Control Allocation Problem Research Articles

Joint Network Control and Resource Allocation for Space-Terrestrial Integrated Network Through Hierarchal Deep Actor-Critic Reinforcement Learning

Conventional approaches to network control and resource allocation by allocating dedicated spectrum resources and separate infrastructure for massive Internet of Things (IoT) network are cost-inefficient. The concept of space-terrestrial integrated network (STIN), which is one of the enabling technologies and architectures for future 6 G wireless networks, can provide a solution to the network control and resource allocation problems for massive IoTs. In this paper, a novel STIN based network control and resource allocation problem is proposed for massive IoTs and solved through state of the art hierarchical deep actor-critic networks (H-DAC). The massive IoT networks spread over urban vicinity where cooperation can be possible. This is leveraged to negotiate a joint policy for per unit spectrum which the IoT networks are willing to pay. Deep actor-critic based reinforcement learning (RL) is used in this paper to solve the joint network control and resource allocation problem which is modeled as a utility maximization problem. The RL based algorithms solve the problem of cost per unit spectrum for the federated cloud of IoT networks and the data rate assigned to each IoT network and IoT devices. The algorithm also decides whether to transmit either through the space network or the terrestrial network. We validate performance of our proposed H-DAC scheme by comparing it with results of variants of the actor-critic based RL. We show that through proper system state formulation and reward design, the proposed H-DAC scheme outperforms the reference schemes with different network parameters and metrics.

Adaptive Saturated Fault-Tolerant Control for Spacecraft Rendezvous With Redundancy Thrusters

This article investigates the spacecraft rendezvous control problem in the presence of parametric uncertainties and external disturbances. Redundancy thrusters subject to undesirable saturations and faults are also considered. Benefiting from the asymmetry and smoothness of the Gauss error function, we introduce a new saturation function to model each thruster saturation with asymmetric magnitude constraints. By augmenting the six-degree-of-freedom (6DOF) spacecraft relative dynamics with auxiliary dynamic systems, a novel adaptive saturated fault-tolerant control strategy is proposed. In particular, an optimization method is designed to solve the control allocation (CA) problem considering the thruster faults and saturations. In terms of Lyapunov theorem, it is demonstrated that the closed-loop augmented system driven by the proposed control scheme is asymptotically stable. Finally, the simulation results validate the effectiveness of the proposed control scheme.

Project and Control Allocation of a 3 DoF Autonomous Surface Vessel With Aerial Azimuth Propulsion System

To gather hydrological measurements is a difficult task for Autonomous Surface Vessels. It is necessary for precise navigation considering underwater obstacles, shallow and fast water flows, and also mitigate misreadings due to disturbs caused by their propulsion system. To deal with those problems, this paper presents a new topology of an Autonomous Surface Vessel (ASV) based on a catamaran boat with an aerial propulsion system with azimuth control. This set generates an over-actuated 3 Degree of Freedom (DoF) ASV, highly maneuverable and able of operating over the above-mentioned situations. To deal with the high computational cost of the over-actuated control allocation (CA) problem, this paper also proposes a Fast CA (FCA) approach. The FCA breaks the initial nonlinear system into partially-dependent linear subsystems. This approach generates smaller connected systems with overlapping solution spaces, generating fast and robust convergence, especially attractive for embedded control devices. Both proposals, i.e., ASV and FCA, are assessed through mathematical simulations and real scenarios.

Kriging Aerodynamic Modeling and Multi-Objective Control Allocation for Flying Wing UAVs With Morphing Trailing-Edge

Flexible skin and continuous deformable control surface are the basic of adaptive wing technology for future aircraft. This paper presents a continuous morphing trailing-edge and its control allocation method for the flying wing Unmanned Aerial Vehicle (UAV). Firstly, we apply the Kriging method to establish the aerodynamic model of the morphing trailing-edge, with the initial sample points generated by the non-uniform optimal Latin Hypercube Sampling (LHS). Then, based on the Kriging model, the multi-objective control allocation problem is converted into a standard optimization form. To solve such problem, we design a Comprehensive Multi-Objective Particle Swarm Optimization (C-MOPSO) algorithm and an improved Hierarchical MOPSO (H-MOPSO) algorithm, in which multiple optimization objectives are prioritized and hierarchically optimized using the PSO algorithm. As for performance analysis, an attitude angle tracking flight control system is established to validate the effectiveness of our proposed control allocation methods. Simulation results show that both the C-MOPSO and H-MOPSO methods have similar performance in attitude angle tracking, while H-MOPSO achieves better multi-objective allocation performance.

Backstepping Control for a Tandem Rotor UAV Robot with Two 2-DOF Tiltable Coaxial Rotors

The study of a fully actuated multi-rotor UAV robot is very important in the field of infrastructure inspection because it needs a dexterous motion, such as hovering in a special fixed attitude, etc. This paper presents a backstepping control method for a simplified fully actuated model of a tandem-rotor UAV robot with two 2-DOF tiltable coaxial rotors. A MIMO vectorial backstepping approach is adopted here because the input distribution matrix is a square and nonsingular matrix. The two-stage control method based on the Lyapunov second method is presented to stabilize the position and attitude of the whole system. The static control allocation problem is also solved by using a Moore-Penrose pseudo-inverse. Finally, two simulations are demonstrated to verify the performance of the proposed control method, where one is a stabilizing problem in which all the desired position and attitude are to be constant, whereas the other is a trajectory tracking problem in which the desired positions are time-varying while the desired attitudes are to be constant.

Feedback linearization control for a tandem rotor UAV robot equipped with two 2-DOF tiltable coaxial-rotors

In this paper, computed torque control as one method of feedback linearization techniques is considered for an unmanned aerial vehicle (UAV) robot that has two tiltable coaxial rotors so as to realize multifunctional locomotion modes, where each rotor has 2-DOF tilt mechanism. First, a dynamical model of such an UAV robot is derived following the Newton–Euler law. Next, under the assumptions that the anti-torque of the tiltable coaxial rotors is zero and the gyro moment effect of the tiltable coaxial rotors can be ignored, a computed torque controller is derived, because the resultant model of the UAV robot can be simplified to a fully actuated model, which has six motion inputs and six generalized coordinate outputs. In addition, a control allocation problem of the system, in which the control inputs designed using the dynamical model are assigned to all motors for tilting the rotor as well as rotating it, is solvable by using a Moore–Penrose pseudo-inverse, where a coordinate transformation is used to simply the allocation problem. Finally, some simulations are demonstrated to verify the effectiveness of the computed torque control strategy for the robot.

Load-swing suppression control using an omnidirectionally movable multi-rotor in 2D-horizontal plane

In this paper, a method for controlling the swing of loads is proposed by applying forces to a suspended load directly, instead of controlling a suspending machine itself, such as a helicopter and a crane. A special multi-rotor capable of moving in the level surface is developed to control the swing of loads. First, to create a model for controlling the swing of loads, a drone model is used to model a multi-rotor that can generate forces in (x, y)-directions and also produce a torque for yaw-rotation, and has four special coaxial rotors, while a double spherical pendulum is used as a rope model which hangs the multi-rotor. Moreover, in what is called a control allocation problem that determines the number of revolutions of each rotor, assume that the number of revolutions for each rotor consists of one for generating two forces in translational motion, and of one for generating one torque in rotational motion. Then, the number of revolutions for plane movement is first decided at the 1st step, and the number of revolutions for a torque required for yaw motion is next decided from the difference between the torque generated by the translational forces decided at the 1st step and the target torque. Finally, the validity of the proposed load-swing suppression control is verified with simulations.

Low complexity resource allocation algorithms for chunk based OFDMA multi-user networks with max–min fairness

This paper investigates the resource allocation problem in orthogonal frequency division multiple access networks with multiple users. Different from most works that perform resource allocation based on each subcarrier, we consider the subcarriers are grouped into chunks due to simplicity of implementation. The objective is to maximize the users’ minimal transmission rate by adjusting the transmission power allocation and chunk allocation while taking into account several important constraints. The problem is formulated as a mixed integer nonlinear programming problem, which is generally hard to solve. By binary relaxation, the problem is transformed to be a convex problem. By exploiting the special structure of the relaxed problem, a barrier method based fast algorithm is proposed. To achieve binary chunk allocation, a low complexity suboptimal algorithm is proposed further. When the number of chunks is less than the number of users, it is necessary to consider the joint admission control and resource allocation problem. To solve this problem, a fast algorithm based on bisection search and Hungarian method is designed. Simulations are carried out to validate the superior performance of the proposed algorithms in terms of the users’ minimal transmission rate and running time comparing with benchmark algorithms under different conditions.

Minimum Length Scheduling for Full Duplex Time-Critical Wireless Powered Communication Networks

Radio frequency (RF) energy harvesting is key in attaining perpetual lifetime for time-critical wireless powered communication networks (WPCNs) due to full control on energy transfer, far field region, small and low-cost circuitry. In this paper, we propose a novel minimum length scheduling problem to determine the optimal power control, time allocation and schedule subject to data, energy causality and maximum transmit power constraints in a full-duplex WPCN. We first formulate the problem as a mixed integer non-linear programming problem and conjecture that the problem is NP-hard. As a solution strategy, we demonstrate that the power control and time allocation, and the scheduling problems can be solved separately in the optimal solution. For the power control and time allocation problem, we derive the optimal solution by evaluating Karush-Kuhn-Tucker conditions. For the scheduling, we introduce a penalty function allowing reformulation of the problem as a sum penalty minimization problem. Upon derivation of the optimality conditions based on the characteristics of the penalty function, we propose two polynomial-time heuristic algorithms and a reduced-complexity exact algorithm employing smart pruning techniques. Via extensive simulations, we illustrate that the proposed heuristic schemes outperform the schemes for predetermined transmission order of users and achieve close-to-optimal solutions.

Control allocation for a class of morphing aircraft with integer constraints based on Lévy flight

Aiming at tracking control of a class of innovative control effector (ICE) aircraft with distributed arrays of actuators, this paper proposes a control allocation scheme based on the Levy flight. Different from the conventional aircraft control allocation problem, the particular characteristic of actuators makes the actuator control command totally subject to integer constraints. In order to tackle this problem, first, the control allocation problem is described as an integer programming problem with two desired objectives. Then considering the requirement of real-time, a metaheuristic algorithm based on the Levy flight is introduced to tackling this problem. In order to improve the searching efficiency, several targeted and heuristic strategies including variable step length and inherited population initialization according to feedback and so on are designed. Moreover, to prevent the incertitude of the metaheuristic algorithm and ensure the flight stability, a guaranteed control strategy is designed. Finally, a time-varying simulation model is introduced to verifying the effectiveness of the proposed scheme. The contrastive simulation results indicate that the proposed scheme achieves superior tracking performance with appropriate actuator dynamics and computational time, and the improvements for efficiency are active and the parameter settings are reasonable.

Distributed Energy Efficient Channel Allocation in Underlay Multicast D2D Communications

In this paper, we address the optimization of the energy efficiency of underlay multicast device-to-device (D2MD) communications on cellular networks. In particular, we maximize the energy efficiency of both the global network and the individual users considering various fairness factors such as maximum power and minimum rate constraints. For this, we employ a canonical mixed-integer non-linear formulation of the joint power control and resource allocation problem. To cope with its NP-hard nature, we propose a two-stage semi-distributed solution. In the first stage, we find a stable, yet sub-optimal, channel allocation for D2MD groups using a cooperative coalitional game framework that allows co-channel transmission over a set of shared resource blocks and/or transmission over several different channels per D2MD group. In the second stage, a central entity determines the optimal transmission power for each user in the system via fractional programming. We performed extensive simulations to analyze the resulting energy efficiency and attainable transmission rates. The results show that the performance of our semi-distributed approach is very close to that obtained with a pure optimal centralized one.

Hierarchical coordinated control distribution and experimental verification for six-wheeled unmanned ground vehicles

In recent years, the all-wheel independent drive has been the most promising form of drive configuration in unmanned ground vehicles. Considering the difficulties in the control allocation for this kind of vehicle, this paper presents a hierarchical control coordination strategy with three layers to distribute control in real time effectively and accurately. In the upper layer, a hybrid instruction parsing method is proposed, which converts commands of the control panel into driving force requirement and target steering yaw rate, respectively, to prioritize steering command to maintain the trajectory based on the motor properties. Subsequently, a sliding mode controller is employed to convert the target yaw rate into the required yaw moment. The state estimation layer receives data from the sensors and estimates different properties/parameters required in other layers. The lower-level control layer receives commands from the upper layer and allocates respective control to wheels. The control allocation problem has been formulated as an optimization problem and later has been converted into a quadratic programming problem, in which a novel modified barrier method with the combination of reduced equation dimension has been adopted to minimize the computational effort and complexity for implementation on the embedded platform. Computer simulation and field experiment have been conducted, which verify the performance of the proposed strategy.

QoS Enhancement In VoD Systems: Load Management And Replication Policy Optimization Perspectives

AbstractThe amount of online video content is exponentially increasing, which spurs its access demands. Providing optimal quality of service (QoS) for this ever-increasing video data is a challenging task due to the number of QoS constraints. The system resources, the distributed system platform and the transport protocol thus all need to collaborate to guarantee an acceptable level of QoS for the optimal video streaming process. In this paper, we present a comprehensive survey on QoS management for the video-on-demand systems. First, we focus on load management and replication algorithms in content delivery networks and peer-to-peer (P2P) networks for their shortcomings. We also address the problem of admission control and resource allocation with the objectives of congestion avoidance and frame-loss reduction. Besides, we introduce and discuss various replication schemes. For both the client–server architecture and P2P networks, we highlight the need for a specific storage management policy to preserve system reliability and content availability. We also focus on content distribution and streaming protocols scaling. We deduce that content availability is linked to the characteristics and the performance of the streaming protocols. Finally, we create a comparison table that presents the different contributions of the discussed approaches as well as their limitations. We believe that such a comprehensive survey provides useful insights and contributes to the related domains.

Joint resource allocation algorithm based on multi-objective optimization for wireless sensor networks

With the limitations of the network resources and battery energy of wireless sensors, the competition of resources in the process of communication will increase the network energy consumption and reduce the Quality of Service (QoS), resulting in that the application of Multi-Radio Multi-Channel (MRMC) Wireless Sensor Networks (WSNs) face many challenges. In this paper, we concentrate on the resource allocation of joint time slot assignment, channel allocation and power control for MRMC WSNs. Due to the diversity of research objectives and the computational complexity of the non-convex problem, this paper develops a two-stage resource allocation optimization algorithm by analyzing the interdependence of various resources. Specifically, to exchange information with conflict-free transmission among all sensors, a graph coloring algorithm for time slot assignment is designed firstly. Then based on the first stage of this algorithm, the problem of joint power control and channel allocation is studied and formulated as a multi-objective optimization problem to achieve the trade-off between energy efficiency and network capacity maximization under the constraints of link interference and load balance. Multi-objective hybrid particle swarm optimization is introduced to obtain the Pareto optimal solutions. The simulation results show that the proposed algorithm significantly performs better in terms of achieving the trade-off of multi-performance.

Fast Nonsingular Terminal Sliding Mode Flight Control for Multirotor Aerial Vehicles

This article is concerned with the robust flight control of multirotor aerial vehicles (MAVs) subject to bounded force and torque disturbances. The focus is on the entire class of MAVs containing an arbitrary even number (≥4) of fixed (not vectoring) rotors. To deal with this problem, first, a ubiquitous hierarchical control architecture in which the attitude control loop is nested inside the position control loop is adopted and augmented with a control allocator which makes the design of the control laws themselves independent of the rotor arrangement. Specially, the control allocation problem is formulated as a quadratic program that minimizes the thrust commands and accounts for the thrust range and rate bounds. Second, geometric attitude and position control laws are designed separately using a multi-input fast nonsingular terminal sliding mode control (FNTSMC) strategy, which guarantees singularity-free finite-time stability and robustness. The main contributions are, first, the augmentation of the hierarchical control scheme for extending its applicability to any fixed-rotor MAV and, second, a detailed geometric design and finite-time stability analysis of the position and attitude control loops using the FNTSMC theory. The system is evaluated on computational simulations as well as on a hardware-in-the-loop experiment, showing that it is effective, simple to implement and adjust, and reliable to operate in nonlinear regimes as well as under bounded disturbances.

Resource Allocation for D2D-Based V2X Communication With Imperfect CSI

Different from the traditional device-to-device (D2D) communication, vehicle-to-everything (V2X) communication is characterized by the high mobility of vehicles, which introduces the fast variations of the channel state information (CSI) such that the accurate CSI is difficult to be obtained. Unlike other previous work which focuses on the perfect CSI, in this article, we investigate the joint power control and resource allocation problem for the D2D-based V2X communication in a more practical case where the CSI is imperfect. We aim at maximizing the sum ergodic capacity of all vehicular user equipments (VUEs) with imperfect CSI under the minimum signal-to-interference-plus-noise-ratio (SINR) requirements of cellular user equipments (CUEs) and the outage probability constraints of VUEs. We derive an approximate closed-form expression of VUE's ergodic capacity based on Jensen's inequality and propose a simple and efficient lower bound-based power control approach to solve the power control problem with low computational complexity. Based on the optimal power allocation results, we further propose an enhanced Gale-Shapley algorithm to solve the spectrum resource allocation problem, which is tailored to the scenario where each VUE can reuse spectrum resources of multiple CUEs but the resource of each CUE can be shared with one VUE at most and the maximum number of reuse CUEs for each VUE is limited by the maximum transmit power of the VUE. The numerical results verify the tightness of the proposed algorithm and demonstrate the proposed algorithm can enhance the sum ergodic capacity of VUEs efficiently and effectively.

Effective control allocation using hierarchical multi-objective optimization for multi-phase flight

For different flight phases in an overall flight mission, different control and allocation preferences should be pursued considering lift, drag or maneuverability characteristics. The multi-objective flight control allocation problem for a multi-phase flight mission is studied. For an overall flight mission, different flight phases namely climbing, cruise, maneuver and gliding phases are defined. Firstly, a multi-objective control allocation problem considering drag, lift or control energy preference is constructed. Secondly, considering different control preferences at different flight phases, the analytic hierarchical process method is used to construct a comprehensive performance index from different objectives such as lift or drag preferences. The active set based dynamic programming optimization method is used to solve the real-time optimization problem. For the validation, the Innovative Control Effector (ICE) tailless aircraft nonlinear model and the angular acceleration measurements based adaptive Incremental Backstepping (IBKS) are used to construct the validation platform. Finally, an overall flight mission is simulated to demonstrate the efficiency of the proposed multi-phase and multi-objective flight control allocation method. The results show that the comprehensive performance index for different phases, which are determined from the Analytic Hierarchy Process (AHP) method, can suitably satisfy the preference requirements for different flight phases.

Geometric Insight into the Control Allocation Problem for Open-Frame ROVs and Visualisation of Solution

The overall control system for an open-frame Remotely Operated Vehicle (ROV) is typically built from three subsystems: guidance, navigation and control (GNC). The control allocation plays a vital role in the control subsystem. Typically, open-frame underwater vehicles have p actuators (thrusters) for the motion in the horizontal plane, and the control allocation problem, in this case, is very complex and hard to visualise, because the normalised constrained control subset is a p-dimensional unit cube. The aim of this paper is to give a clear picture and a geometric interpretation of the problem and to introduce a hybrid method, based on the integration of a weighted pseudoinverse and the fixed-point method. The main idea of the hybrid method is visualised, and the deep geometric insight is provided using a “virtual” ROV in low-dimensional control spaces, including visualisation of the attainable command set, solution lines, control energy spheres and the role of pseudoinverse and fixed-point iterations. The same concepts are then extended to higher-dimensional cases, for open-frame ROV with four X-shaped (vectored) horizontal thrusters, which is one of the most common thruster configurations for commercial ROVs. The proposed hybrid method has been developed, integrated into a generic fault-tolerant ROV control system and evaluated in virtual and real-world environments off the west coast of Ireland using observation-class ROV Latis and work-class ROV Étaín.

Joint Subcarrier and Power Allocation in NOMA: Optimal and Approximate Algorithms

Non-orthogonal multiple access (NOMA) is a promising technology to increase the spectral efficiency and enable massive connectivity in 5G and future wireless networks. In contrast to orthogonal schemes, such as OFDMA, NOMA multiplexes several users on the same frequency and time resource. Joint subcarrier and power allocation problems (JSPA) in NOMA are NP-hard to solve in general. In this family of problems, we consider the weighted sum-rate (WSR) objective function as it can achieve various tradeoffs between sum-rate performance and user fairness. Because of JSPA's intractability, a common approach in the literature is to solve separately the power control and subcarrier allocation (also known as user selection) problems, therefore achieving sub-optimal result. In this work, we first improve the computational complexity of existing single-carrier power control and user selection schemes. These improved procedures are then used as basic building blocks to design new algorithms, namely Opt-JSPA, $\varepsilon$-JSPA and Grad-JSPA. Opt-JSPA computes an optimal solution with lower complexity than current optimal schemes in the literature. It can be used as a benchmark for optimal WSR performance in simulations. However, its pseudo-polynomial time complexity remains impractical for real-world systems with low latency requirements. To further reduce the complexity, we propose a fully polynomial-time approximation scheme called $\varepsilon$-JSPA. Since, no approximation has been studied in the literature, $\varepsilon$-JSPA stands out by allowing to control a tight trade-off between performance guarantee and complexity. Finally, Grad-JSPA is a heuristic based on gradient descent. Numerical results show that it achieves near-optimal WSR with much lower complexity than existing optimal methods.

Task-Priority Control of Redundant Robotic Systems using Control Lyapunov and Control Barrier Function based Quadratic Programs

This paper presents a novel task-priority control framework for redundant robotic systems based on a hierarchy of control Lyapunov function (CLF) and control barrier function (CBF) based quadratic programs (QPs). The proposed method guarantees strict priority among different groups of tasks such as safety-related, operational and optimization tasks. Moreover, a soft priority measure in the form of penalty parameters can be employed to prioritize tasks at the same priority level. As opposed to kinematic control schemes, the proposed framework is a holistic approach to control of redundant robotic systems, which solves the redundancy resolution, dynamic control and control allocation problems simultaneously. Numerical simulations of a hyper-redundant articulated intervention autonomous underwater vehicle (AIAUV) is presented to validate the proposed framework.