Trajectory Planning for Emergency Landing of VTOL Fixed-Wing Unmanned Aerial Vehicles

In recent years, inspired by technological progress and the outstanding performance of Unmanned Aerial Vehicles (UAVs) in several local wars, the UAV industry has witnessed explosive development, widely used in communication relay, logistics, surveying and mapping, patrol, surveillance, and other fields. Vertical Take-Off and Landing fixed-wing UAV has both the advantages of vertical take-off and landing of rotorcraft and the advantages of long endurance of fixed-wing UAV, which broadened its application field and is the most popular UAV at present. Recently, fixed-wing UAV failure analysis highlights that cruise engine shutdown is the most common reason for emergency landing, which is also a governing factor for Vertical Take-Off and Landing (VTOL) fixed-wing UAV failures. Nevertheless, the emergency landing trajectory of the latter UAV type after engine shutdown is different from that of the conventional fixed-wing UAVs due to the VTOL power system. Hence, spurred by the requirement of a safe emergency landing trajectory for VTOL fixed-wing UAVs, this paper develops an architecture capable of safe emergency landing for such platforms. The suggested method develops a particle dynamics model of the VTOL UAV and analyzes its aerodynamic characteristics utilizing Computational Fluid Dynamics (CFD) results. The UAV’s trajectory is divided into three parts for enhanced planning. For the guidance stage, the initial position and heading angle are arbitrary. Hence, the Dubins shortest cross-range and the fastest descent trajectory are adopted to steer the UAV above the landing window quickly. The spiral stage comprises a conical and cylindrical part combined with a spiral descent trajectory of variable radius for energy management and landing course alignment. Given the limited energy storage of VTOL power systems, the landing stage exploits an optimal control trajectory problem solved by a Gaussian pseudospectral method, involving trajectory conventional landing planning, unpowered landing, distance optimal landing, and wind-resistant landing. All trajectories meet the dynamics constraints, terminal constraints, and sliding performance constraints and cover both 2-dimensional and 3-dimensional trajectories. A large number of simulation experiments demonstrate that the proposed trajectories manage broad applicability and strong feasibility for VTOL fixed-wing UAVs.

Adaptive neural network control of second-order underactuated systems with prescribed performance constraints

This paper studies the trajectory tracking control problem of second-order underactuated system subject to system uncertainties and prescribed performance constraints. By combining radial basis function neural networks (RBFNNs) with input–output linearization methods, an adaptive neural network-based control approach is proposed and the adaptive laws are given through Lyapunov method and Taylor expansion linearization approach. The main contributions of this paper are that: (1) by introducing weight performance function and transformation function, the states never violate the prescribed performance constraints; (2) the control scheme takes the unknown control gain direction into consideration and the singular problem of control design can be avoided; (3) through rigorously stability analysis, all signal of closed-loop system are proved to be uniformly ultimately bounded. The effectiveness of the proposed control scheme was verified by comparative simulation.

Concerto: Dynamic Processor Scaling for Distributed Data Systems with Replication

A surge of interest in data-intensive computing has led to a drastic increase in the demand for data centers. Given this growing popularity, data centers are becoming a primary contributor to the increased consumption of energy worldwide. To mitigate this problem, this paper revisits DVFS (Dynamic Voltage Frequency Scaling), a well-known technique to reduce the energy usage of processors, from the viewpoint of distributed systems. Distributed data systems typically adopt a replication facility to provide high availability and short latency. In this type of architecture, the replicas are maintained in an asynchronous manner, while the master synchronously operates via user requests. Based on this relaxation constraint of replica, we present a novel DVFS technique called Concerto, which intentionally scales down the frequency of processors operating for the replicas. This mechanism can achieve considerable energy savings without an increase in the user-perceived latency. We implemented Concerto on Redis 6.0.1, a commercial-level distributed key-value store, demonstrating that all associated performance issues were resolved. To prevent a delay in read queries assigned to the replicas, we offload the independent part of the read operation to the fast-running thread. We also empirically demonstrate that the decreased performance of the replica does not cause an increase of the replication lag because the inherent load unbalance between the master and replica hides the increased latency of the replica. Performance evaluations with micro and real-world benchmarks show that Redis saves 32% on average and up to 51% of energy with Concerto under various workloads, with minor performance losses in the replicas. Despite numerous studies of the energy saving in data centers, to the best of our best knowledge, Concerto is the first approach that considers clock-speed scaling at the aggregate level, exploiting heterogeneous performance constraints across data nodes.

Simulation of Constant-Volume Removal Rate Machining of Middle-Convex and Varying Ellipse Piston

One kind of constant-volume removal rate machining method of the middle-convex and varying ellipse piston is proposed in this paper. By analyzing the structure and movement relationship of the middle-convex and varying ellipse piston machine, the NC machining model is built. And, the constant-volume removal rate machining model is also built by superposing the variable rotation satisfying the dynamic performance constraints on the uniform rotation of the spindle of the CNC piston lathe. Then, the instantaneous position parameters of each axis of the CNC piston lathe are obtained and turned into NC code. The functional feasibility of the method finally is verified by simulation machining.

Adaptive Fixed-Time Tracking Consensus Control for Multiagent Nonlinear Pure-Feedback Systems with Performance Constraints

This paper investigates adaptive fixed-time tracking consensus control problems for multiagent nonlinear pure-feedback systems with performance constraints. Compared with existing results of first/second/high-order multiple agent systems, the studied systems have more complex nonlinear dynamics with each agent being modeled as a high-order pure-feedback form. The mean value theorem is introduced to address the problem of nonaffine structure in nonlinear pure-feedback systems. Meanwhile, radial basis function neural networks (RBFNNs) are employed to approximate unknown functions. Furthermore, a constraint variable is used to guarantee that all local tracking errors are within the prescribed boundaries. It is shown that, by utilizing the proposed consensus control protocol, each tracking consensus error can converge into a neighborhood around zero within designed fixed time, the tracking consensus performance can be ensured during the whole process, and all signals in the investigated systems are bounded. Finally, two simulations are performed and the results demonstrate the effectiveness of the proposed control strategy.