Fault tolerant flight control system for the tilt-rotor UAV

A fault tolerant control scheme for actuator and sensor faults is proposed for a tilt-rotor unmanned aerial vehicle (UAV) system. The tilt-rotor UAV has a vertically take-off and landing (VTOL) capability like a helicopter during the take-off & landing while it could cruise with a high speed as a conventional airplane flight mode. A dual system in the flight control computer (FCC) and the sensor is proposed in this study. To achieve a high reliability, a fault tolerant flight control system is required for the case of actuator or sensor fault. For the actuator fault, the fault tolerant control scheme based on model error control synthesis is presented. A designed fault tolerant control scheme does not require system identification process and it provides an effective reconfigurability without fault detection and isolation (FDI) process. For the sensor fault, the fault tolerant federated Kalman filter is designed for the tilt-rotor UAV system. An FDI algorithm is applied to the federated Kalman filter in order to improve the accuracy of the state estimation even when the sensor fails. For a linearized six-degree-of-freedom linear model and nonlinear model of the tilt-rotor UAV, numerical simulation and process-in-the-loop simulation (PILS) are performed to demonstrate the performance of the proposed fault tolerant control scheme.     

Fault estimation and fault tolerant control for discrete-time nonlinear systems with perturbation by a mixed design scheme

This paper focuses on the observer-based fault-tolerant control problem for the discrete-time nonlinear systems with the perturbation and the fault signals. First, the nonlinear term with perturbation is put into the local nonlinear part so that the nonlinear system with perturbation can be described as an interval type-1 (IT1) T-S fuzzy system. Then, based on the unknown input observer technology, the IT1 T-S fuzzy fault estimation (FE) observer scheme is presented to obtain the real-time FE information and decouple the local nonlinear part from the estimation error system, where the design complexity and the computational burden are reduced simultaneously. Second, based on the real-time FE information, an FE-based interval type-2 (IT2) T-S fuzzy fault-tolerant control scheme is presented to achieve the compensation for the influence of the fault signal and the stabilization for the system. Different from the traditional methods, a mixed design scheme, which is based on the IT1 T-S fuzzy fault estimation observer method and the IT2 T-S fuzzy fault-tolerant controller method, is proposed in this paper. This strategy can not only reduce the computational burden, but also obtain a less conservative result. Finally, the effectiveness of the mixed design approach is illustrated by an example.     

Stochastic fault tolerant control of networked control systems

A problem of stabilization about uncertain networked control systems (NCSs) with random but bounded delays is discussed in this paper. By using augmented state-space method, this class of problems can be modeled as discrete-time jump linear systems governed by finite-state Markov chains. A new switched model based on probability is proposed to research problems of reliable control when actuators become ageing or partially disabled. Using improved V-K iteration algorithm, a class of reliable controllers are designed to make systems asymptotically mean square stable under several stochastic disturbances such as random time-delay and stochastic actuator failure and the maximal redundancy degree is given through this method.     

Active fault tolerant control scheme for aircraft with dissimilar redundant actuation system subject to hydraulic failure

In this paper, an active fault tolerant control (AFTC) scheme is proposed for more electric aircraft (MEA) equipped with dissimilar redundant actuation system (DRAS). The effect of various fault/failure of hydraulic actuator (HA) on the system performance is analyzed in this work. In nominal condition, the state feedback control law is designed for primary control surfaces. In the presence of fault/failure of certain HA, control allocation (CA) scheme together with integral sliding mode controller (ISMC) is retrofitted with existing control law and engaged the secondary (redundant) actuators into the loop. A modified recursive least square (RLS) algorithm is proposed to identify the parametric faults in HA and to measure the effectiveness level of the actuator. In an event of failure of all HA’s in the system, electro hydraulic actuators (EHA) are taken in loop to bring the system back to its nominal operation. In order to stabilize the closed-loop dynamics of HA and EHA, fractional order controllers are designed separately for each actuator. Simulations on the lateral directional model of aircraft demonstrated the effectiveness of the proposed scheme as compared to the existing methods in the literature.     

Statistical information based two-layer model predictive control with dynamic economy and control performance for non-Gaussian stochastic process

In this paper, a two-layer model predictive control (MPC) hierarchical architecture of dynamic economic optimization (DEO) and reference tracking (RT) is proposed for non-Gaussian stochastic process in the framework of statistical information. In the upper layer, with state feedback and dynamic economic information, the economically optimal trajectories are estimated by entropy and mean based dynamic economic MPC, which uses the nonlinear dynamic model instead of the steady-state model. These estimated optimal trajectories from the upper layer are then employed as the reference trajectories of the lower layer control system. A survival information potential based MPC algorithm is used to maintain the controlled variables at their reference trajectories in the nonlinear system with non-Gaussian disturbances. The stability condition of closed-loop system dynamics is proved using the statistical linearization method. Finally, a numerical example and a continuous stirred-tank reactor are used to illustrate the merits of the proposed economic optimization and control method.     

Dynamic learning-based fault tolerant control for robotic manipulators with actuator faults

In this paper, a learning-based active fault-tolerant control (FTC) scheme for robot manipulators with uncertainties and actuator faults is proposed. Unlike traditional FTC methods, with dynamic learning theory, both uncertainties and actuator faults can be accurately identified/learned by radial basis function networks. Based on the learned knowledge, dynamical classifiers and experience-based controllers corresponding to different fault modes are constructed. With the help of dynamical classifiers, fault detection and isolation can be obtained rapidly and accurately, and the correct experience-based controller (instead of the controller reconfigured online) corresponding to the current fault system is selected to compensate for faults, and superior control performance is achieved, even in the presence of faults. The simulation studies demonstrate the feasibility of the proposed FTC method.     

Fault detection and diagnosis for stochastic systems via output PDFs

This paper investigates a new type of fault detection and diagnosis (FDD) problem for non-Gaussian stochastic distribution systems via the output probability density function (PDF). The PDF can be approximated by using square root B-spline expansions. In this framework, an optimal fault detection algorithm is presented by introducing the tuning parameter such that the residual is as sensitive as possible to the fault. When the fault occurs, an adaptive network parameter-updating law is designed to approximate the fault. At last, paper-making process example is given to demonstrate the efficiency of the proposed approach.     

Fault tolerant cooperative control for affine multi-agent systems: An optimal control approach

The goal of this paper is to propose an optimal fault tolerant control (FTC) approach for multi-agent systems (MASs). It is assumed that the agents have identical affine dynamics. The underlying communication topology is assumed to be a directed graph. The concepts of both inverse optimality and partial stability are further employed for designing the control law fully developed in the paper. Firstly, the optimal FTC problem for linear MASs is formulated and then it is extended to MASs with affine nonlinear dynamics. To solve the Hamilton-Jacobi-Bellman (HJB) equation, an Off-policy Reinforcement Learning is used to learn the optimal control law for each agent. Finally, a couple of numerical examples are provided to demonstrate the effectiveness of the proposed scheme.     

Robust suboptimal feedback control for a fed-batch nonlinear time-delayed switched system

The optimal control strategy constructed in the form of a state feedback is effective for small state perturbations caused by changes in modeling uncertainty. In this paper, we investigate a robust suboptimal feedback control (RSPFC) problem governed by a nonlinear time-delayed switched system with uncertain time delay arising in a 1,3-propanediol (1,3-PD) microbial fed-batch process. The feedback control strategy is designed based on the radial basis function to balance the two (possibly competing) objectives: (i) the system performance (concentration of 1,3-PD at the terminal time of the fermentation) is to be optimal; and (ii) the system sensitivity (the system performance with respect to the uncertainty of the time-delay) is to be minimized. The RSPFC problem is subject to the continuous state inequality constraints. An exact penalty method and a novel time scaling transformation approach are used to transform the RSPFC problem into the one subject only to box constraints. The resulting problem is solved by a hybrid optimization algorithm based on a filled function method and a gradient-based algorithm. Numerical results are given to verify the effectiveness of the developed hybrid optimization algorithm.     

A predictive fault tolerant control method for qLPV systems subject to input faults and constraints

In this paper, we investigate the use of Model Predictive Control (MPC) applications for quasi-Linear Parameter Varying (qLPV) systems subject to faults along the input channels. We propose a Fault Tolerant Control (FTC) mechanism based on a robust state-feedback MPC synthesis, considering polytopic inclusions. In order to alleviate the numerical burden of the robust min-max procedure, we use small prediction horizons, in such a way that the solution becomes viable for real-time systems. The FTC system is able to tolerate time-varying saturation of the actuator, which may happen due to malfunctions. Recursive feasibility and poly-quadratic stability guarantees are ensured through the synthesis of adequate terminal ingredients. Accordingly, we present a catalogue of three different LMI remedies, considering: (a) parameter-independent ingredients, (b) a parameter-dependent terms and (c) a parameter-dependent maps that take into account bounded rates of parameter variation. An autonomous driving car example is used to illustrate the performances of the proposed technique, which is compared to other MPCs from the literature. The proposed FTC method is able to ensure good performances, obtained with reduced computational demand.     

Decentralized fault tolerant model predictive control of discrete-time interconnected nonlinear systems

This paper presents a novel approach to address the decentralized fault tolerant model predictive control of discrete-time interconnected nonlinear systems. The overall system is composed of a number of discrete-time interconnected nonlinear subsystems at the presence of multiple faults occurring at unknown time-instants. In order to deal with the unknown interconnection effects and changes in model dynamics due to multiple faults, both passive and active fault tolerant control design are considered. In the Active fault tolerant case an online approximation algorithm is applied to estimate the unknown interconnection effects and changes in model dynamics due to multiple faults. Besides, the decentralized control strategy is implemented for each subsystem with the model predictive control algorithm subject to some constraints. It is showed that the proposed method guarantees input-to-state stability characterization for both local subsystems and the global system under some predetermined assumptions. The simulation results are exploited to illustrate the applicability of the proposed method.     

Robust fault tolerant control of robot manipulators with global fixed-time convergence

In this paper, a robust fault tolerant control, which provides a global fixed-time stability, is proposed for robot manipulators. This approach is constructed based on an integration between a fixed-time second-order sliding mode observer (FxTSOSMO) and a fixed-time sliding mode control (FxTSMC) design strategy. First, the FxTSOSMO is developed to estimate the lumped disturbance with a fixed-time convergence. Then, based on the obtained disturbance estimation, the FxTSMC is developed based on a fixed-time sliding surface and a fixed-time reaching strategy to form a global fixed-time convergence of the system. The proposed approach is then applied for fault tolerant control of a PUMA560 robot and compared with other state-of-the-art controllers. The simulation results verify the outstanding fault estimation and fault accommodation capability of the proposed fault diagnosis observer and fault tolerant strategy, respectively.     

Active fault tolerant control design for reusable launch vehicle using adaptive sliding mode technique

In this paper, the problem of active fault tolerant control for a reusable launch vehicle (RLV) with actuator fault using both adaptive and sliding mode techniques is investigated. Firstly, the kinematic equations and dynamic equations of RLV are given, which represent the characteristics of RLV in reentry flight phase. For the dynamic model of RLV in faulty case, a fault detection scheme is proposed by designing a nonlinear fault detection observer. Then, an active fault tolerant tracking strategy for RLV attitude control systems is presented by making use of both adaptive control and sliding mode control techniques, which can guarantee the asymptotic output tracking of the closed-loop attitude control systems in spite of actuator fault. Finally, simulation results are given to demonstrate the effectiveness of the developed fault tolerant control scheme.     

Adaptive output feedback fault tolerant control for uncertain nonlinear systems based on actuator switching

In this paper, an adaptive output feedback fault tolerant control (FTC) based on actuator switching is proposed for a class of single-input single-output (SISO) nonlinear systems with uncertain parameters and possible actuator failures, for which a set of healthy actuators are available as backups. While high-gain K-filters are utilized to estimate the unmeasured states, an adaptive control law is designed to compensate for the parameter uncertainties and certain actuator failures, an actuator switching strategy based on a set of appropriately designed monitoring functions (MFs) is proposed to tackle those serious actuator failures, make tracking error satisfy prescribed transient and steady-state performance and guarantee closed-loop signal boundedness.     

Fault tolerant control for partial loss of control authority in aircraft using piecewise affine slab models

In this paper, a new fault tolerant control methodology is proposed for partial loss of control authority in aircraft using piecewise affine (PWA) slab models while minimizing an upper bound on a quadratic cost function. The proposed controller stabilizes and satisfies performance bounds for both the nominal and faulty systems. The controller design criteria are cast as a set of Linear Matrix Inequalities (LMIs) that can be solved efficiently. The new technique is illustrated in a numerical example for the Beechcraft 99 aircraft model.     

Event-Triggered formation control for time-delayed discrete-Time multi-Agent system applied to multi-UAV formation flying

This paper studies the formation control for a time-delayed discrete-time multi-agent system (MAS). An event-triggered controller is proposed to reduce the communication load of the system. Based on the designed event-triggered condition and properties of Schur stable matrix, the stability of formation for discrete-time MAS is proved. Utilizing the virtual simulation platform integrated Robot Operating System (ROS) and Gazebo, a virtual scene with unmanned aerial vehicles (UAVs) models is built and the verification for the theoretical algorithm is completed. Finally, an experimental platform with four practical UAVs is constructed and the result shows that the expected formation is achieved and controller proposed can solve the formation control problem for time-delayed discrete-time MASs. Besides, the effectiveness of the event-triggered mechanism on reducing communication frequency is comfirmed in practical scenarios.