In this paper, a novel event-triggered-based decay aggregation efficient model predictive control (DAEMPC) problem is investigated for nonlinear systems represented by interval type-2 (IT2) T-S fuzzy models subject to cyber attacks and actuator saturation. First, to make full use of communication resources, an adaptive event triggered (AET) strategy is applied to determine the data transmission in the sensor to controller link. A Bernoulli random process is introduced to denote the denial-of-service (DoS) attack, and the polytopic description method is utilized to characterize the actuator saturation. Second, the efficient model predictive controller concerning the decay aggregation approach is designed for the considered nonlinear networked control system (NCS). It involves offline solving feedback control law and designing ellipse feasible sets whose projections are vertical in the x-space, and online optimizing the perturbation variable instead of the whole performance objective function. Different from the previous studies, the presented AET-based DAEMPC algorithm not only compensates for the deficiencies in the communication network, but also enlarges the initial feasible set and reduces the computational burden. Finally, the validity of the presented algorithm is illustrated through the simulation of continuous stirred tank reactor (CSTR).

This paper investigates a guaranteed cost control method for a specific class of output feedback networked interval type-2 (IT2) T-S fuzzy systems, considering stealthy denial-of-service (DoS) attacks, dynamic hybrid-triggered mechanism, as well as bounded disturbances. First, to alleviate network transmission burden, a dynamic hybrid-triggered mechanism that integrates both time-triggered and event-triggered mechanisms is addressed. Second, closed-loop stability of the networked control systems (NCSs) is demonstrated through the quadratic boundedness (QB) technique. Then, by applying Lyapunov stability theory, sufficient conditions are provided for the stability of the considered NCS and the existence of an observer-based output feedback guaranteed cost controller. Finally, the feasibility of the proposed control strategy is demonstrated through two simulation examples.

In this paper, the event-triggered output feedback model predictive control (MPC) for LPV system subject to roundrobin (RR) protocol scheduling and bounded disturbance. First, the event-triggered output feedback controller with a dynamic threshold trigging condition is utilized with the target to reduce the computation burden. Second, in order to avoid the data collision of network transmission problem, the RR protocol is adopted in the controller to actuator channel. Based on these techniques, an effective robust MPC problem that involves eventtriggered strategy, data scheduling, and bounded disturbance is designed as a min-max optimization problem. The sufficient conditions of closed-loop performance of the proposed MPC algorithm are finally given and proved. The simulation example is exploited to validate the effectiveness of the proposed results.

The objective of this paper is to investigate an event-triggered output feedback model predictive control (MPC) approach for the nonlinear cyber-physical system (CPS) with a stochastic communication protocol (SCP) scheduling, which is approximated by an interval type-2 Takagi-Sugeno (IT2 T-S) fuzzy model. For the objective of enhancing network communication efficiency and relieving data collision caused by limited communication resource, an SCP protocol ruled by a Markov stochastic process is favourably utilized to govern the data scheduling of network. Based on an event-triggered output feedback control law, a mode-dependent IT2 fuzzy controller is formally designed, in which the feedback gain is optimized by solving an online constrained MPC optimization problem. By the utilization of defining the mean-square quadratic boundedness (MSQB) for confining the augmented system state into a robust invariant set, both the feasibility of controller and closed-loop stochastic stability are ensured and proved with the satisfaction of physical constraint in the mean-square sense. Finally, we validate the effectiveness of the proposed method by a numerical simulation example.

ABSTRACT This paper examines the dynamic output feedback control of interval type‐2 (IT‐2) fuzzy systems suffering from stealthy denial of service (DoS) attacks under the adaptive event‐triggered stochastic communication protocol (AETSCP). Considering the advantages of the AET mechanism and the SCP, a new AETSCP is proposed, whose trigger parameters can be dynamically adjusted based on the changing trend of the system state and the stealthy DoS attack probability. The signal‐to‐interference plus noise ratio (SINR)‐based DoS attacker continuously senses the channel and utilizes the trigger characteristics of the AET mechanism to cleverly launch the attack to ensure its stealthiness. A dynamic quantizer is employed to quantize the measured output, and a time‐varying fault matrix is adopted to represent actuator faults. By applying the mode‐dependent Lyapunov function, the sufficient condition to ensure the stability of the considered networked control system (NCS) is derived, and the dynamic output feedback controller satisfying the performance is designed. Lastly, a simulation example is used to verify the effectiveness of the proposed algorithm.

This paper proposes two model predictive control (MPC) methods for a Hammerstein model with both unmeasurable state and bounded disturbance. First, a dynamic output feedback MPC is designed such that the dynamics of the state estimation error is decoupled from that of the estimated state. Hence, the separation principle holds and a part of the controller parameters can be designed off-line. Second, a case is considered where Hammerstein nonlinearity is not exactly inverted, that is, a reserved static nonlinearity exists in the closed-loop system. This reserved nonlinearity is merged into a polytopic description in combination with the linear dynamic part. The control move is then parameterized as a feedback law followed by the approximate inverse of Hammerstein nonlinearity. The recursive feasibility and closed-loop stability of both approaches are guaranteed. Numerical examples are given in order to show effectiveness of the proposed approaches.

In this paper, the networked model predictive control (MPC) for the linear system with the arbitrary bounded delays and packet losses during the data transmission and processing is studied. Since MPC requires the prediction of the input and state within the two links (i.e. sensor to controller and controller to actuator links), the closed-loop model is constructed by considering all possible transmission statuses. The obtained controller generalises the results of Kothare et al. (1996), in specifying the system model, physical constraints and closed-loop stability, while guaranteeing the recursive feasibility of the optimisation problem. Two simulations are implemented to show the effectiveness of the presented networked MPC approach.

This paper aims to address a finite-horizon model predictive control (MPC) for non-linear drum-type boiler-turbine system using a system-identification method. Considering that the strong state coupling of a non-linear mechanism model, the subspace identification method is first utilized to obtain a linear state-space model, and transformed into an input–output model. By taking the inputs and outputs of the input–output model as system states, an augmented non-minimal state-space (NMSS) model of state measurable is constructed. In order to reduce the computation burden, the augmented NMSS model is further transformed into a canonical formulation by adopting a Kalman decomposition. Based on the minimal realization state-space model, the MPC controller is parameterized as a finite-horizon optimization problem. Finally, simulations are performed and evaluated the performance of the proposed method, and the simulation results show that: the linear model approximate the non-linear system accurately; the proposed MPC method can achieve a satisfactory stable control performance; and the computation time 18.388 s for the overall optimization problem also illustrates the real-time performance effectively.

This article investigates the robust model predictive control (MPC) problem for networked control systems represented by the linear parameter‐varying model, in which an event‐triggered strategy and the round‐robin (RR) protocol scheduling locate at the sensor‐to‐controller and controller‐to‐actuator channels, respectively. By considering the problems of system state immeasurable and communication burden in engineering application, an output feedback controller that combines the aperiodic event‐triggered strategy is applied, where the triggering condition is designed in a time‐varying fashion. In addition, in order to avoid unexpected data collisions, the RR protocol is utilized to schedule a shared network and guarantee the efficiency of the control system. The controller parameters are obtained by solving an online convex robust MPC optimization problem, and the feasibility of the optimization problem and closed‐loop stability are also addressed. The effectiveness of the proposed theoretical results is illustrated by a numerical simulation example.

This paper proposes a general robust model predictive control (MPC) approach for the constrained Takagi-Sugeno (T-S) fuzzy model with additive bounded disturbances. We adopt the homogeneous polynomially parameter-dependent (HPP) Lyapunov matrix with the arbitrary complexity degree and the corresponding HPP control law for the controller design. By applying the Pólya’s theorem and the extended nonquadratic boundedness property, a systematic approach to construct a set of sufficient conditions for assessing robust stability described by parameter-dependent linear matrix inequalities (LMIs) is established. The proposed approach is an improvement over the existing approaches in terms of control performance and stabilizable model range. Numerical examples are provided to show the effectiveness of the proposed robust MPC approach.

For the uncertain linear systems described by the linear parameter varying (LPV) model, a parameter-dependent open-loop model predictive control (MPC) is proposed. The controller applies a tree trajectory to generate the vertices of uncertainty state predictions. Based on the state prediction tree, the future free control moves are parameter-dependent, whose vertices correspond to those of state predictions. The cost function penalizes the deviations of all the vertices of state/input from their steady-state target values. It is shown that the offset-free property is achieved by this method. A simulation example is given to demonstrate effectiveness of the approach.

This article considers security control under the network environment for a system with deception attacks, polytopic uncertainty, and persistent bounded disturbance. Since the state is immeasurable and the physical constraint is considered, the output feedback robust model predictive control (MPC) is utilized. The previous results on the output feedback robust MPC are extended to address the deception attacks. The mean-square quadratic boundedness is defined in order to characterize the closed-loop stability. An optimization problem is proposed, which can be solved by linear matrix inequality techniques. By a simple refreshment of the state estimation error set, the optimization problem is shown to be recursively feasible, and the augmented state converges to the neighborhood of equilibrium point. The effectiveness of the proposed theoretical approach is demonstrated by a numerical simulation example.

This paper addresses the offset-free model predictive control (MPC) for the intermittent transonic wind tunnel (ITWT). The offset-free property holds by introducing the controller state equation which introduces an integral action, while the controller state can keep as the decision variable. The infinite-horizon control moves are parameterized as a sequence of degrees of freedom followed by a control law. The terminal constraint set and terminal control law are designed based on the asymptotic invariance ellipsoid in which physical constraints are satisfied. The previous dual-mode MPC is appropriately modified withholding the closed-loop stability. Simulation results verify the effectiveness of the proposed methods.

The model with polytopic parametric uncertainty and bounded disturbance is controlled by the approach named dynamic OFRMPC (Output Feedback Robust Model Predictive Control). A key knob for control performance and region of attraction for this approach is the selection of Lyapunov matrix. A Lyapunov matrix, which does not have structural restriction, is proposed. In the ICCA (Iterative Cone Complementary Approach), which is invoked in optimizing the control law parameters, the starting up steps are designed as a variant CCA (Cone Complementary Approach). ICCA designs an outer loop, over CCA, for searching the minimum cost bound, while the variant CCA omits the outer loop by adding the cost bound in CCA objective function. This starting up can reduce the computational burden. The suboptimal dynamic OFRMPC (where CCA is avoided) is discussed, and a previous approach is re-formulated. A numerical example is given to show the advantages of the proposed approach.

When the mono-polar blocking fault occurs at the line-commutated converter (LCC)-based high voltage direct current (HVDC) transmission system, due to the long switch time of the installed mechanical switching type VAR compensations, the reactive power of the sending alternating current (AC) system will be surplus. Consequently, the voltage of sending AC system will be sharply increased during the switch process of VAR compensations, which may trip off the connected doubly fed induction generator (DFIG)-based wind farm. For ensuring the sending system can operate in stability and security, the power flow and the overvoltage mechanism of sending AC system under mono polar blocking have been investigated firstly. Furthermore, the power controllable operation area of the DFIG system as well as the transient response of the synchronous compensator (SC) and static synchronous compensator (STATCOM) have been researched. Then, a fault ride through control strategy by coordinating the VAR compensations, DFIG-based wind farm, HVDC system converter and SC is designed. The simulation results show that the proposed scheme can significantly restrain the sending AC system's overvoltage during the switch process of the installed VAR compensators under mono-polar blocking fault, thus, the transient stability of the HVDC sending system can be effectively enhanced.

This paper addresses a computationally efficient MPC approach for set-point optimization and its application to an intermittent transonic wind tunnel (ITWT). In the presented method, the open-loop prediction, which adopts the Kalman filter to obtain the open-loop dynamic/steady state predictions of manipulated/controlled variables (MVs/CVs), is presented. Based on the open-loop prediction, a linearization steady-state model featured by the total pressure and the Mach number is used for set-points optimization in steady-state target calculation (SSTC), being formulated as a linear programming (LP) problem. Based on these set-points of MVs/CVs, the dynamic control computes the optimal control moves by solving a quadratic programming (QP) problem. The effectiveness of the proposed method is illustrated on ITWT, and satisfactory performances are obtained.

In this study, a novel two-step model predictive control (MPC) for Hammerstein systems subject to norm-bounded disturbance is addressed. In the first step, the intermediate control law for the linear part of the system is posed as the solution to the unconstrained MPC problem that minimises a quadratic cost function over a given finite time, for which the solution is determined by a novel Riccati iterative equation. In the second step, the actual control move is obtained by solving non-linear algebraic equation group and desaturation. The quadratic boundedness technique is used to specify the stability for closed-loop system with norm-bounded disturbance, and the sufficient conditions for quadratic convergent of the system state are presented. Simulation results demonstrate the effectiveness of the proposed approach to this class of systems.

This paper is mainly concerned with the design problem of two-step model predictive control (MPC) for nonlinear systems represented by Hammerstein model, where the network-induced time delays exist between sensor to controller (S2C) and controller to actuator (C2A) links. We assume that the system state is not measurable, so the state observer is employed to estimate the state. The intermediate variable for the linear part of the system is calculated by minimising the quadratic performance function. The time-delay compensation algorithm of two-step output feedback predictive control (TSOFPC) for Hammerstein systems is presented and validated by a numerical example.

In this paper, we investigate a robust constrained model predictive control synthesis approach for discrete-time Takagi-Sugeno's (T-S) fuzzy system with structured uncertainty. The key idea is to determine, at each sampling time, a state feedback fuzzy predictive controller that minimizes the performance objective function in the infinite time horizon by solving a class of linear matrix inequalities (LMIs) optimization problem. To do this, the fuzzy predictive controller is designed on the basis of non-parallel distributed compensation (non-PDC) control law, relaxed stability conditions of the closed-loop fuzzy system are developed by employing an extended nonquadratic Lyapunov function and introducing additional slack and collection matrices. In addition, the presented approach is capable of ensuring the robust asymptotic stability as well as the recursive feasibility of the closed-loop fuzzy system. Simulations on a highly nonlinear continuous stirred tank reactor (CSTR) are eventually presented to demonstrate the effectiveness of the developed theoretical approach.

In this paper, a robust constrained model predictive control approach for discrete-time uncertain Takagi-Sugeno (T-S) fuzzy systems is developed. Based on the non-parallel distributed compensation law (non-PDC), a fuzzy predictive controller is designed to stabilize the resulting closed-loop system via an extended non-quadratic Lyapunov function. Slack matrices and collection matrix are employed to obtain less conservative results and sufficient conditions for the solvability of this problem is provided in the form of linear matrix inequalities, the real-time fuzzy predictive control law is easily calculated and implemented, at each sampling time, which minimizes the objective function over infinite moving horizon subjects to input and output constraints. To demonstrate the effectiveness and applicability, the simulation results on a continuous stirred tank reactors (CSTR) is illustrated by the fuzzy model predictive control approach we proposed.