Adaptive Reconfigurable Control Research Articles

Control of spacecraft by using multiple model algorithms

Abstract. An effective scheme for fault detection and identification (FDI) and adaptive reconfigurable control (ARC) for controlling a spacecraft under conditions of actuator failures, saturation of control inputs and large external disturbances is proposed. The obtained results increase significantly the autonomy of the spacecraft. The control scheme, based on a general methodology of multiple models, switching and tuning, is designed in the context of nonlinear spacecraft dynamics. The results obtained by using the proposed scheme are illustrated by using numerical simulation algorithms. Keywords spacecraft control, multi-model approach, nonlinear dynamics of spacecraft, numerical simulation algorithm

A multi-model based adaptive reconfiguration control scheme for an electro-hydraulic position servo system

A multi-model based adaptive reconfiguration control scheme for an electro-hydraulic position servo system

Adaptive finite-time reconfiguration control of unmanned aerial vehicles with a moving leader

This paper investigates adaptive reconfiguration control problem for unmanned aerial vehicle (UAV) helicopter system with a moving leader. Only part of UAV helicopter is informed to have access to the leader’s position. The six degree-of-freedom UAV system is composed of position outer loop and attitude inner loop. In this paper, we introduce a new fully distributed, finite-time reconfiguration controller and the problem of inter-UAVs collision avoidance was solved using potential energy function approach, extending the asymptotical formation controller without collision avoidance from the literature. The distinctive feature of our algorithm from existing works is that the novel formation reconfiguration controller can achieve finite-time, collision avoidance and fully distributed formation only based on relative positions between UAV and its adjacents. It means that the control algorithm is independent of any global information that requires to be calculated by each follower UAV. The system uncertainties are estimated by radial basis function neural network in practical finite time. Simulation results are shown to demonstrate the efficiency of the designed strategy.

Adaptive Flight Control Under Actuation and Sensor Failures and Slow-Engine Dynamics

Current approaches to in-flight failure accommodation are focused either on failures of sensors or control surface actuators. Some results also focus on simultaneous sensor and actuator failures. However, there are apparently no results for the case of engine actuation failures when the engine dynamics are slow and its output is not measurable. Hence, the stability proofs for the case of accommodation of simultaneous actuator, engine, and sensor gain failures are not available. On the other hand, overall system stability is crucial in preventing loss of control under flight-critical failures. In this paper, a stable adaptive approach to simultaneous sensor, actuator, and slow-engine gain failure accommodation in flight control is proposed. The approach is broken into two parts: 1) design of a stable adaptive reconfigurable control scheme for accommodation of actuator and engine gain failures in the case of slow-engine dynamics and nonmeasurable engine output, and 2) stable adaptive control design extending part 1 results to the case of failures of air-data sensors. In the latter case, a suitable state estimator, referred to as the virtual sensor, is proposed, and the stability of the overall system is demonstrated. Finally, a multiple-model approach is proposed to achieve effective control strategy selection under multiple simultaneous sensor, actuator, and engine gain failures, and overall system stability is demonstrated. Computer simulations of the generic transport model are included to illustrate the proposed approach.

Set‐membership estimation‐based adaptive reconfiguration scheme for linear systems with disturbances

SummaryThis paper presents an adaptive reconfiguration control for linear systems with both unknown actuator failures and disturbances. The model uncertainties are included to present additive disturbances which are unknown but bounded. By using set‐membership estimation technique for such a problem, two interval observers are developed to estimate the system states subject to model uncertainties. Based on these state estimates, an adaptive state feedback reconfiguration controller is proposed to compensate actuator failures for achieving system states tracking to those of reference model under the framework of model reference adaptive control approach. The effectiveness of the proposed method is demonstrated through simulation results. Copyright © 2015 John Wiley & Sons, Ltd.

Adaptive reconfigurable control of systems with time‐varying delay against unknown actuator faults

SUMMARYThis work is concerned with the problem of delay‐dependent adaptive fault‐tolerant controller design against unknown actuator faults for linear continuous systems with time‐varying delay. Based on the online estimation of possible faults by discontinuous adaptation law, identification parameters of the adaptive state feedback controller are updated autonomously to compensate the fault effects on the delayed system. For the first time, a convex combination idea and a projection‐type adaptive approach are combined organically to derive the main results. A set of new delay‐dependent reconfigurable stabilization criteria, which guarantee the stability of closed‐loop systems in both fault‐free and faulty cases, is established in terms of linear matrix inequalities. Two numerical instances for linear delayed systems and the linearized model for the lateral motion of Boeing 747 are respectively simulated to illustrate the superiority and the effectiveness of the presented adaptive delay‐dependent results. Copyright © 2013 John Wiley & Sons, Ltd.

Method and Application of Intelligent Reconfigurable Control

In this paper we propose an intelligent adaptive retrofit reconfigurable controller in which the full system states are reliably measurable and available for feedback and diagnosis. The proposed approach retains the existing nominal controller and adds a suitably chosen signal that assures that the desired closed-loop performance is achieved despite the failure. The approach is based on the properties of the system controlled by the nominal controller, and judiciously chosen adaptation signals. Furthermore, some representative methods of intelligent reconfigurable control are introduced, especially intelligent fault diagnosis and intelligent reconfigurable control strategies based on adaptive neural networks and fuzzy reasoning has been discussed in details, which has ensure that the aircraft has retain its flying qualities to a satisfactory level even to the presence of severe control failures. The ability to control aircraft is shown through simulation results with the intelligent reconfigurable controller using the adaptive fuzzy-neural reasoning of the aircraft.

Multiple model adaptive reconfiguration control of state delayed systems

In this paper, a new direct model reference adaptive control framework based on a multiple model approach is proposed. The main objective is to synthesize a fault-tolerant controller which reconfigures the faulty system with state delay. This approach provides stable adaptation for a class of linear systems with state delay in the presence of unknown actuator faults. The proof of stability is based on the Lyapunov−Krasovskii function, while appropriate switching of the multiple models ensures asymptotic tracking for the system outputs, and under the proposed algorithms, the tracking errors of the reconfigured system trend to zero rapidly. Simulation results are presented to illustrate the efficiency of the proposed method.

Multiple Model-based Adaptive Reconfiguration Control for Actuator Fault

In this paper,an active fault tolerant control strategy is developed to compensate for the effect of actuator fault in the presence of non-measurable rate on the actuator second-order dynamics.The proposed control scheme is a combination between multiple model and adaptive reconfiguration control.By means of the designed method,the system output can track that of the reference model asymptotically,and the simulation results have illustrated the effectiveness of the proposed algorithms.

Multiple Model-based Adaptive Reconfiguration Control for Actuator Fault

In this paper, an active fault tolerant control strategy is developed to compensate for the effect of actuator fault in the presence of non-measurable rate on the actuator second-order dynamics. The proposed control scheme is a combination between multiple model and adaptive reconfiguration control. By means of the designed method, the system output can track that of the reference model asymptotically, and the simulation results have illustrated the effectiveness of the proposed algorithms.

Multiple-Model Adaptive Fault-Tolerant Control of a Planetary Lander

In this paper we present an approach to fault-tolerant control based on multiple models, switching, and tuning and its implementation to a hardware-in-the-loop simulation of Delta Clipper Experimental dynamics. The Delta Clipper Experimental is characterized by large control input redundancy, which made it an ideal test bed for evaluation of advanced fault-tolerant and adaptive reconfigurable control strategies. The overall failure detection, identification, and accommodation architecture is an upgraded version of our Fast Online Actuator Reconfiguration Enhancement (FLARE) system. The FLARE approach is based on representing different possible fault and failure scenarios using multiple observers, such that the case of nominal (no-failure) operation is covered along with the loss-of-effectiveness, lock-in-place, and hardover failures of the flight control effectors. Based on a suitably chosen performance criterion, the FLARE system quickly detects single or multiple failures and reconfigures the controls, thus achieving either the original desired performance or graceful performance degradation. In the first stage of the project, the FLARE system was tested on a medium-fidelity simulation of Delta Clipper Experimental dynamics, resulting in excellent performance over a large range of single and multiple faults and failures. Following that, in collaboration with Boeing Phantom Works, the FLARE run-time code was installed at their site and tested on a hardware-in-the-loop test bed consisting of an electromechanical actuator actuating a gimballed engine as a part of a simulation of the Delta Clipper Experimental dynamics. A large number of hardware-in-the-loop simulations were run to cover a dense test-case matrix, including cases of up to 10 simultaneous control effector failures. In all cases FLARE was able to quickly and accurately detect the failures and reconfigure the controls, resulting in excellent overall system performance. In this paper we describe the Delta Clipper Experimental and its dynamics model, along with the multiple models, switching, and tuning based modification of our FLARE system. This is followed by a description ofthe experimental test bed and a discussion of the results obtained through hardware-in-the-loop testing.

Adaptive fault-tolerant control for a class of nonlinear systems with time delay

This article deals with the problem of adaptive fault-tolerant control against unknown actuator faults for a class of nonlinear time delay systems with disturbance. The actuator faults are types of loss of effectiveness. The aim is to find an adaptive fault tolerant controller, such that the system is not only stabilized, but also the state vectors of normal and fault cases with disturbance track that of the normal case without disturbance, which has the designed performance. A new delay-dependent adaptive law is proposed to design the adaptive reconfigurable controller, which is excited to offset the effect of faults and disturbance automatically. Numerical and simulation results are provided to demonstrate the effectiveness of the proposed controller.

A Multi-Layer Autonomous Intelligent Control Architecture for Unmanned Aerial Vehicles

In the past several years there has been a lot of interest in the design of efficient autonomous intelligent controllers for Unmanned Aerial Vehicles (UAV). This is a highly complex and challenging problem since future UAVs will be expected to complete autonomously a wide variety of complex missions, and achieve performance comparable to that of manned vehicles. In this paper a four-layer autonomous intelligent control architecture for UAVs is described, and related issues are discussed. The architecture consists of the following layers: (i) Redundancy Management Layer that consists of the online Failure Detection and Identification (FDI) and robust feedback Adaptive Reconfigurable Controller (ARC); (ii) Autonomous Trajectory Generation (ATG) layer whose role is to fit feasible trajectories through the desired way-points in real time; (iii) Autonomous Path Planning (APP) layer that generates way-points on-line in response to a dynamically changing environment; and (iv) Autonomous Decision Making (ADM) layer whose role is to assess the available control authority after failures, and make missionrelated decisions in near-real time. The main distinguishing feature of the architecture is that its layers are connected through the Achievable Dynamic Performance (ADP) calculation module which results in a system in which all the decisions are made based on the current available resources. Recent extensions of this architecture are also discussed and described in detail. At the end, a discussion is included on the Verification and Validation (V&V) of intelligent and adaptive control systems, and some recent results are presented.

Multiple-Model Adaptive Flight Control Scheme for Accommodation of Actuator Failures

A new parameterization for the modeling of control effector failures in flight control applications and a stable adaptive scheme for failure detection, identification, and accommodation is proposed. The failures include lock in place, hard over, and loss of effectiveness. It is shown that the resulting representation leads naturally to a multiple-model formulation of the corresponding control problem that can be solved using a multiple-model adaptive reconfigurable control approach. We derive stable multiple-model adaptive reconfigurable control algorithms for several cases of increasing complexity, including the most complex case, wherein one of the effectors undergoes lock-in-place or hard-over failure, and all others lose effectiveness. In all cases, the stability of the overall reconfigurable control system is demonstrated using multiple Lyapunov functions, extensions of the Lyapunov method, and the separation between identification and control arising in the context of indirect adaptive control. The approach is illustrated through numerical simulations of the F/A-18 aircraft during carrier landing.

Reconfigurable internal model control based on adaptive lattice filtering

In this paper a reconfigurable adaptive control (RAC) algorithm is presented. The algorithm relies on lattice filtering and is suitable for linear discrete jump systems. The utilized lattice filter identifies recursively the system’s dynamics as an autoregressive model through the transfer function’s reflection coefficients. In addition, the system’s order is monitored through the magnitude of the estimated reflection coefficients. An internal model control (IMC) structure is employed, where the stable-inverse of the filter is utilized in the forward path in cascade with a gain compensator and a lowpass detuning filter. Upon sudden variations of the estimated system’s order the RA controller reconfigures its structure and calculates the command signal based on the IMC principle. Simulation studies are used to investigate the efficacy of the suggested scheme.

Direct adaptive reconfigurable flight control for a tailless advanced fighter aircraft

Direct adaptive reconfigurable flight control for a tailless advanced fighter aircraft