Jovan Boskovic

Stable multiple model adaptive flight control for accommodation of a large class of control effector failures

We propose a new parametrization for the modeling of control effector failures in flight control applications. The failures include float, 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 the most complex case when one of the effectors undergoes float, lock-in-place or hard-over failure, while all others lose effectiveness. The stability of the overall reconfigurable control system is demonstrated using 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-18 aircraft carrier landing manoeuvre.

Robust Tracking Control Design for Spacecraft Under Control Input Saturation

A continuous globally stable tracking control algorithm is presented for spacecraft in the presence of control input saturation, parametric uncertainty, and external disturbances. The proposed control algorithm has the following properties: 1) fast and accurate response in the presence of bounded disturbances and parametric uncertainty; 2) explicit accounting for control input saturation; and 3) computational simplicity and straightforward tuning. A detailed stability analysis of the resulting closed-loop system is included. It is shown that global stability of the overall system is guaranteed with continuous control even in the presence of bounded disturbances and parametric uncertainty. In the proposed controller a single parameter is adjusted dynamically in such a fashion that it is possible to prove that both attitude and angular velocity errors will tend to zero asymptotically. The stability proof is based on a Lyapunov analysis and the properties of the quaternion representation of spacecraft dynamics. One of the main features of the proposed design is that it establishes a straightforward relationship between the magnitudes of the available control inputs and those of the desired trajectories and disturbances even with continuous control. Numerical simulations are included to illustrate the spacecraft performance obtained using the proposed controller.

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.

Comparison of linear, nonlinear and neural-network-based adaptive controllers for a class of fed-batch fermentation processes

Five different control strategies for controlling a complex nonlinear and time-varying fermentation process are compared. The main objective of the paper is to determine conditions under which neural-network-based controllers may prove superior to conventional linear and nonlinear adaptive controllers. Extensive computer simulations were performed under identical conditions using the five methods and were evaluated using the same set of criteria. Neural networks are found to be superior when adequate prior information concerning the dynamics of the process is not available and accuracy and robustness are critical issues.

A multiple model-based reconfigurable flight control system design

We consider a problem of designing a reconfigurable control strategy that achieves acceptable flight performance in the presence of wing battle damage for a tailless advanced fighter aircraft (TAFA). This is a complex practical problem since wing damage results in abrupt variation in the aircraft dynamics. Hence fast and accurate control reconfiguration is vital for assuring aircraft survivability. Our suggested reconfigurable controller is based on the concept of multiple models, switching, and tuning. The overall control system consists of multiple parallel identification models, describing different percentages of wing damage, and corresponding controllers. Based on a suitably chosen switching mechanism, the system quickly finds the model that is closest to the current damage mode, and switches to the corresponding controller achieving excellent overall performance. In addition, the boundedness of the signals in the system is guaranteed if the switching interval is chosen to be sufficiently small. It is shown that the key element is the design of sufficiently robust individual controllers for each of the damage conditions. This has been accomplished using a combination of inverse dynamics and output error feedback control laws. The properties of the overall system are illustrated through simulations using linearized TAFA models provided by Boeing. Simulation results have demonstrated the potential of the multiple model-based approach to solve complex practical reconfigurable control design problems.

Robust Integrated Flight Control Design Under Failures, Damage, and State-Dependent Disturbances

Ar obust integrated fault-tolerant flight control system is presented that accommodates different types of actuator failures and control effector damage, even while rejecting state-dependent disturbances. It is shown that a decentralized failure detection, identification, and reconfiguration system, combined judiciously with adaptive laws for damage estimates and variable structure adjustment laws for disturbance estimates, yields a stable system despite simultaneous presence of failures, damage and disturbances. The proposed system is well suited for the case of first-order actuator dynamics. The properties of the proposed algorithms are illustrated on a medium-fidelity nonlinear simulation of Boeing’s Tailless Advanced Fighter Aircraft.

Adaptive Control Design for Nonaffine Models Arising in Flight Control

Adaptive tracking control algorithms are developed for a class of models encountered in flight control that are nonaffine in the control input. The essence of the approach is to differentiate the function that is nonlinear in the control input and obtain an increased-order system that is linear in the derivative of the control signal and can be used as a new control variable. A systematic procedure is developed and related theoretical and practical issues are discussed. The proposed procedure, referred to as the controller for nonaffine plants, is developed for several cases of nonaffine models with unknown parameters. It is shown that the key aspect in the adaptive control design is the definition of the estimate of the derivative of systems state, which results in a convenient error model from which the adaptive laws can be written in a straightforward manner. The proposed approach is tested using a three-degree-of-freedom simulation of a typical fighter aircraft and is shown to result in a substantially improved system response.

A multiple model predictive scheme for fault-tolerant flight control design

The model predictive control (MPC) strategy is used as a basic control law within the framework of multiple models, switching and tuning to design a reconfigurable flight control system. The controller described can explicitly take into account hard constraints on control inputs, and achieve acceptable flight performance in the presence of control effector freezing. To arrive at an effective reconfigurable control design, a new parametrization of the aircraft model in the presence of control effector freezing is suggested. It turned out that such a parametrization is well suited for use within the MPC framework. The overall multiple model predictive control scheme quickly identifies the nature and time instant of the failure, and carries out automatic reconfiguration of the control law achieving acceptable flight performance. The properties of our reconfigurable controller are evaluated through simulations of an F/A-18A aircraft carrier landing manoeuvre in the presence of critical control effector failures.

A combined direct, indirect, and variable structure method for robust adaptive control

Direct methods and indirect methods for the adaptive control of linear plants with unknown parameters are currently well known. Methods for combining direct and indirect methods have also been suggested. In the paper the latter method is combined with the variable structure method. New adaptive laws are suggested which overcome the principal drawbacks of the variable structure method. While the direct and indirect components of the new method assure stability, the variable structure component improves the transient response of the system. Simulation results are presented to show that the overall system is asymptotically stable in the ideal case, and exhibits robustness and improved performance in the presence of perturbations. >

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.