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This paper presents the design of a fault detection and diagnosis system for a quadrotor unmanned aerial vehicle under partial or total actuator fault. In order to control the quadrotor, the dynamic system is divided in two subsystems driven by the translational and the rotational dynamics, where the rotational subsystem is based on a linear parameter-varying model. A robust linear parameter-varying observer applied to the rotational subsystem is considered to detect actuator faults, which can occur as total failures (loss of a propeller or a motor) or partial faults (degradation). Furthermore, fault diagnosis is done by analyzing the displacements of the roll and pitch angles. Numerical experiments are carried out in order to illustrate the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

This paper presents a fast and accurate robust path-following control approach for a fully actuated marine surface vessel in the presence of external disturbances. The path following is realized by simultaneously converging the yaw rate and sway velocity to their respective desired values, which are generated according to the path-following demand. An improved combined control strategy using an integral terminal sliding mode (ITSM) based composite nonlinear feedback (CNF) technique considering the external disturbances, time-varying tracking reference, input saturations and transient performance improvement is proposed in this study. The proposed ITSM-CNF combines the advantages of the CNF control in improving the transient performance and of the ITSM control in guaranteeing good robustness and finite-time convergence. A continuous and smooth sliding mode controller, based on an integral nonsingular terminal sliding surface, is added to the CNF controller to eliminate chattering. The overall stability of the closed-loop system is strictly proved based on the Lyapunov method. Simulations verify the effectiveness of the ITSM-CNF controller in improving the transient path-following performance, inhibiting overshoots, eliminating steady-state errors, rejecting external disturbances and removing chattering effects, considering input saturations, varying path curvature and finite-time convergence. [ABSTRACT FROM AUTHOR]

In this paper, a novel fuzzy Generalized Predictive Control (GPC) is proposed for discrete-time nonlinear systems via Takagi-Sugeno system based Kernel Ridge Regression (TS-KRR). The TS-KRR strategy approximates the unknown nonlinear systems by learning the Takagi-Sugeno (TS) fuzzy parameters from the input-output data. Two main steps are required to construct the TS-KRR: the first step is to use a clustering algorithm such as the clustering based Particle Swarm Optimization (PSO) algorithm that separates the input data into clusters and obtains the antecedent TS fuzzy model parameters. In the second step, the consequent TS fuzzy parameters are obtained using a Kernel ridge regression algorithm. Furthermore, the TS based predictive control is created by integrating the TS-KRR into the Generalized Predictive Controller. Next, an adaptive, online, version of TS-KRR is proposed and integrated with the GPC controller resulting an efficient adaptive fuzzy generalized predictive control methodology that can deal with most of the industrial plants and has the ability to deal with disturbances and variations of the model parameters. In the adaptive TS-KRR algorithm, the antecedent parameters are initialized with a simple K-means algorithm and updated using a simple gradient algorithm. Then, the consequent parameters are obtained using the sliding-window Kernel Recursive Least squares (KRLS) algorithm. Finally, two nonlinear systems: A surge tank and Continuous Stirred Tank Reactor (CSTR) systems were used to investigate the performance of the new adaptive TS-KRR GPC controller. Furthermore, the results obtained by the adaptive TS-KRR GPC controller were compared with two other controllers. The numerical results demonstrate the reliability of the proposed adaptive TS-KRR GPC method for discrete-time nonlinear systems. [ABSTRACT FROM AUTHOR]

This study addresses the control of locally Lipschitz non‐linear systems under quantisation and input saturation non‐linearities. The non‐linear dynamics of the systems are taken to be locally Lipschitz, rather than the conventional globally Lipschitz counterpart, to consider a generalised form of the Lipschitz non‐linear systems. An ellipsoidal region containing the origin is constructed, for which the non‐linear dynamics satisfy the Lipschitz condition in contrast to the conventional approaches. Saturation and quantisation non‐linearities are dealt using a generalised local sector condition and a bound on the quantisation noise. A regional control strategy for the stabilisation of non‐linear systems using state feedback is devised by employing these conditions, which is further extended to attain robustness against external perturbations. The proposed control strategies guarantee convergence of the states of a non‐linear system inside a bounded reducible region in the neighbourhood of the origin. In contrast to the conventional approaches, the present study considers ellipsoidally Lipschitz non‐linear systems, supports various types of quantisers, ensures attenuation of the disturbances, and provides a clear picture of the region of stability. An example of the application of the proposed control strategies for a modified Chua's circuit is demonstrated. [ABSTRACT FROM AUTHOR]

The leader–following consensus issue is investigated for multiagent systems under external disturbances and denial-of-service (DoS) attacks. DoS attacks are considered to be designed according to an unknown attack strategy and would block the communication network. First, a novel triggering mechanism is designed to save communication resources during normal communication periods by event-triggered strategy and to detect the end of attack periods by time-triggered strategy. Then, a set of estimators is constructed to predict the states of the agent itself and its neighbors when the attack occurs. The estimators do not contain the control signals, which only correct the prediction when the actual system states are received. As a result, a distributed switching controller is designed that includes event-triggered states during normal communication periods and predicted states during attack periods. Finally, the proposed secure control protocol is proven to ensure that the system states are eventually consensus and the Zeno behavior is not exhibited. Moreover, a simulation example is given to illustrate the effectiveness of the presented strategy. [ABSTRACT FROM AUTHOR]

This paper presents a robust H ∞ output-feedback vehicle lateral motion control strategy considering network-induced delay and tire force saturation. The unavoidable time delay in the in-vehicle networks degrades the control performance, and even deteriorates the system stability. In addition, the tire lateral force suffers saturation phenomenon, which also deteriorates the control effect in extreme driving conditions. To handle the network-induced control delay and tire force saturation, a robust H ∞ controller is presented to regulate the vehicle lateral motion. An output-feedback control schema, which does not need the vehicle lateral velocity, is designed to achieve the desired control performance and reduce the cost of control system. The tire cornering stiffness uncertainty and external disturbances are also considered in the controller design to improve the robustness of the proposed controller. The comparative simulation results based on Carsim-Simulink joint simulation verify the effectiveness and robustness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This paper presents a robust H ∞ output-feedback control strategy for the path following of autonomous ground vehicles (AGVs). Considering the vehicle lateral velocity is usually hard to measure with low cost sensor, a robust H ∞ static output-feedback controller based on the mixed genetic algorithms (GA)/linear matrix inequality (LMI) approach is proposed to realize the path following without the information of the lateral velocity. The proposed controller is robust to the parametric uncertainties and external disturbances, with the parameters including the tire cornering stiffness, vehicle longitudinal velocity, yaw rate and road curvature. Simulation results based on CarSim–Simulink joint platform using a high-fidelity and full-car model have verified the effectiveness of the proposed control approach. [ABSTRACT FROM AUTHOR]

This paper investigates the yaw control issue for in-wheel-motor (IWM) electric ground vehicles (EGVs) based on the differential steering in the presence of the complete failure of the active front-wheel steering. Differential steering is an emerging steering mechanism, generated from the differential torque between the left and right wheels in IWM EGVs. In case that the regular steering system is defective, differential steering can be utilized to act as the sole steering power, and thus avoid dangerous consequences for vehicles. For this purpose, a robust H ∞ output-feedback controller based on differential steering is designed to achieve yaw stabilization, considering that the desired steering angle is uncertain and hard to obtain. Parameter uncertainties for the cornering stiffnesses and the external disturbances are considered to make vehicle robust to different driving conditions. CarSim-Simulink joint simulation results based on a high-fidelity and full-car model verify the effectiveness of the proposed controller to guarantee the equal vehicle handling and stability. [ABSTRACT FROM AUTHOR]

Abstract: The paper is concerned with the design of a fuzzy model-based predictive controller for activated sludge wastewater treatment processes. The control purpose is to maintain the dissolved oxygen concentration in an aerobic reactor of the wastewater treatment plant at the set-point. The fuzzy model of the activated sludge processes is derived based on the Activated Sludge Model No. 1 (ASM1), including the structure of the fuzzy rules. The required fuzzy space of input variables is partitioned by fuzzy c-means cluster algorithm and the consequent parameters are identified using the method of least squares. Compared with both traditional PID control and dynamic matrix control schemes, the proposed fuzzy model-based predictive control paradigm achieves satisfactory benefits in terms of both transient and steady performances. [Copyright &y& Elsevier]

This article studies the problem of memory event-triggered cooperative adaptive control of heterogeneous nonlinear multiagent systems (MASs) under denial-of-service (DoS) attacks based on the multiplayer mixed zero-sum (ZS) game strategy. First, a neural-network-based reinforcement learning scheme is structured to obtain the Nash equilibrium solution of the proposed multiplayer mixed ZS game scheme. Then, a memory-based event-triggered mechanism considering the historical data is proposed. This effectively avoids incorrect triggering information caused by unknown external factors. Moreover, thanks to the idea of switching topology, the mixed ZS game problem under the influence of node-based DoS attacks is solved efficiently. In accordance with the Lyapunov stability theory, it is proved that all signals of heterogeneous MASs are bounded, all heterogeneous followers can track the trajectory of the leader during the no-attack period, the attacked follower can achieve stabilization control during the attack period, and the remaining nonattacked followers can achieve cooperative control during the attack period. Finally, the effectiveness of the designed memory-event-triggered-based mixed ZS game cooperative control strategy is tested by the given simulation results.

This paper provides a detailed analysis of the output voltage/current tracking control of a PWM DCDC converter that has been modeled as a Markov jump system. In order to achieve that, a dynamic sensorless strategy is proposed to perform active disturbance rejection control. As a convex optimization problem, a novel reformulation of the problem is provided to compute optimal control. Accordingly, necessary less conservative conditions are established via Linear Matrix Inequalities. First, a sensorless active disturbance rejection design is proposed. Then, to carry out the control process, a robust dynamic observer-predictive controller approach is introduced. Meanwhile, the PWM DC-DC switching power converters are examined as discrete-time Markovian switching systems. Considering that the system is subject to modeling uncertainties, time delays, and load variations as external disturbances, and by taking partial input saturation into account, the Lyapunov-Krasovskii function is used to construct the required feasibility frame and less conservative stability conditions. As a result, the proposed design provides an efficient control strategy with disturbance rejection and time-delay compensation capabilities and maintains robust performance with respect to constraints. Finally, a PWM DC-DC power converter simulation study is performed in different scenarios, and the obtained results are illustrated in detail to demonstrate the effectiveness of the proposed approach.

In this article, a novel integral reinforcement learning (RL)-based nonfragile output feedback tracking control algorithm is proposed for uncertain Markov jump nonlinear systems presented by the Takagi-Sugeno fuzzy model. The problem of nonfragile control is converted into solving the zero-sum games, where the control input and uncertain disturbance input can be regarded as two rival players. Based on the RL architecture, an offline parallel output feedback tracking learning algorithm is first designed to solve fuzzy stochastic coupled algebraic Riccati equations for Markov jump fuzzy systems. Furthermore, to overcome the requirement of a precise system information and transition probability, an online parallel integral RL-based algorithm is designed. Besides, the tracking object is achieved and the stochastically asymptotic stability, and expected H ∞ performance for considered systems is ensured via the Lyapunov stability theory and stochastic analysis method. Furthermore, the effectiveness of the proposed control algorithm is verified by a robot arm system.

The finite-time event-triggered stabilization is studied for a class of discrete-time nonlinear Markov jump singularly perturbed models with partially unknown transition probabilities (TPs). T-S fuzzy strategy is adopted to characterize the related nonlinear Markov jump singularly perturbed models. The control objective is to make sure that the system states remain within a bounded domain during a fixed-time interval. First, a mode-dependent event-triggered scheme is constructed to reduce the communication burden and save the network bandwidth. On that basis, by using a new Lyapunov function, a developed finite-time stability criterion is derived for the corresponding system to avoid an ill-conditioned issue due to a small singular perturbation parameter. Moreover, the mode-dependent fuzzy controller gain and the event-triggered parameter are co-designed under the framework of partially unknown TPs. Finally, the feasibility of the main results is provided to verify the finite-time event-triggered control strategy.

In this article, the group consensus problem is addressed for a network of multiagent systems (MASs). Unlike in existing literature, where a relative-state feedback-based distributed control input is used to achieve group consensus, this work aims at designing a relative-output-based distributed control law to achieve the same goal. To that effect, the Lyapunov stability theory is used to formulate the sufficient and necessary conditions for the existence of such a feedback controller and then separate conditions have been included for its design. In addition to that, a new linear matrix inequality is explored to choose the intracluster coupling strengths to ensure group consensus. In this article, the relative-output-based control approach is investigated for both the leaderless and the leader-following frameworks of the group consensus problem, and the theoretical findings presented are validated using numerical examples and simulation results.

This paper deals with the problem of robust unknown input fault detection observers (UIFDOs) design for interval type-2 Takagi-Sugeno (T-S) fuzzy systems with immeasurable premise variables. It has been shown that choosing the system states, which may be immeasurable, as premise variables, will able us to model a larger class of nonlinear systems. Accordingly, the premise variables of underlying system are considered to be immeasurable. However, the design procedure of a stable observer for such systems are more challenging. Furthermore, the system is supposed be affected by time-varying delays and unknown inputs. The UIFDO is exploited so as to generate a residual signal with the most possible sensitivity to fault and the least sensitivity to exogenous signals. In this paper, this issue is investigated thoroughly, in this respect the design procedure consists of two sections: 1) measurable and 2) immeasurable premise variables. Sufficient design conditions are provided in terms of linear matrix inequalities for both cases. The effectiveness of the proposed UIFDO in detection of two different kinds of faults is illustrated during the simulation of a numerical example. Moreover, a fair comparison has been drawn between the proposed UIFDO and an existent reference to indicate that interval type-2 T-S fuzzy model is more superior than type-1. Finally, the faulty behavior of a one-link manipulator is investigated to express the applicability of the proposed method.

This paper investigates the problem of exponential stabilization for sampled-data Takagi-Sugeno (T-S) fuzzy control systems with packet dropouts. An input delay approach is adopted to model the sample-and-hold behavior with a time-varying delayed control input, and a switched system approach is proposed to model the data-missing phenomenon. On this basis, the sampled-data T-S fuzzy control system with packet dropouts is modeled as a switched T-S fuzzy system with time-varying delay. The objective is to design a sampled-data fuzzy controller to guarantee the exponential stability of the resulting closed-loop system. Based on a new piecewise time-dependent Lyapunov functional, a novel sufficient condition is derived for the existence of exponentially stabilizing sampled-data fuzzy controllers. All the solutions to the problem are formulated in the form of linear matrix inequalities. Finally, two simulation examples are provided to illustrate the effectiveness of the proposed methods.