Stability Properties and Cross Coupling Performance of the Control Allocation Scheme CAPIO

For a modern aerodynamically unstable fighter, like the JAS 39 Gripen, the flight control system typically provides 45/spl deg/ phase margin. Therefore rate limiting of control surfaces, which may cause large phase shifts, is an important issue. Software rate limiters are placed on the control servo commands in order to prevent the hydraulic servos from rate limiting. When a rate limiter is saturated, the phase shift drastically reduces the stability margins of the closed loop and increases the risk for pilot-induced oscillation (PIO). This paper describes a novel method for compensating the phase shift of a rate limiter. In contrast to earlier phase compensation methods, this method uses feedback instead of logic or feedforward. Open loop and closed loop properties of the method are discussed. The method gives a drastic improvement on stability margins and reduces PIO tendencies.

A Constrained Control Allocation and Tuning Scheme for Hybrid Actuators in Spacecraft Attitude Control

In this paper, the problem of control allocation - distribution of control power among redundant control effectors, under a set of constraints - for the inner loop of a re-entry vehicle guidance and control system is studied. Our control allocation scheme extends a previously developed model-predictive algorithm by providing asymptotic tracking of time-varying control input commands. The approach accounts for non-negligible dynamics of the actuators with hard constraints, setting it apart from most existing control allocation schemes, where a static relationship between control surface deflections (actuator outputs) and moments about a three-body axis (plant inputs) is assumed. The approach is readily extended to encompass a variety of linear actuator dynamics without the need for redesign of the overall control allocation scheme, allowing for increased effectiveness of the inner loop in terms of speed of maneuverability. Simulation results, with consideration given toward implementation, are provided for an experimental reusable launch vehicle, and are compared to those of static control allocation schemes.

The development of an inner-loop control law for a reusable launch vehicle is described in this paper. The control design is based upon a dynamic inversion control law developed for a lifting body with six control surfaces. The commanded body rates constitute the input to a dynamic inversion control law that forms the inner-loop control structure. This provides the control system the ability to track roll, pitch, and yaw rates. In addition to these three inner-loop variables, a velocity control loop has been developed. This velocity loop provides limited control over the vehicle’s velocity, utilizing only the drag produced by the control surfaces. As a result, instead of using a typical three-axis control allocator, a four-axis control allocation algorithm was developed (three bodyaxis rotation rates and one linear acceleration). The outputs of the dynamic inversion algorithm, namely the commanded body angular acceleration vector and linear longitudinal acceleration, serve as the inputs to a linear programming based control allocator. A method for improving the perfor

The problem of a significant phase shift in a control loop is posing a lot of challenges to the control design. One of them is definitely the loss of performance with the increased phase shift when using a filter at the system’s output. This paper contains a description of a new FIR weights determination method focused on low-pass filter design. The primary goal of this method is to minimize the phase shift caused by the filter. The filter theoretically fits a defined polynomial to an asymmetric data set. In this case, the nearest neighbour samples are only taken from the past side of the filtered vector of signal samples. This feature allows reducing the value of the phase shift, especially for a low-frequency spectrum. Therefore, the filter can be used directly in the closed-loop control and will minimize the loss of system performance.

substantially from the prescribed or expected characteristics. To accommodate failures, the baseline inner-loop control is assisted by a fault detection and isolation algorithm tasked with monitoring and reporting control surface health. Similarly, the baseline outer-loop guidance is augmented with an adaptive neural network forming an adaptive outer-loop guidance system. These adaptive subsystems have been formulated for, and applied to, a transport aircraft model in a nonlinear, high-fidelity aircraft simulation environment to demonstrate the concept. Included results demonstrate that the fault detection and isolation and adaptive outer-loop functionality consistently improve tracking performance during both piloted maneuvers and autonomous waypoint path following. In some cases, these retrofit subsystems enable tasks to be completed that were not previously feasible. Adaptive enhancement of the robustness of existing non-adaptive systems has been an area of significant interest for many years. Although application in the aircraft community is of great benefit for maximizing performance and safety for unexpected changes in the dynamics due to flight control failures, damage, and adverse environmental conditions, certification of these technologies for safety-critical flight systems is challenging. The approach described here is developed under the constraint of maintaining a retrofit architecture thereby allowing the baseline, previously certified, software to remain. The augmented modules serve to assist systems already in place. This paper discusses the development and application of independent and modular adaptive subsystems that are retrofitted to existing guidance and control architectures. The baseline inner-loop control is assisted by a fault detection and isolation (FDI) algorithm tasked with monitoring and reporting control surface health. Similarly, the baseline outer-loop (BOL) guidance is augmented with an adaptive neural network collectively forming an adaptive outer-loop (AOL) guidance system. In general, an adequate inner-loop feedback control law is necessary to faithfully track guidance commands, and various inner-loop control design techniques can provide the requisite performance. Even in the case of degraded inner-loop tracking capability, the outer-loop guidance system may still meet its objectives by inflating inner-loop commands to compensate for inner-loop tracking errors. Nonetheless, faithful innerloop tracking is desirable to reduce the burden on other subsystems, and this is especially the case for piloted

For the enhancement of survivability and maneuverability, modern aircraft systems have redundant control effectors. Control allocation is a useful method for distributing control signals among the individual effectors. In order to implement a control allocation scheme, the control system is designed using two-step procedures. In the first step, the control law is designed by adaptive backstepping control. The second step is to design the control allocator. A robust control allocation method is presented in this paper, which is motivated from the concept of the worst-case robust approximation approach. By assuming uncertainties in the control effectiveness matrix, the worst-case robust control allocation problem is investigated. The proposed robust control allocation technique is compared with weighted least squares control allocation. In particular, nonlinear simulations demonstrate that the proposed robust control allocation method has satisfactory performance and robustness for the assumed uncertainties in the control effectiveness matrix.

Control allocation is essential for managing MIMO dynamic systems, such as autonomous vehicles and robotics, which operate across varying temporal scales, posing challenges in optimizing control actions. A common approach involves traditional solvers for overdetermined systems---most notably the singular value decomposition (SVD), the least squares QR algorithm (LSQR), and Arnoldi-type methods---, which, while effective for homogeneous temporal scales, struggle to handle mixed dynamics. Their inability to explicitly account for temporal variations and uncertainties often results in suboptimal performance and high computational costs. To address these challenges, this work introduces a novel combination of the Dynamic Confined Space of Velocities (DCSV) method with Krylov subspace exploration, significantly enhancing control allocation in mixed-temporal-scale systems. The DCSV method generates optimized initial estimates, which are iteratively refined using Krylov methods, achieving precise control with reduced computational costs. Comparative analysis against traditional solvers demonstrates that DCSV consistently outperforms these methods, achieving higher accuracy with fewer computational resources, particularly in systems with mixed dynamic scales. Since LSQR is closely related to quadratic programming (QP) and linear programming (LP) through its cost function, and SVD directly computes generalized inverses, their evaluation provides a basis for comparison with conventional control allocation strategies. The proposed method surpasses these approaches by achieving a better balance between computational efficiency and control precision. In addition, the uncertainty principle embedded in the DCSV framework establishes theoretical limits that ensure robustness in the allocation process, supported by a structured sequential exploration process formulated via pseudocode. By optimizing initial estimates through DCSV, fewer refinement iterations are needed, leading to faster and more accurate control allocation, as confirmed by formal analysis. This methodology offers a robust and efficient solution to the challenges posed by mixed temporal scales, with enhanced adaptability for real-world applications in dynamic and computationally constrained environments.

This paper presents a method for solving the station-keeping problem of near-space airship (NSA) at an altitude of 22km above sea level with wind influence. Firstly, according to the system overall configuration, the nonlinear mathematical model of NSA is established. Secondly, considering the steady wind and turbulence influences, the station-keeping control law of NSA longitudinal plane which divided into forward control loop and vertical control loop is designed. The vertical position control loop adopts the dual PID control method. In order to improve attitude response performance and decrease energy consumption of actuators, attitude control allocation strategy between fins and vectored thruster is provided. At last, the simulation experiment proves the validity of the proposed station-keeping control method of NSA.

We study the problem of admission control and resource (subcarriers, power and bit-loading) allocation in a heterogeneous OFDMA wireless network serving both QoS-constrained high-priority users and best-effort users. By clustering subcarriers, efficient algorithms for cluster and power allocation have been proposed. Our strategy maximizes the total network utility of the best-effort users while satisfying the QoS request for maximum number of high-priority users. We approximate best-effort user utility function as a piece-wise linear function and propose a linear programming based cluster allocation algorithm. The feasibility of the resource allocation problem depends on the number of HP users in the network. Since a large number of demanding HP users would render the resource allocation problem infeasible, a joint admission control and resource allocation scheme could be an efficient way of tackling both problems with less overhead. By incorporating admission control, we propose an efficient and optimal joint admission control and resource allocation algorithm.

This paper addresses the difficult problem of fault-tolerant control and closed-loop control allocation for spacecraft attitude control system with actuator failures, actuator saturation, and external disturbances. As a fundamental step, a modified fault diagnosis observer is proposed utilizing the iterative learning methodology to reconstruct the actuator failures in real time. On the basis of the previously reconstructed failure information, a fault-tolerant control scheme incorporating with a parameter adjusting law is presented to enforce the spacecraft attitude control system to reach the real sliding mode surface in finite time. Meanwhile, the singularity of control command will be avoided using the saturation function, and the chattering problem restrained by the adjusting law. Furthermore, with the concept of control allocation, a novel on-line closed-loop constrained optimal fault-tolerant control allocation scheme is employed to distribute the signals synthesized by the baseline controller over the redundant actuators with failures and constraints. The stability of the closed-loop fault-tolerant control allocation process is guaranteed by the theory of the closed-loop discrete-time feedback control system. The key achievement of the proposed systematic strategy is that the whole closed-loop attitude control system can theoretically be guaranteed to be stable. Numerical simulations are carried out to verify the effectiveness and superiority of the developed approach.

This paper presents a new control allocation scheme for advanced coordinated vehicle dynamics control (CVDC) that takes into account vehicle operating condition and tire-surface friction coefficient. Individual tire slip and slip angles are selected as the control variables to resolve the inherent tire force nonlinear constraints. A real-time adaptable accelerated fixed-point (AFP) method is proposed to solve the magnitude and rate constrained quadratic programming control allocation (CA) problem. It could achieve faster convergence rates when control variable saturations occur. The performance of this control allocation approach is evaluated for adverse driving conditions simulated using the CarSimreg vehicle simulation package.

This paper investigates the power control and resource allocation problem in a simultaneously wireless information and power transfer (SWIPT)-based cognitive two-way relay network, in which two secondary users exchange information through a power splitting (PS) energy harvesting (EH) cognitive relay node underlay in a primary network. To enhance the secondary networks’s transmission ability without detriment to the primary network, we formulate an optimization to maximize the minimum transmission rates of the cognitive users by jointly optimizing power allocation at the sources, the time allocation of transmission frames and power splitting at the relay, under the constraint that the transmission power of the cognitive network is set not to exceed the primary user interference threshold to ensure primary work performance. To efficiently solve this problem, a sub-optimal algorithm named the joint power control and resource allocation (JPCRA) scheme is proposed, in which we decouple the non-convex problem into convex problems and use alternative steps in the optimization algorithm to get final solutions. Numerical results reveal that the proposed scheme enhances transmission fairness and outperforms three traditional schemes.

The moments produced by the aerodynamic control effectors on aircraft are typically non-linear functions of displacement with a region of linearity about zero displacement. In this paper we briefly review a novel approach that was developed at AFRL for solving the control allocation problem where the control moments are piecewise linear functions of the control displacement. The advantages of this approach are that it avoids cumbersome, high-order polynomial curve fits of the aerodynamic data and thus avoids solution of a non-linear programming problem. Stability and control derivatives are almost always stored in multi-dimensional look-up tables where it is assumed that the data is piecewise linear. The approach that is presented utilizes this piecewise linear assumption for the control effector moment data and accounts for non-linearities in the moment-effector relationships and position constraints on the effectors as well. The control allocation problem using piecewise linear functions is posed as a mixed-integer linear program (MILP) and solved using a freely available GNU-licensed mixed-integer linear programming code. We demonstrate two applications of the piecewise linear control allocation (PLCA) approach. The first application to be considered is to use the PLCA approach in the inner-loop control law of a re-usable launch vehicle on approach and landing. The second application is constraint estimation for trajectory reshaping and re-targeting. In order to successfully re-target a trajectory when an aircraft has experienced degraded performance due to a failure or damage to the vehicle, the effects of the failure or damage on the lift, drag, and trimmability of the vehicle must be known a priori over the entire flight envelope

This paper presents a two stage low noise chopper amplifier for integrated angular acceleration sensor signal processing circuit which consists of amplifiers, phase-locked loop and automatic gain control. Capacitor feedback amplifier circuit is one of the most suitable for CMOS technology however capacitor amplifier with chopped op-amp has poor low frequency performance because of parasitic resistive coupling between inputs of the op-amp. In presented amplifier modulators are placed outside capacitive feedback which improves noise and gain performance. Proper gain distribution between stages contributes to minimizing output noise and phase shift. Chopper ripple is of no concern because of low pass filters in phase-locked loop in signal processing circuit. Proposed circuit is designed in 0.18 CMOS technology. The chopper amplifier consumes 1.75 mA current. Input referred noise voltage at 15 kHz is 8.2 nV/√Hz, gain of the amplifier is 53 dB while the phase shift is 0.6°.