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W2914052597 abstract "This paper describes the maturation of a control allocation technique designed to assist pilots in recovery from pilot-induced oscillations. The control allocation technique to recover from pilot-induced oscillations is designed to enable next-generation high-efficiency aircraft designs. Energy-efficient next-generation aircraft require feedback control strategies that will enable lowering the actuator rate limit requirements for optimal airframe design. A common issue on aircraft with actuator rate limitations is they are susceptible to pilot-induced oscillations caused by the phase lag between the pilot inputs and control surface response. The control allocation technique to recover from pilot-induced oscillations uses real-time optimization for control allocation to eliminate phase lag in the system caused by control surface rate limiting. System impacts of the control allocator were assessed through a piloted simulation evaluation of a nonlinear aircraft model in the NASA Ames Research Centers Vertical Motion Simulator. Results indicate that the control allocation technique to recover from pilot-induced oscillations helps reduce oscillatory behavior introduced by control surface rate limiting, including the pilot-induced oscillation tendencies reported by pilots. Manual flight control system design for fighter aircraft is one of the most demanding problems in automatic control. Fighter aircraft dynamics generally have highly coupled uncertain and nonlinear dynamics. Multivariable control design techniques offer a solution to this problem. Robust Multivariable Flight Control provides the background, theory and examples for full envelope manual flight control system design. It gives a versatile framework for the application of advanced multivariable control theory to aircraft control problems. Two design case studies are presented for the manual flight control of lateral/directional axes of the VISTA-F-16 test vehicle and an F-18 trust vectoring system. They demonstrate the interplay between theory and the physical features of the systems. This document describes the nonlinear aircraft simulation model ADMIRE. It describes the main aircraft model, the flight control system, actuators, the sensor models and the uncertainty parameters with respective limits. This document also contains a description on how to properly install and run the model. The ADMIRE describes a generic small single seated, single engine fighter aircraft with a delta-canard configuration, implemented in MATLAB®/Simulink® Release 13. The model envelope is up to Mach 1.2 and 6000m altitude. The model is augmented with a longitudinal flight control system (FCS) that controls the pitch rate at low speed and the load factor at higher speeds, and a lateral controller that controls the wind vector roll rate and the angle of sideslip. The longitudinal FCS also contains a very rudimentary speed controller. The model has thrust vectoring capability, although this is not used in the present FCS. For the purpose of the robustness analysis, the model is extended with the possibility to change some predefined uncertainty parameters within prescribed limits. The uncertainty parameters consist of configuration parameter-, aerodynamic-, actuator- and sensor (air data)-uncertainties. The model can be trimmed and linearized within the entire model envelope. This paper proposes an on-line sliding mode control allocation scheme for fault tolerant control. The effectiveness level of the actuators is used by the control allocation scheme to redistribute the control signals to the remaining actuators when a fault or failure occurs. The paper provides an analysis of the sliding mode control allocation scheme and determines the nonlinear gain required to maintain sliding. The on-line sliding mode control allocation scheme shows that faults and even certain total actuator failures can be handled directly without reconfiguring the controller. The simulation results show good performance when tested on different fault and failure scenarios. This paper presents an application of the inversion theory to the design of nonlinear control systems for simultaneous lateral and longitudinal maneuvers of aircraft. First, a control law for the inner loop is derived for the independent control of the angular velocity components of the aircraft along roll, pitch, and yaw axes using aileron, elevator, and rudder. Then by a judicious choice of angular velocity command signals, independent trajectory control of the sets of output variables (angle of attack, roll, and sideslip angles), (roll rate, angle of attack, and yaw angle), or (pitch, roll, and yaw angles) is accomplished. These angular velocity command signals are generated in the outer loops using state feedback and the reference angle of attack, pitch, yaw, and roll angle trajectories. Simulation results are presented to show that in the closed-loop system, various lateral and longitudinal maneuvers can be performed in spite of the presence of uncertainty in the stability derivatives. Methods for allocating redundant controls for systems with four or more objectives are studied. Previous research into aircraft control allocation has focused on allocating control effectors to provide commands for three rotational degrees of freedom. Redundant control systems have the capability to allocate commands for a larger number of objectives. For aircraft, direct force commands can be applied in addition to moment commands. When controls are limited, constraints must be placed on the objectives which can be achieved. Methods for meeting commands in the entire set of of achievable objectives have been developed. The Bisecting Edge Search Algorithm has been presented as a computationally efficient method for allocating controls in the three objective problem. Linear programming techniques are also frequently presented. This research focuses on an effort to extend the Bisecting Edge Search Algorithm to handle higher numbers of objectives. A recursive algorithm for allocating controls for four or more objectives is proposed. The recursive algorithm is designed to be similar to the three objective allocator and to require computational effort which scales linearly with the controls. The control allocation problem can be formulated as a linear program. Some background on linear programming is presented. Methods based on five formulations are presented. The recursive allocator and linear programming solutions are implemented. Numerical results illustrate how the average and worst case performance scales with the problem size. The recursive allocator is found to scale linearly with the number of controls. As the number of objectives increases, the computational time grows much faster. The linear programmingsolutions are also seen to scale linearly in the controls for problems with many more controls than objectives. In online applications, computational resources are limited. Even if an allocator performs well in the average case, there still may not be sufficient time to find the worst case solution. If the optimal solution cannot be guaranteed within the available time, some method for early termination should be provided. Estimation of solutions from current information in the allocators is discussed. For the recursive implementation, this estimation is seen to provide nearly optimal performance. This paper proposes a framework for modeling and controlling systems described by interdependent physical laws, logic rules, and operating constraints, denoted as mixed logical dynamical (MLD) systems. These are described by linear dynamic equations subject to linear inequalities involving real and integer variables. MLD systems include linear hybrid systems, finite state machines, some classes of discrete event systems, constrained linear systems, and nonlinear systems which can be approximated by piecewise linear functions. A predictive control scheme is proposed which is able to stabilize MLD systems on desired reference trajectories while fulfilling operating constraints, and possibly take into account previous qualitative knowledge in the form of heuristic rules. Due to the presence of integer variables, the resulting on-line optimization procedures are solved through mixed integer quadratic programming (MIQP), for which efficient solvers have been recently developed. Some examples and a simulation case study on a complex gas supply system are reported. In this brief, we propose a control allocation method for a particular class of uncertain over-actuated affine nonlinear systems, with unstable internal dynamics. Dynamic inversion technique is used for the commanded output to track a smooth output reference trajectory. The corresponding control allocation law has to guarantee the boundedness of the states, including the internal dynamics, and satisfy control constraints. The proposed method is based on a Lyapunov design approach with finite-time convergence to a given invariant set. The derived control allocation is in the form of a dynamic update law which, together with a sliding mode control law, guarantees boundedness of the output tracking error as well as of the internal dynamics. The effectiveness of the control law is tested on a numerical model of the non-minimum phase planar vertical take-off and landing (PVTOL) system. The performance and computational requirements of optimization methods for control allocation are evaluated. Two control allocation problems are formulated: a direct allocation method that preserves the directionality of the moment and a mixed optimization method that minimizes the error between the desired and the achieved moments as well as the control effort. The constrained optimization problems are transformed into linear programs so that they can be solved using well-tried linear programming techniques such as the simplex algorithm. A variety of techniques that can be applied for the solution of the control allocation problem in order to accelerate computations are discussed. Performance and computational requirements are evaluated using aircraft models with different numbers of actuators and with different properties. In addition to the two optimization methods, three algorithms with low computational requirements are also implemented for comparison: a redistributed pseudoinverse technique, a quadratic programming algorithm, and a fixed-point method. The major conclusion is that constrained optimization can be performed with computational requirements that fall within an order of magnitude of those of simpler methods. The performance gains of optimization methods, measured in terms of the error between the desired and achieved moments, are found to be small on the average but sometimes significant. A variety of issues that affect the implementation of the various algorithms in a flight-control system are discussed. Next-generation aircraft with a large number of actuators will require advanced control allocation methods to compute the actuator commands needed to follow desired trajectories while respecting system constraints. Previously, algorithms were proposed to minimize the ℓ 1 or ℓ 2 norms of the tracking error and of the actuator deflections. This paper discusses the alternative choice of the ℓ 1 norm, or the ℓ ∞ norm. Minimization of the control effort translates into the minimization of the maximum actuator deflection (minmax optimization). This paper shows how the problem can be solved effectively by converting it into a linear program and solving it using a simplex algorithm. Properties of the algorithm are also investigated through examples. In particular, the minmax criterion results in a type of load balancing, where the load is the desired command and the algorithm balances this load among various actuators. The solution using the ℓ 1 norm also results in better robustness to failures and lower sensitivity to nonlinearities in illustrative examples. This paper also discusses the extension of the results to a normalized ℓ 1 norm, where the norm of the actuator deflections are scaled by the actuator limits. Minimization of the control effort then translates into the minimization of the maximum actuator deflection as a percentage of its range of motion. Limits on the motion and on the rate of motion of the actuators driving the control surfaces of aircraft significantly affect the performance of flight control systems. After a failure or damage to the aircraft, the constraints become even more restrictive because of the loss of control power. There is also often an increase in cross couplings between the axes and, for a period of time, a significant uncertainty about the moments generated by the individual control surfaces. A model reference adaptive control algorithm is considered for flight control reconfiguration. The tracking performance of the algorithm deteriorates drastically for large maneuvers if actuator saturation is not accounted for. Four methods of command limiting are proposed to handle the problem, which are based on a scaling of the control inputs, a relaxation of the control requirements, a scaling of the reference inputs, and a least-squares approximation of the commanded accelerations. Simulations demonstrate the effectiveness of the algorithms in the reconfigurable flight control application. Even the simplest method is found to considerably improve the responses, and, surprisingly, the performance of all four methods is similar despite their widely different concepts and complexity levels. In some cases, degraded transient responses are observed, which are attributed to the uncertainty in the aircraft parameters following a failure. A novel method is presented for the solution of the control allocation problem where the control variable rates or moments are nonlinear functions of control position. Historically, control allocation has been performed under the assumption that a linear relationship exists between the control induced moments and the control effector displacements. However, aerodynamic databases are discrete valued and almost always stored in multidimensional lookup tables, where it is assumed that the data are connected by piecewise linear functions. The approach that is presented utilizes this piecewise linear assumption for the control effector moment data. This assumption allows the control allocation problem to be cast as a piecewise linear program that can account for nonlinearities in the moment/effector relationships, as well as to enforce position constraints on the effectors. The piecewise linear program is then recast as a mixed-integer linear program. It is shown that this formulation accurately solves the control allocation problem when compared to the aerodynamic model. It is shown that the control effector commands for a reentry vehicle by the use of the piecewise linear control allocation method are markedly improved when compared to the performance of more traditional control allocation approaches that use a linear relationship between the control moments and the effectors. The technique is also applied to determine those flight conditions (angle of attack and Mach number) at which the reentry vehicle can be trimmed for the purpose of providing constraint estimates to trajectory reshaping algorithms. Control allocation as it pertains to aerospace vehicles, describes the way in which control surfaces on the outside of an aircraft are deflected when the pilot moves the control stick inside the cockpit. Previously, control allocation was performed by a series of cables and push rods, which connected the 3 classical control surfaces (ailerons, elevators, and rudder), to the 3 cockpit controls (longitudinal stick, lateral stick, and rudder pedals). In modern tactical aircraft however, it is not uncommon to find as many as 10 or more control surfaces which, instead of being moved by mechanical linkages, are connected together by complex electrical and/or hydraulic circuits. Because of the large number of effectors, there can no longer be a one-to-one correspondence between surface deflections on the outside of the cockpit to pilot controls on the inside. In addition, these exterior control surfaces have limits which restrict the distance that they can move as well as the speed at at which they can move. The purpose of Constrained Control Allocation is to deflect the numerous control surfaces in response to pilot commands in the most efficient combinations, while keeping in mind that they can only move so far and so fast. The implementation issues of Constrained Control Allocation techniques are discussed, and an aerodynamic model of a highly modified F-15 aircraft is used to demonstrate the various aspects of Constrained Control Allocation. Control allocation is examined for linear time-invariant problems that have more controls than degrees of freedom. The controls are part of a physical system and are subject to limits on their maximum positions. A control allocation scheme commands control deflections in response to some desired output. The ability of a control allocation scheme to produce the desired output without violating the physical position constraints is used to compare allocation schemes. Methods are developed for computing the range of output for which a given scheme will allocate admissible controls. This range of output is expressed as a volume in the n- dimensional output space. The allocation schemes which are detailed include traditional allocation methods such as Generalized Inverse solutions as well as more recently developed methods such as Daisy Chaining, Cascading Generalized Inverses,Null-Space Intersection methods, and Direct Allocation. Non-linear time-varying problems are analyzed and a method of control allocation is developed that uses Direct Allocation applied to locally linear problems to allocate the controls. This method allocates controls that do not violate the position limits or the rate limits for all the desired outputs that the controls are capable of producing. The errors produced by the non-linearities are examined and compared with the errors produced by globally linear methods. The ability to use the redundancy of the controls to optimize some function of the controls is explored and detailed. Additionally, a method to reconfigure the controls in the event of a control failure is described and examined. Detailed examples are included throughout, primarily applying the control allocation methods to an F-18 fighter with seven independent moment generators controlling three independent moments and the F-18 High Angle of Attack Research Vehicle (HARV) with ten independent moment generators. This paper discusses the methodology and algorithms used for control allocation on the Short Take-Off and Vertical Landing (STOVL) variant of Lockheed Martins Joint Strike Fighter (the X-35B). Control allocation for STOVL vehicles is particularly challenging due to the stringent performance requirements for safe, low-speed flight and the combined use of both aerodynamic and propulsive control effectors. The numerous control effectors' capabilities are a function of many variables including flight condition and engine power setting. To meet these challenging requirements, the X-35B control laws used a control allocation methodology that included a cascaded generalized inverse algorithm, and an onboard model of the aircraft aerodynamic and propulsive characteristics and control effectiveness. To improve the safety and reliability of the vehicle, the methodology needed the ability to handle effector failure scenarios and to avoid structural coupling. The topics covered include: 1) handling of redundant effectors; 2) effector redistribution in the presence of effector limits (position limits and rate limits); 3) effector redistribution in the presence of gain limiting; 4) failure accommodation; and 5) Onboard Model characteristics. This paper describes the results of recent research into the problem of allocating several flight control effectors to generate moments acting on a flight vehicle. The results focus on the use of various generalized inverse solutions and a hybrid solution utilizing daisy chaining. In this analysis, the number of controls is greater than the number of moments being controlled, and the ranges of the controls are constrained to certain limits. The control effectors are assumed to be individually linear in their effects throughout their ranges of motion and independent of one another in their effects. A standard of comparison is developed based on the volume of moments or moment coefficients a given method can yield using admissible control deflections. Details of the calculation of the various volumes are presented. Results are presented for a sample problem involving 10 flight control effectors. The effectivenesses of the various allocation schemes are contrasted during an aggressive roll about the velocity vector at low dynamic pressure. The performance of three specially derived generalized inverses, a daisy-chaining solution, and direct control allocation are compared. This paper presents a flight controller for a tailless aircraft with a large suite of conventional and unconventional control effectors. The controller structure is modular to take advantages of individual technologies from the areas of plant parameter estimation, control allocation, and robust feedback control. Linear models generated off-line provide plant parameter estimates for control. Dynamic inversion control provides direct satisfaction of flying qualities requirements in the presence of uncertainties. The focus of this paper, however, is control allocation. Control allocation is posed as constrained parameter optimization to minimize an objective that is a function of the control surface deflections. The control law is decomposed into a sequence of prioritized partitions, and additional optimization variables scale the control partitions to provide optimal command limiting which prevent actuator saturation. Analysis shows that appropriate prioritization of dynamic inversion control laws provides graceful command and loop response degradation for unachievable commands. Preliminary simulation results show that command variable response remains decoupled for achievable commands while other command limiting methods may result in unacceptable coupled response. A modular flight control system is developed for a tailless fighter aircraft with innovative control effectors. Dynamic inversion control synthesis is used to develop a full envelope flight control law. Minor dynamic inversion command variable revisions are required due to the tailless nature of the configuration studied to achieve nominal stability and performance. Structured singular value and simulation analysis shows that robust stability is achieved and robust performance is slightly deficient due to modeling errors. A multi-branch linear programming-based method is developed and used for allocation of redundant limited control effectors. A demonstration that feedback control of systems with redundant controls can be reduced to feedback control of systems without redundant controls and control allocation is presented. It is shown that control allocation can introduce unstable zero dynamics into the system, which is important if input/output inversion control techniques are utilized. The daisy chain control allocation technique for systems with redundant groups of controls is also presented. Sufficient conditions are given to ensure that the daisy chain control allocation does not introduce unstable zero dynamics into the system. Aircraft flight control examples are given to demonstrate the derived results. Closed-loop stability for dynamic inversion controllers depends on the stability of the zero dynamics. The zero dynamics, however, depend on a generally nonlinear control allocation function that optimally distributes redundant controls. Therefore, closed-loop stability depends on the control allocation function. A sufficient condition is provided for globally asymptotically stable zero dynamics with a class of admissible nonlinear control allocation functions. It is shown that many common control allocation functions belong to the class of functions that are covered by the aforementioned zero dynamics stability condition. Aircraft flight control examples are given to demonstrate the utility of the results. An algorithm is presented for the identification of aircraft stability derivatives and distributed control derivatives, in real time. Feedback control correlates the effectors' displacement with the aerodynamic angles, while the most commonly used control allocation algorithms correlate the effectors. The result is that valid derivative estimation is not possible. This paper addresses the effector identification problem by including decorrelating excitation into the control allocation cost function while still satisfying the desired control moment, and therefore does not introduce any residual perturbations into the motion variables. A two-step identification algorithm is used where the stability derivative and a generalized control derivative are identified in the first step. Results are shown for a stability axis roll maneuver with the stability and control derivatives being identified for five differential lateral directional effectors. Two methods are discussed for design of reconfigurable flight-control systems when one or more control surfaces are jammed. The first is a robust servomechanism control approach, which is a generalization of the classical proportional-plus-integral control to multi-input/multi-output systems. The second proposed method is a control allocation approach based on a quadratic programming formulation. The formulation is formally analyzed, and a globally convergent fixed-point iteration algorithm is used to make onboard implementation of this method feasible. The two methods are applied to reconfigurable entry flight control design for the X-33 vehicle. Nonlinear six-degree-of-freedom simulations demonstrate simultaneous tracking of angle-of-attack and roll-angle commands during control surface failures. The control-allocation method appears to offer more uniform and good performance at the expense of modestly higher computation requirement. Give a consistent set of m linear equations in n unknown variables, a minimum-effort solution is defined to be a solution of that set of equations whose maximum component's magnitude is the smallest possible. An algorithmic procedure for obtaining a minimum-effort solution is developed. Its development is based on the duality principle from functional analysis. Possible applications of such an algorithm for typical digital control problems is presented in the introductory section. In such situations, it is frequently desirable to effect a given control task while using minimum control amplitude. A column exchange algorithm is presented for the determination of a minimum ℓ ∞ solution to a system of consistent linear equations. The algorithm is based on first solving the associated dual problem as specified by a well-known theorem from functional analysis and then generating the required solution by means of an alignment criterion. The procedure has been shown to have a rapid speed of convergence on all examples treated to date. Herein is described an algorithmic procedure for determining a minimum l ∞ -norm solution to the system of consistent linear equations [ Ax = y , ] where A is a m × n matrix of rank m , y is a known m × 1 vector and x is an unknown n × 1 vector. The algorithm's development is based on some fundamental concepts from functional analysis. Its computational efficiency is shown to easily exceed that of the linear programming formulization of the same problem. This paper presents a fault-tolerant adaptive control allocation scheme for overactuated systems subject to loss of effectiveness actuator faults. The main idea is to use an ‘ad hoc’ online parameters estimator, coupled with a control allocation algorithm, in order to perform online control reconfiguration whenever necessary. Time-windowed and recursive versions of the algorithm are proposed for nonlinear discrete-time systems and their properties analyzed. Two final examples have been considered to show the effectiveness of the proposed scheme. The first considers a simple linear system with redundant actuators and it is mainly used to exemplify the main properties and potentialities of the scheme. In the second, a realistic marine vessel scenario under propeller and thruster faults is treated in full details. The problem of disturbance rejection and control allocation with an uncertain control effectiveness matrix is investigated in this paper for a flight control system. An ℋ 2 / ℋ ∞ feedback controller is designed to produce the three axis moments and simultaneously suppress disturbance noise. A feedforward controller is used to track the reference signals. Under the condition of uncertainty included in the control effectiveness matrix, a robust least-squares scheme is employed to deal with the problem of distributing the three axis moments to the corresponding control surfaces. The proposed robust least-squares control allocation is studied for both unstructured and structured uncertainties. To illustrate the effectiveness of the proposed scheme, a simulation for an experimental satellite launch vehicle model is conducted. Comparisons of robust least-squares control allocation and pseudoinverse control allocation are presented. Results show that a disturbance is rejected and robust least-squares control allocation is effectively robust to uncertain control effectiveness matrix. A quaternion-based attitude control system is developed for the X-33 in the asce" @default.
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