Supervisory fault tolerant control of the GTM UAV using LPV methods

A multi-level reconfiguration framework is proposed for fault tolerant control of over-actuated aerial vehicles, where the levels indicate how much authority is given to the reconfiguration task. On the lowest, first level the fault is accommodated by modifying only the actuator/sensor configuration, so the fault remains hidden from the baseline controller. A dynamic reallocation scheme is applied on this level. The allocation mechanism exploits the actuator/sensor redundancy available on the aircraft. When the fault cannot be managed at the actuator/sensor level, the reconfiguration process has access to the baseline controller. Based on the LPV control framework, this is done by introducing fault-specific scheduling parameters. The baseline controller is designed to provide an acceptable performance level along all fault scenarios coded in these scheduling variables. The decision on which reconfiguration level has to be initiated in response to a fault is determined by a supervisor unit. The method is demonstrated on a full six-degrees-of-freedom nonlinear simulation model of the GTM UAV

Fault tolerant LPV control of the GTM UAV with dynamic control allocation

The aim of the paper is to present a dynamic control allocation architecture for the design and development of reconfigurable and fault-tolerant control systems in aerial vehicles. The baseline control system is designed for the nominal dynamics of the aircraft, while faults and actuator saturation limits are handled by the dynamic control allocation scheme. Coordination of these components is provided by a supervisor which re-allocates control authority based on health information, flight envelope limits and cross coupling between lateral and longitudinal motion. The monitoring components and FDI filters provide the supervisor with information about different fault operations, based on that it is able to make decisions about necessary interventions into the vehicle motions and guarantee fault-tolerant operation of the aircraft. The design of the proposed reconfigurable control algorithm is based on Linear Parameter-varying (LPV) control methods that uses a parameter dependent dynamic control allocation scheme. The design is demonstrated on the lateral axis motion of the NASA AirSTAR Flight Test Vehicle simulation model

Fault Tolerant Flight Control of Unmanned Aerial Vehicles

Safety, reliability and acceptable level of performance of dynamic control systems are the major keys in all control systems especially in safety-critical control systems. A controller should be capable of handling noises and uncertainties imposed to the controlled process. A fault-tolerant controller should be able to control a system with guaranteed stability and good or acceptable performance not only in normal operation conditions but also in the presence of partial faults or total failures that can be occurred in the components of the system. When a fault occurs in a system, it suddenly starts to behave in an unanticipated manner. Thereby, a fault-tolerant controller should be designed for being able to handle the fault and guarantee system stability and acceptable performance in the presence of faults/damages. This shows the importance and necessity of Fault-Tolerant Control (FTC) to safety-critical and even nowadays for some new and non-safety-critical systems. During recent years, Unmanned Aerial Vehicles (UAVs) have proved to play a significant role in military and civil applications. The success of UAVs in different missions guarantees the growing number of UAVs to be considerable in future. Reliability of UAVs and their components against faults and failures is one of the most important objectives for safety-critical systems including manned airplanes and UAVs. The reliability importance of UAVs is implied in the acknowledgement of the Office of the Secretary of Defense in the UAV Roadmap 2005-2030 by stating that, ”Improving UA [unmanned aircraft] reliability is the single most immediate and long-reaching need to ensure their success”. This statement gives a wide future scenery of safety, reliability and Fault-Tolerant Flight Control (FTFC) systems of UAVs. The main objective of this thesis is to investigate and compare some aspects of fault tolerant flight control techniques such as performance, robustness and capability of handling the faults and failures during the flight of UAVs. Several control techniques have been developed and tested on two main platforms at Concordia University for fault-tolerant control techniques development, implementation and flight test purposes: quadrotor and fixedwing UAVs. The FTC techniques developed are: Gain-Scheduled Proportional-Integral-Derivative (GS-PID), Control Allocation and Re-allocation (CA/RA), Model Reference Adaptive Control (MRAC), and finally the Linear Parameter Varying (LPV) control as an alternative and theoretically more comprehensive gain scheduling based control technique. The LPV technique is used to control the quadrotor helicopter for fault-free conditions. Also a GS-PID controller is used as a fault-tolerant controller and implemented on a fixedwing UAV in the presence of a stuck rudder failure case

Certification Considerations for Adaptive Systems

Advanced capabilities planned for the next generation of aircraft, including those that will operate within the Next Generation Air Transportation System (NextGen), will necessarily include complex new algorithms and non-traditional software elements. These aircraft will likely incorporate adaptive control algorithms that will provide enhanced safety, autonomy, and robustness during adverse conditions. Unmanned aircraft will operate alongside manned aircraft in the National Airspace (NAS), with intelligent software performing the high-level decision-making functions normally performed by human pilots. Even human-piloted aircraft will necessarily include more autonomy. However, there are serious barriers to the deployment of new capabilities, especially for those based upon software including adaptive control (AC) and artificial intelligence (AI) algorithms. Current civil aviation certification processes are based on the idea that the correct behavior of a system must be completely specified and verified prior to operation. This report by Rockwell Collins and SIFT documents our comprehensive study of the state of the art in intelligent and adaptive algorithms for the civil aviation domain, categorizing the approaches used and identifying gaps and challenges associated with certification of each approach

Reducing the Mast Vibration of Single-Mast Stacker Cranes by Gain-Scheduled Control

In the frame structure of stacker cranes harmful mast vibrations may appear due to the inertial forces of acceleration or the braking movement phase. This effect may reduce the stability and positioning accuracy of these machines. Unfortunately, their dynamic properties also vary with the lifted load magnitude and position. The purpose of the paper is to present a controller design method which can handle the effect of a varying lifted load magnitude and position in a dynamic model and at the same time reveals good reference signal tracking and mast vibration reducing properties. A controller design case study is presented step by step from dynamic modeling through to the validation of the resulting controller. In the paper the dynamic modeling possibilities of single-mast stacker cranes are summarized. The handling of varying dynamical behavior is realized via the polytopic LPV modeling approach. Based on this modeling technique, a gain-scheduled controller design method is proposed, which is suitable for achieving the goals set. Finally, controller validation is presented by means of time domain simulations

Predictive control approaches to fault tolerant control of wind turbines

This thesis focuses on active fault tolerant control (AFTC) of wind turbine systems. Faults in wind turbine systems can be in the form of sensor faults, actuator faults, or component faults. These faults can occur in different locations, such as the wind speed sensor, the generator system, drive train system or pitch system. In this thesis, some AFTC schemes are proposed for wind turbine faults in the above locations. Model predictive control (MPC) is used in these schemes to design the wind turbine controller such that system constraints and dual control goals of the wind turbine are considered. In order to deal with the nonlinearity in the turbine model, MPC is combined with Takagi-Sugeno (T-S) fuzzy modelling. Different fault diagnosis methods are also proposed in different AFTC schemes to isolate or estimate wind turbine faults.The main contributions of the thesis are summarized as follows:A new effective wind speed (EWS) estimation method via least-squares support vector machines (LSSVM) is proposed. Measurements from the wind turbine rotor speed sensor and the generator speed sensor are utilized by LSSVM to estimate the EWS. Following the EWS estimation, a wind speed sensor fault isolation scheme via LSSVM is proposed.A robust predictive controller is designed to consider the EWS estimation error. This predictive controller serves as the baseline controller for the wind turbine system operating in the region below rated wind speed.T-S fuzzy MPC combining MPC and T-S fuzzy modelling is proposed to design the wind turbine controller. MPC can deal with wind turbine system constraints externally. On the other hand, T-S fuzzy modelling can approximate the nonlinear wind turbine system with a linear time varying (LTV) model such that controller design can be based on this LTV model. Therefore, the advantages of MPC and T-S fuzzy modelling are both preserved in the proposed T-S fuzzy MPC.A T-S fuzzy observer, based on online eigenvalue assignment, is proposed as the sensor fault isolation scheme for the wind turbine system. In this approach, the fuzzy observer is proposed to deal with the nonlinearity in the wind turbine system and estimate system states. Furthermore, the residual signal generated from this fuzzy observer is used to isolate the faulty sensor.A sensor fault diagnosis strategy utilizing both analytical and hardware redundancies is proposed for wind turbine systems. This approach is proposed due to the fact that in the real application scenario, both analytical and hardware redundancies of wind turbines are available for designing AFTC systems.An actuator fault estimation method based on moving horizon estimation (MHE) is proposed for wind turbine systems. The estimated fault by MHE is then compensated by a T-S fuzzy predictive controller. The fault estimation unit and the T-S fuzzy predictive controller are combined to form an AFTC scheme for wind turbine actuator faults

Fault-Tolerant Control with Applications to Aircraft Using Linear Quadratic Design Framework

Safety is one of the major concerns in the aviation community for both manned aircraft and unmanned aerial vehicles (UAVs). The safety issue of manned aircraft, such as commercial aircraft, has drawn great attentions especially after a series of disasters in recent decades. Safety and reliability issues of UAVs have also attracted significant attention due to their highly autonomous feature towards their future civilian applications. Focusing on the improvement of safety and reliability of aircraft, a fault-tolerant control (FTC) system is demanded to utilize the configured redundancy in an effective and efficient manner to increase the survivability of aircraft in the presence of faults/failures. This thesis aims to develop an effective FTC system to improve the security, reliability, and survivability of the faulty aircraft: manned aircraft and UAVs. In particular, the emphases are focused on improving the on-line fault-tolerant capability and the transient performance between faults occurrence and control re-configuration. In the existing fault-tolerant literature, several control approaches are developed to possess fault-tolerant capability in recent decades, such as sliding mode control (SMC), model reference adaptive control (MRAC), and model predictive control (MPC), just as examples. Different strategies have their specific benefits and drawbacks in addressing different aspects of fault-tolerant problems. However, there are still open problems in the fault-tolerant performance improvement, the transient behavior management, consideration of the interaction between FTC and fault detection and diagnosis (FDD), etc. For instance, MPC is recognized as a suitable inherent structure in synthesizing a FTC system due to its capability of addressing faults via solving constraints, reforming cost function, and updating model on-line. However, this on-line FTC capability introduces further challenges in terms of fault problem formulation, on-line computation, transient behavior before reconfiguration is triggered, etc. Designing an efficient FDD is also a challenge topic with respect to time response speed, accuracy, and reliability due to its interaction with a fault-tolerant controller. In the control design framework based on linear quadratic (LQ) cost function formulation, faults can be accommodated in both passive and active way. A passive FTC system is synthesized with a prescribed degree of stability LQ design technique. The state of the post-fault system is obtained through state-augmented extended Kalman filter (SAEKF), which is a combined technique with state and parameter estimation. In terms of reconfiguration capability, MPC is considered as a favorable active FTC strategy. In addition to MPC framework, the improvement of on-line computational efficiency motivates MPC to be used to perform fault-tolerant flight control. Furthermore, a Laguerre-function based MPC (LF-MPC) is presented to enhance the on-line fault-tolerant capability. The modification is based on a series of Laguerre functions to model the control trajectory with fewer parameters. In consequence, the computation load is reduced, which improves the real-time fault-tolerant capability in the framework of MPC. The FTC capability is further improved for accommodating the performance degradation during the transient period before the control reconfiguration. This approach is inspired by exponentially increasing weighting matrix used in linear quadratic regulator (LQR). Two platforms are used to perform the evaluation of the designed FTC system. A quadrotor UAV, named the Qball-X4, is utilized to test FTC designed with exponentially increasing weighing matrix LQ technique and FDD designed with SAEKF. The evaluation is conducted under the task of trajectory tracking in the presence of loss of control effectiveness (LOE) faults of actuators. The modified MPC is utilized to synthesize an active FTC system to accommodate the elevator stuck fault of a Boeing 747-100/200 benchmark model. The exponentially increasing weighing matrix LQ technique is further implemented in LF-MPC framework to improve the fault-tolerant capability before the control reconfiguration. A time delayed FDD is integrated into the evaluation process to present the effectiveness of the proposed FTC strategies. The designed FTC system is evaluated under the emergency landing task in the event of failure of elevators