In this study, an intelligent digital redesign (IDR) technique is proposed for an observer-based sampled-data fuzzy controller of non-linear systems. By using a Takagi–Sugeno fuzzy model, the pre-designed analog and sampled-data fuzzy controllers are supposed, and these discretised closed-loop systems are obtained, respectively. Based on the IDR problem, the authors guarantee both stability and state-matching conditions. Unlike the previous techniques, the proposed IDR not only improves the state-matching performance using the state-matching error cost function, but is also derived in the strict linear matrix inequality format. In a numerical example, the effectiveness of the proposed technique and the results of the improved performance are shown.

The authors address control system design based on an interval observer for non-linear switched systems with non-linear vector functions that are assumed to satisfy Lipschitz conditions. First, the observer gain satisfying a Metzler matrix can be solved by optimisations of linear matrix inequalities (LMIs). Second, an interval observer is designed to estimate the states of non-linear switched systems with the average dwell time scheme, and sufficient conditions for state estimation are presented in terms of an LMI formulation. Third, based on an interval observer, state feedback matrices are designed to construct an asymptotically stabilising switching controller. Finally, a numerical example is provided to demonstrate the efficiency of the approach.

This study deals with the reliable guaranteed-cost control problem for a class of delta operator switched linear systems, in terms of mode-dependent average dwell-time (MDADT) approach. Firstly, based on MDADT approach, asymptotical and exponential stabilities of delta operator non-linear switched systems are analysed. Then, the reliable controller and the minimum MDADT are designed to stabilise the considered delta operator linear switched system with actuator faults and to determine an upper bound of the given cost function. A flight control switched system is given to show the validity and potential of the proposed method.

This study is concerned with the problem of output feedback fuzzy H_∞-tracking control design for nonlinear time-delay systems. Initially, a discrete-time Takagi–Sugeno (T–S) modelling approach is applied. Then a general and extended structure for the controller is assumed. Finally, a linear matrix inequality (LMI) approach is offered to find the possible solutions. The final design will result in the closed-loop stability of the system together with minimising the H_∞-tracking control norm to some reference and bounded signals. Despite the existing results which offer a set of coupled LMIs and a double step algorithm to find a possible solution, the authors' results are a single step and less conservative. Moreover, the authors let the time delay to exist both in state and input matrices of the T–S fuzzy model. Two simulation examples are given to illustrate the effectiveness of the obtained method in comparison with the existing ones.

In this study, the problem of adaptive dynamic surface control (DSC) design is investigated for a class of stochastic non-linear systems with input saturation. The saturation non-linearity is modelled via the existing dead zone-based model of saturation. Furthermore, the packaged unknown functions are tackled using the neural networks. On the basis of the DSC technique, an adaptive controller is designed for stochastic non-linear systems with input saturation. The proposed control scheme can guarantee the closed-loop system stability, which shows that all closed-loop system signals are semi-globally uniformly ultimately bounded in the sense of probability and the tracking error converges to a small neighbourhood of the origin. Simulation results are provided to demonstrate the effectiveness and performance of the proposed approach.

This study presents an adaptive decentralised dynamic surface control (DSC) approach for a class of large-scale non-linear systems with unknown non-linear functions, unknown control gains, time-varying delays and in the presence of unknown actuator failures. The considered actuator failures are modelled to cover both loss of effectiveness and stuck at some time-varying values where the values, patterns and time occurrences of the failures are unknown. With the help of neural networks to approximate the unknown non-linear functions, a novel adaptive actuator failure compensation controller is developed based on the backstepping method and the DSC approach. The appropriate Lyapunov–Krasovskii functionals are introduced to design new adaptive laws to compensate the unknown actuator failures as well as uncertainties from unknown non-linearities and time delays. The proposed design method does not require a priori knowledge of the bounds of the unknown time delays and actuator failures. The boundedness of all the closed-loop signals is guaranteed and the tracking errors are proved to converge to a small neighbourhood of the origin. The proposed approach is employed for a double inverted pendulums benchmark and a chemical reactor system. The simulation results show the effectiveness of the proposed method.

An adaptive output-feedback control approach is proposed for uncertain non-linear systems with unknown time delays in strict-feedback form. A linear observer is designed to estimate unmeasurable state variables. In contrast to the existing controllers for strict-feedback systems, the adaptive control design using only single fuzzy approximation (SFA) is presented. In the controller design procedure, unknown non-linear functions including unknown non-linearities caused by compensating time-delay functions are derived to design virtual controllers. They are not approximated by using fuzzy logic systems at each step of the backstepping design, but just passed down to the next step. Finally, a lumped unknown non-linear function including non-linearities of observer error dynamics is defined at the last step and estimated by SFA in order to design the actual control law. Thus, the actual control law using SFA can be implemented, without calculating virtual control laws. Compared with existing output-feedback control approaches for strict-feedback time-delay systems, the proposed control system has a relatively simple structure in that it overcomes the problem of using multiple approximators as well as the problem of calculating the repeated differentiations of virtual controllers. In addition, we show that all signals in the closed-loop system are uniformly ultimately bounded and the tracking error converges to an adjustable neighbourhood of the origin.

This study describes a robust load-frequency control (LFC) scheme in a two-area interconnected power system with uncertain disturbances. A sector-bounded H ^∞ control approach is used for compensating of the non-linearities based on a minimum order dynamic LFC model to avoid the complexity of a high-order model. The simulation in time domain from the use of the proposed controller has been compared with the use of a conventional proportional–integral (PI) type controller affected by the same load change. The simulated results have proved the significant improvement over the PI controller and have verified the effectiveness of the proposed robust H ^∞ controller which can minimise the frequency deviations significantly. This approach treats the tie-line power exchanges in the presences of external disturbance, parameter uncertainties and accidental crashing of generating units as the structural or model uncertainties discussed in this study. Simulation results also show that the proposed H ^∞ controller guarantees robust performance and fast response under a wide range of operating conditions and system uncertainties.

Some of the critical issues in networked control systems are optimisation of control, communication, and computational costs, time-delays, and coupling of control and triggering-condition. To address the optimisation of communication and control, an approach has been recently reported by Gommans et al. (2014), based on self-triggered (ST) linear quadratic (LQ) control. However, the optimisation of computational cost, coupling, and time-delays were not considered. With this motivation, a novel ST LQ methodology is proposed in this study. The features, merits, and demerits of this approach are compared against Gommans et al. (2014) and simulation results are presented for three case studies.

This study presents a control design methodology to address simultaneous robust feedforward and feedback tuning. A tuning guideline including tuning rules based on the time-domain response is introduced. Then, an optimisation algorithm is used in the frequency domain to find the optimal feedback–feedforward controller. Detailed simulations covering a wide range of industrial process models are carried out to demonstrate the validity of the proposed methodology and its advantages over other approaches.

Output regulation is a general framework, for it not only can achieve closed-loop stability, but also can realise asymptotic tracking and disturbance rejection. Within this framework, the authors consider the containment problem of heterogeneous linear multi-agent systems with directed graphs. Via dynamic compensator techniques, the containment problem can be converted into cooperative output regulation problem. Moreover, they artfully construct regulation equations, whose solutions are also given. Adaptive protocols are proposed here by assigning a time-varying coupling weight to each node. Unlike most existing protocols that depend on certain global information, protocols presented in this paper are independent of any global information, and hence are fully distributed. By combining the compensator technique with adaptive control, they propose the distributed dynamic adaptive state feedback and adaptive output feedback protocols for driving followers into the moving convex hull spanned by leaders. The obtained results are applied to the containment control of a network of heterogeneous agents, where the followers are described by mass-damper spring systems, and the leaders are specified by harmonic oscillators.

Kalman consensus filter (KCF) has been developed for distributed state estimation over sensor networks where local estimates are exchanged with time-triggered transmission mechanism. To reduce the amount of data transfer in sensor networks, the authors propose a KCF with an event-triggered communication protocol. The triggering decision is based on the send-on-delta data transmission mechanism: each sensor transmits its local estimates to its neighbours only if the difference between the most recent transmitted estimate and the current estimate exceeds a tolerable threshold. On the basis of the event-triggered communication protocol, an optimal Kalman gain matrix is derived by minimising the mean squared errors for each sensor and a suboptimal KCF is developed for scalable considerations. By using the Lyapunov-based approach, a sufficient condition is presented for ensuring the stochastic stability of the suboptimal KCF. A numerical example is provided to verify the effectiveness of the proposed filter.