliding mode control law is proposed to ensure both the convergence of the system's tracking error and the boundedness of the relative distance between each pair of agents. First, two new finite-time neural-based observer designs are introduced to estimate both the agent velocity and the system uncertainty. The sliding mode differentiator is then employed for every agent to approximate the unknown derivatives of the formation reference to further construct the limited-information-based sliding mode controller. To ensure that the system is collision-free, artificial potential fields are introduced along with a time-varying topology. An example of a multiple omnidirectional robot system is used to conduct numerical simulations, and necessary comparisons are made to justify the effectiveness of the proposed limited-information-based control scheme.

This paper addresses the problems of the input-to-state stochastic stability for neutral switched stochastic delay systems. Two switching signals in the input controller, including the external inp...

This paper focuses on exponential synchronization of complex networks controlled by aperiodically intermittent noise with time delays. An aperiodically intermittent controller is designed to synchronize complex networks and the interval of intermittent rate is derived. Delay observations on the state of noise are made and the upper bound of time delays is obtained. A sufficient condition for exponential synchronization of the underlying systems is derived. Moreover, the auxiliary networks without time delays are introduced and the sufficient criterion for stochastic synchronization of the networks is also obtained. The comparison principle plays a significant role in synchronizing complex networks in this paper. An example is given to demonstrate the feasibility and effectiveness of the new approaches.

This paper considers the problems on the mean square exponential synchronization and the almost surely exponential synchronization for neutral stochastic dynamical delay networks with Markovian switching via the single pinning impulsive control. By using a unified approach incorporating the concept of average impulsive interval and Lyapunov function, and the Borel–Cantelli lemma, two criteria on the mean square exponential stability and the almost surely exponential stability for impulsive neutral stochastic delay systems with Markovian switching are derived. It is shown that the impulsive control with a suitable impulsive strength can stochastically stabilize the underlying systems. With the symmetry and irreducibility of the switching topologies, and the obtained stochastic stability criteria, sufficient conditions are derived to determine the mean square exponential synchronization of the underlaying networks via the single pinning impulsive controller, which are given with algebraic inequalities and ${\mathcal {M}}$ -matrix. The almost surely exponential synchronization is also analyzed. Two examples are given to verify the validity of the theoretic results presented.

This study is concerned with the design of an event-triggered fault estimation and fault tolerant model predictive control scheme for linear systems subject to disturbances and actuator faults by using the method of delta operator. First, an observer is established to estimate the state and the fault simultaneously. An H 2 / H ∞ model predictive controller with fault compensation signals is then proposed through solving a constrained optimisation problem. An event-triggered scheme is employed to conserve the network bandwidth. The new design techniques are shown to be effective via simulation results.

This paper presents a new fuzzy functional observer based fault detection technique for fuzzy systems with time-delay subject to exogenous disturbance. The estimation error of the fuzzy functional observer is used to obtain a residual to detect the fault. The proposed fault detection scheme does not require any threshold to compare with the residual. The exogenous disturbance is decoupled from the error dynamics using the concept of unknown input observers to ensure the robustness of the residual generator. The time-delay is considered to be time-varying and bounded. Sufficient conditions are presented to ensure the asymptotic stability of the observer by linear matrix inequality approach. The proposed design technique is illustrated and verified using examples to demonstrate its effectiveness for fault diagnosis.

This paper considers the problem of achieving time-varying formation for second-order multi-agent systems with actuator hysteresis, unknown system dynamics and external disturbances. A novel adapti...

This paper studies the memory output-feedback integral sliding mode control for Furuta pendulum systems subject to unmatched harmonic disturbances and measurement noise. By introducing an artificial time delay, the derivatives of angular positions are approximated via Taylor’s expansion on the integral remainder. On this basis, a novel delay-dependent output-based sliding surface is constructed for state estimation and performance analysis, which inherits the appealing properties of the state-feedback integral sliding surface such as the elimination of reaching phase. Wherein, the artificial delay is regarded as a flexible design parameter ranging between 0 and its upper bound, with the bound determined by solving linear matrix inequalities. As a result, an integral sliding mode controller (ISMC) consisting of a static output-feedback part and a switching part is designed based on the descriptor system representation of the original system to control the pendulum in a noisy environment. Comparative simulation studies with $H_\infty $ control, state-feedback ISMC, and high-order ISMC demonstrate the effectiveness of the design method.

In this paper, the issue of stochastic stabilization of nonlinear systems via intermittent Brownian motion under random disturbance is investigated. Two types of noise are considered. One is Brownian noise that is regarded as an intermittent control input. The other is the random disturbance caused by the unknown external environment, which can lead to time fluctuations of the intermittent Brownian noise stabilization. We first present an intermittent stochastic system with random disturbance. We then propose an almost sure stability condition for the underlying system. The stability results are then used to stabilize the memristive Lorenz system. Numerical simulations are also given to validate the effectiveness of the theoretic results obtained.

This paper presents an active fault tolerant control scheme for discrete-time semi-Markov jump systems. The semi-Markov kernel approach and the finite sojourn time restriction are employed to obtain finite stability sufficient conditions for stability and stabilization. Semi-Markov processes are applied to model the faults occurrence of the actuators and the fault detection process. To guarantee the stability of the resulting closed loop system, an elapsed-time-dependent controller design method is applied. Finally, a numerical example and a vertical take-off and landing vehicle (VTOL) example are shown to illustrate the effectiveness and potential of the proposed design technique.

An optimal strategy is derived for robust performance of feedforward and feedback noise controllers in active noise control systems with a bounded narrowband disturbance. The designed recursive algorithm updates the weighting factor to make sure that controller updates are performed when the current measurement data contain new information to improve the estimation quality. This significantly reduces the computational complexity by allowing fewer updates in the steady state and persistent updates otherwise. The presented algorithm can guarantee non-increasing and positive-definite covariance matrices for feedforward and feedback filters, and this results in a bounded and non-increasing estimation error. Moreover, the proposed algorithm achieves fast convergence with improved performance in steady-state noise reduction. In simulations, the comparison of the proposed method with the established methods under benchmark conditions demonstrates the improvement in the overall performance.

This paper studies the control synthesis for uncertain semi-Markov jump systems in a discrete-time domain subjected to external disturbance. The switching between modes is determined by a function ...

This paper focuses on the problem of adaptive output-constrained neural tracking control for uncertain nonstrict-feedback systems in the presence of unknown symmetric output dead zone and input saturation. A Nussbaum-type function-based dead-zone model is introduced such that the dynamic surface control approach can be used for controller design. The variable separation technique is employed to decompose the unknown function of entire states in each subsystem into a series of smooth functions. Radial basis function neural networks are utilized to approximate the unknown black-box functions derived from Young’s inequality. With the help of auxiliary first-order filters, the dimensions of neural network input are reduced in each recursive design. A main advantage of the proposed method is that for an $n$ -order nonlinear system, only one adaptation parameter needs to be tuned online. It is rigorously shown that the proposed output-constrained controller guarantees that all the closed-loop signals are semiglobal uniformly ultimately bounded and the tracking error never violates the output constraint.

Addressing the leader-follower consensus control problem with time-varying output constraints for a class of second-order nonlinear multi-robot systems, we apply a unified barrier Lyapunov function to transform the constrained output state into the unconstrained one, while removing the feasibility condition existing in the traditional barrier Lyapunov function. Using the radial basis function-based neural network to approximate the unknown nonlinear function, we derive an adaptive neural network consensus controller to ensure that each robot follows the predefined trajectory of a leader. We then verify the effectiveness of the consensus control via simulation studies on a team of robotic manipulators.

For practical applications of multi-agent systems, agents could be continuously subject to external disturbances, which is likely to negatively affect their tracking performances. Furthermore, the unknown or inaccurate factors in system dynamics can also bring negative effects to a system’s performance. In this paper, an uncertainty-observer-based dynamic sliding mode control scheme is proposed to deal with time-varying formation control problems of second-order nonlinear multi-agent systems. An uncertainty observer is first implemented for each agent to estimate the combination of the agent’s external and internal uncertainties and its time derivative. An observer-based dynamic sliding mode formation control law is developed for a cluster of second-order nonlinear agents to achieve time-varying formation. Finally, a numerical simulation is provided to illustrate the validity of the proposed control approach.

This paper presents a new technique for stabilising a Takagi–Sugeno (T-S) fuzzy system with time-delay and uncertainty. A robust fuzzy functional observer is employed to design a controller when the system states are not measurable. The model uncertainty is norm bounded, and the time-delay is time-varying but bounded. The parallel distributed compensation method is applied for defining the fuzzy functional observer to design this controller. The proposed procedure reduces the observer order to the dimension of the control input. Improved stability conditions are provided for the observer compared with the existing results of functional observer-based stabilisation of T-S fuzzy models. Lyapunov–Krasovskii functionals are used to construct delay-dependent stability conditions as linear matrix inequalities. The solution of these inequalities is used for calculating the observer parameters. The sensitivity of the estimation error to the model uncertainty is reduced by minimising the $$L_2$$ gain. The new design method developed is illustrated and verified using two examples.

In this study, an optimally distributed formation control approach is proposed for mixed-order multi-agent systems to achieve formation consistency with obstacle avoidance. The systems consist of unmanned aerial vehicles and ground robots. The authors present a formation consistency control for a mixed-order system under switching topologies. An optimally distributed method is developed by matrix transformation method and linear quadratic regulator theory, which leads to an optimisation on both horizontal and vertical control. For obstacle avoidance, a non-quadratic cost function is introduced based on optimal control. The resulting closed-loop mixed-order system is validated to be stable with an optimal performance. The formation consensus and obstacle avoidance are verified by simulations to show the effectiveness and potentials.

This paper studies the problem of mean square asymptotical synchronization and $$H_\infty $$ synchronization for coupled stochastic reaction–diffusion systems (SRDSs) via boundary control. Based on the deduced synchronization error dynamic, we design boundary controllers to achieve mean square asymptotical synchronization. By virtue of Lyapunov functional method and Wirtinger’s inequality, sufficient conditions are obtained for ensuring mean square asymptotical synchronization. When coupled SRDSs are subject to external disturbance, mean square $$H_\infty $$ synchronization is investigated and corresponding criterion is presented under a designed boundary controller. In addition to focusing on systems with Neumann boundary conditions, we also briefly study coupled SRDSs with mixed boundary conditions and sufficient conditions are provided to achieve the desired performance. Numerical examples are used to verify the effectiveness of our theoretical results.

This paper proposes a novel interval observer design method for discrete-time linear systems with unknown but bounded disturbance and measurement noise. The proposed interval observer has a new structure that provides more design degrees of freedom. A direct method based on H ∞ technique is used to improve the accuracy of interval estimation. The design conditions are formulated into linear matrix inequalities, which can be efficiently solved. Two numerical examples are given to illustrate the design and validate the performance of the interval observers.

This paper investigates the construction of a fuzzy functional observer for nonlinear systems with time-delays, and the application of the observer to estimate the state functions of the parallel distributed compensation controller for stabilizing the system. Two types of time-delays are considered: constant and time-varying delays with bounded time derivative. Stability conditions are obtained using Lyapunov–Krasovskii functional approach; and the conditions are transformed into linear matrix inequalities with equality constraints so that observer parameters can be calculated using the solution of these inequalities. Functional observer construction procedures are presented considering both constant and time-varying time-delays. Two examples, including one for obtaining a power system stabilizer for a single machine infinite bus system, are presented to illustrate effectiveness of the proposed design procedures.

This paper addresses the control problem for a class of discrete-time Markov jump linear systems with partially unknown transition probabilities using model predictive controller subject to external disturbances and input constraints. Our focus is on the design of a model predictive controller to stabilize the system with a given mixed H2/H∞ performance index. Sufficient conditions are derived in terms of a set of linear matrix inequalities. Examples are presented to demonstrate the effectiveness of the proposed controller design method.

This paper presents a novel method of synthesising controllers for nonlinear systems represented by Takagi--Sugeno fuzzy model using functional observer. The proposed method uses minimum order observer for estimating the control input of each rule as a function of states. Existence conditions of the functional observer-based controller are provided in the form of rank equality conditions. The stability conditions of the observer are obtained using quadratic Lyapunov function, and the conditions are presented in the form of linear matrix inequalities. Observer parameters can be obtained by solving these inequalities. Finally, systematic synthesis procedures for constructing the observer are presented. Examples with simulation output are given to demonstrate this method and to compare its performance with a conventional full-state observer-based controller.

This paper investigates the problem of the fault detection filter design for nonhomogeneous Markovian jump systems by a Takagi–Sugeno fuzzy approach. Attention is focused on the construction of a fault detection filter to ensure the estimation error dynamic stochastically stable, and the prescribed performance requirement can be satisfied. The designed fuzzy model-based fault detection filter can guarantee the sensitivity of the residual signal to faults and the robustness of the external disturbances. By using the cone complementarity linearization algorithm, the existence conditions for the design of fault detection filters are provided. Meanwhile, the error between the residual signal and the fault signal is made as small as possible. Finally, a practical application is given to illustrate the effectiveness of the proposed technique.

This paper considers H − ∕ H ∞ fault detection observer design in the finite frequency domain for a class of linear parameter-varying (LPV) descriptor systems. We propose a novel fault detection observer with a non-singular structure. To make the residual sensitive to faults and robust against disturbances, we develop a finite frequency H − ∕ H ∞ design method based on a generalized Kalman–Yakubovich–Popov lemma for LPV descriptor systems. Design conditions for the proposed fault detection observer are derived and converted as linear matrix inequalities. Simulation studies are conducted to demonstrate the performance of the proposed method.

This paper considers robust fault estimation in the finite frequency domain for linear continuous-time descriptor systems. Based on state augmentation, we propose a fault estimation observer with a...

This paper presents a sliding mode control scheme for tracking control of nonlinear singularly perturbed systems in the presence of model errors and external disturbances. A dual-loop feedback control is developed to provide accurate tracking capability and sufficient robustness to system uncertainties. A sliding mode controller is proposed in the outer-loop feedback design such that the plant states are stabilised for given reference trajectories, while an additional robust controller is designed in the inner loop to increase the adaptability to uncertainties, and reduce the effect of unmodelled high-frequency dynamics on plant dynamics. An appealing feature of the control scheme is the attenuation of chattering. The effectiveness and merits of the new control scheme developed are shown via a verification example of velocity control of a quad-rotor.

This paper proposes a new adaptive controller for a class of uncertain nonlinear systems with a quantized signal. Fuzzy logic systems are utilized to approximate nonlinear terms without imposing prior matching conditions required. A hysteretic type of quantizer is incorporated to reduce chattering. A new adaptive backstepping controller is designed to guarantee that the underlying uncertain nonlinear system is semiglobally uniformly ultimately bounded. Two numerical examples are presented to demonstrate the effectiveness and potential of the proposed techniques.

A new model reduction (MR) method is developed for continuous-time switched linear parameter-varying (LPV) systems based on time-weighted gramians. Using time-weighted energy functionals and parameter-independent multiple Lyapunov functions, the time-weighted controllability and observability gramians are obtained. With a balanced transformation matrix optimally derived, the authors show that a balanced realisation can be achieved for the original switched LPV systems. A series of low-order models for different weighting factors are yielded by state truncation or singular perturbation techniques. The reduced-order models can preserve the stability. The feasibility and effectiveness of the proposed MR method are illustrated by two examples including a three spring-mass system.

This paper proposes an approach for constructing and generating chaos from a class of three-dimensional linear switching systems via a heteroclinic loop based on the Shilnikov criterion. First, the existence of a switching rule for the system is derived by utilizing the Shilnikov heteroclinic criterion. Then a general design philosophy and its procedure of switching rule are provided to ensure that the proposed approach is applicable to engineering. Two numerical examples are presented to validate the main principle and the implementability of the scheme. Theoretical analysis and numerical simulation are used to demonstrate the feasibility and effectiveness of developed techniques.

This paper establishes input-to-state stability (ISS) and robust ISS of neural networks with Markovian switching (NNwMS). The M matrix algebraic condition for stochastic NNwMS is given; the result is then extended to stochastic time varying delays NNwMS. From the ISS condition of stochastic delayed NNwMS, we get robust ISS of NNwMS in two cases: delay perturbation in diffusion and delay perturbation in drift, respectively. These ISS criteria are readily to be checked only from the parameters of the NNwMS and also ensure exponential stability without input term. The results presented here include neural networks without Markovian switching as special cases. Two numerical examples are given to show the effectiveness of theoretical criteria.

We propose an H-/L∞ fault detection observer for linear parameter varying systems with unknown disturbance and actuator fault. The fault detection observer is designed to enhance the fault sensitivity of residual and attenuate the effect of disturbance on residual such that timely and robust fault detection is achieved. The observer design method is carried out in the finite frequency domain and the design conditions are expressed as a set of linear matrix inequalities. By using L∞ performance index to measure the effect of disturbance on residual, the fault detection observer exhibits a peak-to-peak disturbance attenuation performance, which can provide a dynamic threshold for residual evaluation in fault detection. Numerical simulations are conducted to verify the effectiveness of the proposed method.

The problem of observer design and stability analysis for T–S fuzzy system with multiplicative noise is investigated in this paper. Based on the fuzzy Lyapunov function, a membership function based global stable non-parallel distributed compensation observer is developed for the T–S fuzzy system. Then we give an improved membership function dependent stability analysis approach, which involves the calculated values of the membership function and its derivatives. The conservativeness of stability conditions for the observer is much relaxed than the existing methods, and these conditions can be formulated as LMIs (linear matrix inequalities), which can be effectively solved by convex optimization techniques. An example is provided to illustrate the effectiveness of proposed method.

This paper considers the sampled-data fuzzy control of nonlinear systems in strict feedback form with disturbances and random missing input data. We propose a novel method in which a state observer and a disturbance observer are combined to construct a sampled-data fuzzy output feedback controller. The stochastic variables with a Bernoulli distributed sequence are used to model missing input data. Fuzzy logic systems are applied to approximate nonlinearities without requiring prior knowledge. The relation between observer gain and sampling period is established. The output feedback controller designed guarantees that the nonlinear system is globally stable. A simulation example of four degrees of freedom robotic arm is used to demonstrate the effectiveness and applicability of the proposed control scheme.

Summary This paper is concerned with the reliable H∞ static output control of linear time-varying delay systems with sensor faults. Time-varying delay is tackled by the input–output transformation and the resulting closed-loop system lies in the framework of scaled small gain. Some techniques are developed to separate the coupling among the Lyapunov matrix, input matrix, control gain matrix, and output matrix. Based on a relaxed Lyapunov–Krasovskii functional, sufficient conditions for the desired static output controller design with the required H∞ performance level are proposed by means of linear matrix inequalities. The effectiveness of the proposed method is validated by two examples. Copyright © 2016 John Wiley & Sons, Ltd.

In addressing the adaptive neural backstepping control for multiple-input and multiple-output nonlinear systems in pure-feedback form with time-delay and input quantisation, we construct a high-gain state observer and an output-feedback adaptive control scheme using backstepping method, with neural networks to estimate the uncertain nonlinear functions. Then, we propose an output feedback neural controller that ensures all the state trajectories in the time-delay quantised nonlinear systems are ultimately bounded, with the control signal being quantised by either a hysteretic quantiser or a logarithmic quantiser. An illustrative example is presented to show the applicability of the new control method developed.

In this paper, a controller is designed for a group of cooperative robots to achieve the stable displacements. Communication network among the robots is assumed unavailable and each robot can only measure the displacements relative to their neighbours. The controller will enable the robots to move to a predefined formation or a special formation under a suitable protocol which we will design. We will discuss the effect of different sizes of the multi-vehicle system and conduct simulations to verify the effectiveness of the proposed controller and the formation protocols.

Self-adapting variable step size (SAVSS) normalized least mean square (NLMS) algorithms are derived and analysed for the active noise control systems with acoustic feedback path. The objective of the proposed SAVSS scheme is to resolve the conflicting requirements of rapid convergence and low mis-adjustment. A tuningless power scheduling scheme is proposed that varies the gain according to the output of the predictor filter, and switches to a low gain on the basis of a minimum criteria. In the steady state, a low gain auxiliary noise significantly improves noise reduction performance, and the update of the feedback compensation filter and predictor filter is discontinued to reduce computations. Simulations are performed with narrowband noises to validate the improved performance of the proposed method in comparison with established state-of-the-art methods.

Complex nonlinearity and strong coupling in the pitch-yaw-roll of a rotorcraft make it difficult for the attitude control of unmanned aerial vehicles, especially in the presence of wind disturbances and plant uncertainties. To confront these problems, this paper presents a control scheme of two sequential steps, structural parameter determination and robust controller design, in order to achieve the desired attitude regulation performance and disturbance attenuation capability. For the structural parameter design, key structural parameters are specified such that the open-loop transfer functions, from torque inputs to angular velocities, exhibit positive-real properties in particular frequency ranges to incorporate robustness into the controller design. Then, a dual-loop structured controller is designed using dynamic inversion approaches and singular perturbation techniques. The controlled inner loop provides adequate decoupling such that the outer loop may control each Euler angle independently, and proportional-derivative control is used in each outer loop to achieve a desired attitude tracking performance. The effectiveness of the control scheme is verified via an attitude tracking example.

Summary This paper considers a class of stochastic systems referred to as stochastic switched systems of neutral type with time-varying delay, which combines switched systems with neutral stochastic systems. The systems consist of subsystems of two forms: (i) only stable subsystems and (ii) both stable subsystems and unstable subsystems. By establishing an integral inequality, the exponential stability in pth(p≥1)-moment for such systems with only stable subsystems is first considered. Then, by using an average dwell time approach, the exponential stability in pth(p≥1)-moment for the second form is addressed. An important finding of this study is that when the average dwell time is chosen to be sufficiently large and the total activation time of unstable subsystems is relatively small compared with that of stable subsystems, the exponential stability in pth(p≥1)-moment for such systems can be guaranteed. Two major advantages of these new results are that the differentiability or continuity of the delay function is not required compared with the existing results in the literature, and the proposed approaches can be used to consider the case when the neutral item and the stochastic perturbation are simultaneously presented. An example is provided to verify the effectiveness and potential of the theoretic results obtained. Copyright © 2016 John Wiley & Sons, Ltd.