DCSV-Krylov Exploration for Control Allocation in Multitime Scale Systems

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

To enable autonomous vehicles to operate in cluttered and unpredictable environments with numerous obstacles, such vehicles need a collision avoidance system that can react to and handle sudden changes in the environment. In this paper, we propose an optimization-based reactive collision avoidance system that uses control barrier functions integrated into the control allocation. We demonstrate the effectiveness of our method through numerical simulations with autonomous surface vehicles. The simulated vehicles track their reference waypoints while maintaining safe distances. The proposed method can be readily implemented on vehicles that already use an optimization-based control allocation method.

A hierarchical controller is proposed for achieving high‐accuracy control and the dynamic balance with the presence of multiple faults of actuator, the external disturbance, and the model uncertainties in multicylinder hydraulic press machine (MCHPM). The method divides the controller design into three steps: Virtual fault‐tolerant control law, control allocation algorithm, and actuator control law, which are progressive. First, to precisely compensate the lumped disturbances including the multiple faults of actuator, the external disturbance, and the model uncertainties, a disturbance observer (DO) is developed. By combining the observer with the sliding mode control (SMC), a virtual fault‐tolerant control law is designed. Second, a highly integrated control allocation algorithm for the virtual fault‐tolerant control law is proposed to get the desired driving force, taking into account dynamic control allocation (DCA), multiobjective optimization (MOO) and Analytic Hierarchy Process (AHP) simultaneously. Third, taking the driving force obtained from above control allocation algorithm as the desired target, the control law of each cylinder is calculated. The global stability for the whole system is proved by the Lyapunov theory. Lastly, results of simulation and experiment show that the proposed controller can effectively handle different faults and have more superior control performance.

Design the stability control strategy of four in-wheel-motors drive electric vehicle (EV) based on control allocation. Two kinds of control allocation methods are designed in this paper, one is the quadratic programming (QP), and the other is a simplified optimization method (SOM). Comparing and evaluating the control strategies through the co-simulation with MATLAB software and CARSIM software. The results of the simulation show: both strategies could stabilize the vehicle posture well under critical condition. QP has more accuracy than SOM, and could rebuild the system automatically when the motor fails. But the SOM doesn’t need iteration, could be possible to use in the real vehicle.

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.

Low-rank prior-based Fast-RPCA for clutter filtering and noise suppression in non-contrast ultrasound microvascular imaging

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.

We show how both the tridiagonal and bidiagonal QR algorithms can be restructured so that they become rich in operations that can achieve near-peak performance on a modern processor. The key is a novel, cache-friendly algorithm for applying multiple sets of Givens rotations to the eigenvector/singular vector matrix. This algorithm is then implemented with optimizations that: (1) leverage vector instruction units to increase floating-point throughput, and (2) fuse multiple rotations to decrease the total number of memory operations. We demonstrate the merits of these new QR algorithms for computing the Hermitian eigenvalue decomposition (EVD) and singular value decomposition (SVD) of dense matrices when all eigenvectors/singular vectors are computed. The approach yields vastly improved performance relative to traditional QR algorithms for these problems and is competitive with two commonly used alternatives---Cuppen’s Divide-and-Conquer algorithm and the method of Multiple Relatively Robust Representations---while inheriting the more modest O ( n ) workspace requirements of the original QR algorithms. Since the computations performed by the restructured algorithms remain essentially identical to those performed by the original methods, robust numerical properties are preserved.

Los bosques mixtos tienen cada vez mayor relevancia en el manejo forestal debido a la creciente evidencia de su mayor potencial para suministrar servicios ecosistémicos. El presente estudio analiza a múltiples niveles (desde el nivel árbol a rodal) y escalas (temporales y espaciales), cómo las interacciones de las especies afectan los procesos involucrados en el crecimiento y productividad del pino albar y el pino negral en bosques mixtos en un ambiente mediterráneo. La hipótesis principal fue que las diferencias en los rasgos estructurales entre especies podrían inducir interacciones que tiene efectos en la productividad del rodal, crecimiento de las especies y cambios en los rasgos estructurales a nivel de rodal y especie; modificando así la dinámica de crecimiento comparada con bosques monoespecíficos. La aproximación metodológica y las funciones predictivas desarrolladas en esta tesis pueden ayudar a los investigadores y gestores a comprender mejor las dinámicas de los bosques mixtos, y desarrollar herramienta para la planificación y evaluación diferentes opciones de manejo.

The next generation (NextGen) transport aircraft configurations being investigated as part of the NASA Aeronautics Subsonic Fixed Wing Project have more control surfaces, or control effectors, than existing transport aircraft configurations. Conventional flight control is achieved through two symmetric elevators, two antisymmetric ailerons, and a rudder. The five control surfaces, reduced to three command variables, produce moments along the three main axes of the aircraft and enable the pilot to control the attitude of the aircraft. Next generation aircraft will have additional redundant control effectors to control the three moments, creating a situation where the aircraft is over-actuated and where a simple relationship no longer exists between the required control surface deflections and the desired moments. NextGen flight controllers will incorporate control allocation algorithms to determine the optimal effector commands to attain the desired moments, taking into account the effector limits. Approaches to solving the problem using linear programming and quadratic programming algorithms have been proposed and tested. It is of great interest to understand their relative advantages and disadvantages and how design parameters may affect their properties. In this paper, we investigate the sensitivity of the effector commands with respect to the desired moments and show on some examples the sensitivity of the solutions provided by the linear programming and quadratic programming methods.

Optimal constrained control allocation in marine surface vessels with rudders

Essential climate variables (ECVs) are physical, chemical, or biological parameters supporting the characterization of the climate system. The oceanic ECVs temperature and salinity have been defined for the sea surface (upper 1 mm) and bulk water (upper 2 m). Direct observation and sampling of the sea surface from ships and buoys are limited due to the potential loss of integrity of the sea surface and contamination by the research platform. The design, development, and operation of a research vehicle with autonomous capabilities based on commercially available components are described in this study. The autonomous operation and fully automated sampling enable high-resolution mapping of the sea surface microlayer (SML), i.e., the upper < 1-mm layer and the near-surface layer (<1.2 m). Sampling the SML is based on a rotating glass disk sampler. The autonomous surface vehicle HALOBATES has been equipped with multiple conductivity–temperature–depth (CTD) sensors in a flow-through system to measure both sea surface and bulk temperature and salinity. The performance of the autopilot under various scenarios is demonstrated. Typical anomalies of ECVs within the SML and near-surface layer are evident and forced by both atmospheric and oceanic processes. Data analyses reveal HALOBATES’ ability to resolve thermohaline structure on different spatial and temporal scales, for example, small-scale freshwater lenses and thermohaline changes across oceanic fronts. The integration of an acoustic Doppler current profiler supports the interpretation of thermohaline structures. HALOBATES is a platform with cutting-edge technology for understanding climate-related processes between the ocean and atmosphere and providing high-resolution sea surface data for validating satellite products of ECVs. Significance Statement An autonomous surface vehicle was developed to address the challenges in the in situ measurement of essential climate variables at the sea surface (upper 1 mm) and near-surface layer (upper 1 m) of the ocean. Autonomous operation for up to 12 h and fully automated sampling enable high-resolution mapping of the sea surface and near-surface layers. The ability of the vehicle to resolve thermohaline structures at various spatial and temporal scales is demonstrated, providing valuable data on freshwater fluxes (evaporation minus precipitation) between the ocean and the atmosphere. This cutting-edge technology enhances the understanding of climate-related processes, offering crucial “sea surface data” for validating satellite products of essential climate variables.

The singular value decomposition (svd) is usually calculated by the Golub/Kahan QR algorithm. Two simple alternative methods for the partial or complete svd are described which are suited for special applications. Both of these can be implemented easily by the user, even on low-capacity microcomputers. Various properties and limitations of these two methods are discussed.

Nowadays, underwater vehicles (UVs) are applied to various tasks such as carrying objects or maintenance of underwater structures. To carry out well these tasks, UVs should keep the position and orientation in the water to perform the specified tasks. However, the systems used in underwater operations are always under the influence of disturbance such as ocean currents and model uncertainties. In this paper, the robust station-keeping (SK) control algorithm based on a sliding mode control (SMC) theory is designed to guarantee stability and better performance of a hovering over-actuated autonomous underwater vehicle (HAUV) despite the existence of model uncertainties and ocean current disturbance in the horizontal plane (HP). Using the Lyapunov theorem, the stability of the proposed controller is demonstrated. Besides, an optimal allocation control (AC) algorithm is also designed to keep the linear position and Euler angles of the HAUV in the presence of model uncertainties as well as ocean currents and to minimize the energy consumption of the system. Finally, a series of simulations and experiments for the HAUV system is conducted to demonstrate the superior performance of the proposed method.

In this paper, a unified algebraic transformation approach is presented for designing parallel recursive and adaptive digital filters and singular value decomposition (SVD) algorithms. The approach is based on the explorations of some algebraic properties of the target algorithms' representations. Several typical modern digital signal processing examples are presented to illustrate the applications of the technique. They include the cascaded orthogonal recursive digital filter, the Givens rotation-based adaptive inverse QR algorithm for channel equalization, and the QR decomposition-based SVD algorithms. All three examples exhibit similar throughput constraints. There exist long feedback loops in the algorithms' signal flow graph representation, and the critical path is proportional to the size of the problem. Applying the proposed algebraic transformation techniques, parallel architectures are obtained for all three examples. For cascade orthogonal recursive filter, retiming transformation and orthogonal matrix decompositions (or pseudo-commutativity) are applied to obtain parallel filter architectures with critical path of five Givens rotations. For adaptive inverse QR algorithm, the commutativity and associativity of the matrix multiplications are applied to obtain parallel architectures with critical path of either four Givens rotations or three Givens rotations plus two multiply-add operations, whichever turns out to be larger. For SVD algorithms, retiming and associativity of the matrix multiplications are applied to derive parallel architectures with critical path of eight Givens rotations. The critical paths of all parallel architectures are independent of the problem size as compared with being proportional to the problem size in the original sequential algorithms. Parallelism is achieved at the expense of slight increase (or the same for the SVD case) in the algorithms' computational complexity.