Piloted simulation of an F-16 flight control system designed using quantitative feedback theory

International audience

A review is presented of the basis of the Horowitz quantitative feedback theory (QFT). It is shown that the basic assumptions of (QFT) have mathematical justification. However, necessary and sufficient conditions for the solvability of the QFT problem are presently not known. By reposing the QFT problem as an M-matrix design problem, it is possible to develop the desired existence conditions as well as simplify the design of the controllers which solve the QFT problem. Some preliminary results are presented and form the basis of ongoing work into the automation of the QFT design process.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

This paper presents the design of a robust proportional integral and derivative (PID) controller for a first-order lag with pure delay (FOLPD) model using particle swarm optimization (PSO)-enabled automated quantitative feedback theory (QFT). The plant model considered here can be approximated as a first-order system with a non-minimum phase (NMP) zero. Synthesis of controller for the FOLPD model via manual graphical technique involved in the QFT method is always a challenging and cumbersome task, because an NMP system stabilizes by a small gain. In this paper, a proposal is being presented to automate the loop-shaping phase in the QFT design method to synthesize a robust controller that can undertake the exact amount of plant uncertainty even in the presence of larger uncertainties than those assumed initially and can ensure a proper trade-off between robust stability and tracking performance specifications over the entire range of design frequencies. In this paper,s the PSO technique has been employed to tune the controller automatically,which can significantly reduce the computational effort compared with manual graphical techniques. It has also been demonstrated that this methodology not only automates loop shaping but also improves design quality and, most usefully, improves performance with optimally tuned PID controller in quantitative manner.

Performance robustness has been an important issue in control and it has been increasingly recognised as an area of significance in many design applications. This issue has particular importance among the engineering community because it offers a solution to control system design problems for plant and processes that cannot be described by a single linear time-invariant model. The aim of this paper is to describe one of the methods available for robust control system design called Quantitative Feedback Theory (QFT) and discuss its application to engineering problems. QFT is a powerful design technique based on multi degree-of-freedom use of feedback. The basic building block of a multi-degree-of-freedom system is a two-degree-of-freedom system in which two controllers are present, one inside the feedback loop, used to reduce closed-loop uncertainty, and the other prior to the loop, used to shape the input in order to achieve the required output. To show the power of the method, the application to an aero engine described by a non-linear model, linearised at five operating conditions, is discussed. A comparison of the QFT method is also made with the H-infinity method. It is shown that the H-infinity formulation of the QFT problem can produce a more conservative solution than the QFT method.

This paper presents quantitative feedback theory (QFT) based control and tracking of ball and beam system. QFT is a frequency domain approach using Nichols chart. This technique achieves desired robust design over a specified range of plant uncertainty. QFT is used for the design of robust controllers for the plant with uncertainties in the parameters, input/output disturbances and noises. Ball and beam system with parametric uncertainty is defined in state space form using QFT control toolbox and templates are generated. Then, based upon the performance and tracking specifications, bounds are determined. After determining the bounds, controller and prefilter are designed which fulfill the necessary control and tracking requirements. Additionally, a first order sliding mode controller (SMC) is intended for the comparison purpose.

Automated synthesis of multivariable QFT controller using interval constraint satisfaction technique

Landing is a challenging flight phase for automatic control of fixed-wing aircraft. For unmanned air vehicles in particular, it is imperative that model uncertainty be considered in the control synthesis. These vehicles tend to have limited sensors and instrumentation yet must achieve sufficient performance in the presence of modeling uncertainties and exogenous inputs such as turbulence. Quantitative feedback theory has been reported in the literature for design of automatic landing control laws, but none of these controllers has been flight-tested. In this paper, quantitative feedback theory is employed to synthesize robust discrete-time controllers for automatic landing of an unmanned air vehicle. A low-cost flight vehicle with standard aileron, rudder, elevator, and throttle controls is used. Dynamic simulation is conducted using uncertain aircraft models and sensor noise profiles derived from flight hardware. Controllers are initially synthesized in deterministic simulations. Control validation is performed using a Monte Carlo analysis of stochastic simulations. Sources of uncertainty considered are sensor noise, model uncertainty, and static winds. Landing-phase simulations presented in this paper indicate a routinely high probability of a successful landing in relatively calm wind conditions. The flight-testing process is discussed, and time histories from two automatic landings are presented. Dynamic responses in flight test are found to be similar to the simulation, but a significant amount of control redesign is still required to achieve adequate experimental performance. The methodology is judged to be a promising candidate for an automatic landing controller for unmanned air vehicles.

Self-repairing control for UAVs via quantitative feedback theory and quantum control techniques

An online delay estimation algorithm is adopted for the adaptive Smith predictor in the networked control systems (NCS) design under the significant delay time. However, the Smith predictor is sensitive to parameter uncertainties within the system model and time delay. The quantitative feedback theory (QFT) controller is suitable to cope with parameter uncertainties of the Smith predictor in this paper; moreover, a template expressed in phase and gain for the QFT design has been constructed by considering the bounded variations with the time delay and the external loading. Therefore, the adaptive Smith predictor control scheme with QFT controller provides better tolerance to parameter uncertainties. Both simulation and experimental results on AC servo motor with both the hybrid controller area network (CAN) bus and the Ethernet network have proven the feasibility and improved performance of the proposed NCS.

Loop shaping is the principle step for synthesising the quantitative feedback theory (QFT) based robust controllers. The controller assures performance robustness in the presence of plant uncertainties. This paper explores a template and bounds free approach for the automated synthesis of low order fixed structure QFT controller for a highly uncertain pneumatic servo actuator system. In this work, the loop-shaping problem has been posed as a multi-objective optimisation problem and solved using the multi-objective variant of the genetic algorithm. At the end of the design process, a set of Pareto optimal solutions (POS) are obtained, to aid the decision maker in choosing an ideal solution from the POS, use of level diagrams has been explored. The simulation of the results and time and frequency domain analysis has been carried out using MATLAB and the results obtained clearly unveil that the designed QFT controller offers robust behaviour over a range of plant's parametric uncertainty.

Implementing the QFT Method in the MATLAB Environment

Quantitative feedback theory (QFT) is an engineering design method with strong practical value in the field of robust control and it is aimed at the objects with parameters changes. This paper introduces the basic principles and the design process of QFT, and for parameter uncertainty of the robot joint control, this paper designs the feedback-controller and the pre-filter of the system based on QFT. MATLAB simulation results show that the method is with good robust performance and dynamic performance in the robot joint control, and meets the performance indicators.

Rotorcrafte ightcontrolsystemspresentmanysignie cantdesignchallenges.First,largevariationsintheresponse characteristics of rotorcraft result from the wide range of airspeeds of typical operation (hover to over 100 kn ). Second, theassumption ofvehiclerigidity often employed in the design of e xed-wing e ight controlsystems israrely justie ed in rotorcraftwhererotordegreesof freedom can havea signie cant impact on the system performance and stability. A methodology is proposed for the design of robust rotorcraft e ight control systems utilizing quantitative feedback theory (QFT). QFT is a technique that accounts for variability in the dynamic response of the controlled element in thedesign of robust control systems. It was developed to address a multiple-input/single-output (MISO) design problem,buttheextension ofthistechniquetoaddressmultiple-input/multiple-output (MIMO)systemswas employed to address the e ight control system (FCS) design of a UH-60 Black Hawk Helicopter. This was accomplished by constructing a set of MISO systems mathematically equivalent to the MIMO system. QFT was applied to each member of the set independently. Inherent conservatism in this design technique leads to limitations in its utility. A second approach utilizing the sequential closure of control loops proved an improved method for designing of robust MIMO FCSs. An analysis of the two FCS design methodologies is presented.

Automated Synthesis of Fixed Structure QFT Controller using Interval Constraint Satisfaction Techniques

In this paper we consider, within the framework of the single input/output (SISO) Quantitative Feedback Theory (QFT), the feedback design problem for sampled-data uncertain systems with performance specifications. A new, direct the Z-domain controller design procedure is proposed, which parallels that used in continuous time SISO QFT.