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This paper aims to investigate the decentralised strategy to coordinate the reconfiguration of multiple spacecraft.

The system of interest consists of multiple spacecraft with independent subsystem dynamics and local constraints, but is linked through their coupling constraints. The proposed method decomposes the centralised problem into smaller subproblems. It minimises the fuel consumption of multiple spacecraft performing a reconfiguration manoeuvre through an iterative computation. In particular, each agent optimises its individual cost function using the most recently available local solution for the other agents.

The simulation scenarios include spacecraft formation reconfiguration and close manoeuvres around obstacles were conducted. The simulation results showed the fast convergence of the proposed algorithm, while local and inter-vehicle constraints were maintained.

The main advantage of this approach is that it adopts a linear form of the objective function. This allows the local optimisation problem to be formulated as a mixed-integer, linear programming problem, most of which can be quickly solved with resort to commercial software.

The purpose of this paper is to simplify the Explicit Nonlinear Model Predictive Controller (ENMPC) by linearizing the trajectory with Quantum-behaved Pigeon-Inspired Optimization (QPIO).

The paper deduces the nonlinear model of the quadrotor and uses the ENMPC to track the trajectory. Since the ENMPC has high demand for the state equation, the trajectory needed to be differentiated many times. When the trajectory is complicate or discontinuous, QPIO is proposed to linearize the trajectory. Then the linearized trajectory will be used in the ENMPC.

Applying the QPIO algorithm allows the unequal distance sample points to be acquired to linearize the trajectory. Comparing with the equidistant linear interpolation, the linear interpolation error will be smaller.

Small-sized quadrotors were adopted in this research to simplify the model. The model is supposed to be accurate and differentiable to meet the requirements of ENMPC.

Traditionally, the quadrotor model was usually linearized in the research. In this paper, the quadrotor model was kept nonlinear and the trajectory will be linearized instead. Unequal distance sample points were utilized to linearize the trajectory. In this way, the authors can get a smaller interpolation error. This method can also be applied to discrete systems to construct the interpolation for trajectory tracking.

The purpose of this paper is to study the alignment measurement in collaborative networks, using the fit concept and predictive performance measurement as its main enablers. A performance prediction approach is used in order to control a collaborative business network based not only in present and past performance measurements of each partner, but also taking into account the future behaviour of the intra‐ and inter‐organisational processes performance.

An exploratory case study was applied to a Brazilian collaborative network and mathematical approaches normally used in control theory were adopted to support alignment measurement.

The use of predictive measurements to manage the alignment between the results of inter‐organisational processes and performance targets set by the collaborative network.

This approach was applied in a specific supply chain network, based on three industrial companies. For other network typologies it will be necessary to evaluate the alignment that can be achieved.

This predictive approach makes it possible to manage performance pro‐actively using feedforward and feedback control. Therefore, tools that consider performance estimation are used based on a data fusion approach, with a proper combination of leading and lagging measurements, which make it possible to use forecasting methods and tools to achieve good predictions.

The paper introduces an approach to alignment measurement leveraged by the new paradigm of performance prediction and presents an alignment metric for collaborative networks.

In the cold rolling process, friction coefficient, oil film thickness and other factors vary dramatically with the change in the rolling speed, which seriously affects the strip thickness deviation. This paper aims to improve the strip control precision with the forecast roll gap model based on CF-PSO-SVM approach in the rolling process.

In this paper, a novel forecasting model of the roll gap based on support vector machine (SVM) optimized by particle swarm optimization with compression factor (CF-PSO) is proposed. Based on lots of online data, the roll gap models regressed by PSO-SVM, genetic algorithm (GA)-SVM and CF-PSO-SVM are obtained and verified through evaluating the performances with the decision coefficient (R²), mean absolute error and root mean square error. In addition, with the good forecasting performances of CF-PSO-SVM, a roll gap compensation model is studied.

The results indicate that the proposed CF-PSO-SVM has excellent learning regression ability compared with other optimization algorithms. Meanwhile, a roll gap compensation model based on the rolling speed and plastic coefficient is obtained, which has been proved validated in product.

In this paper, the SVM algorithm is combined with traditional rolling technology to solve the problems in actual production, which has great supporting significance for the improvement of production efficiency.

This study aims to investigate the rule‐based decentralised control framework for a swarm of UAVs carrying out a cooperative ground target engagement mission scenario.

This study is to investigate the rule‐based decentralised control framework for missions which require high‐level cooperation between team members. The design of the authors’ control strategy is based on agent‐level interactions. Different to a centralized task assignment algorithm, the cooperation of the agents is entirely implicit. The behaviour of the UAVs is governed by rule sets which ultimately lead to cooperation at a system level. The information theoretic measures are adopted to estimate the value of possible future actions. The prediction model is further considered to enhance the team performance in the scenario where there are tight coupled task constraints.

The simulation study evaluates the performance of the decentralised controller and compares it with a centralised controller quantitatively. The results show that the proposed approach leads to a highly cooperative performance of the group without the need for a centralised control authority. The performance of the decentralised control depends on the complexity of the coupled task constraints. It can be improved by using a prediction model to provide information such as the intentions of the neighbours that is not available locally.

The achievable performance of the decentralised control was considered to be low due to the absence of communication and little global coordinating information. This study demonstrated that the decentralised control can achieve highly cooperative performance. The achievable performance is related to the complexity of the coupled constraints and the accuracy of the prediction model.

Most of the existing approaches for flight collision avoidance are concerned with local traffic alone for which the separation is based on the pairwise analysis of aircraft trajectory trends, which is not efficient with regard to some flight path requirements along waypoints. The purpose of this paper is to deal with the global collision avoidance problem which aims at separating aircraft taking into consideration the global traffic in a given area instead of considering them pairwise. It aims to model the concept of global collision avoidance and propose a validated algorithm for the purpose in the framework of free‐flight.

The collision avoidance procedure computes online the appropriate speed and heading for each aircraft, at each sampling time‐instant, to generate a collision‐free flight trajectory along scheduled waypoints. The method accounts for automatic assignment of priority indexes that are updated from one control time horizon to the next. The paper considers here the case of aircraft flying at the same altitude, but the proposed method is easily extendable to the general 3D situation. Owing to the predictive features that are inherent to collision avoidance, the collision‐free trajectories are generated using model predictive control approach. A simulation example is presented in the end and its results show the suitability of the proposed approach.

Since the model predictive control approach is used, the collision avoidance procedure is anticipative; therefore, the avoidance capability depends only on the prediction horizon rather than on the control horizon.

The computation of the trajectory guidance information (speed and heading) at each time‐step is fast, therefore the proposed method is well suited for online processing requirements in real world applications.

The paper provides a flexible modelling framework and a computationally effective algorithm, both based on model predictive control concepts, to cope with global collision avoidance for flight safety enhancement in the framework of free‐flight.

The purpose of this paper is to design a controller for the unmanned aerial vehicle (UAV).

In this study, the constrained multivariable multiple-input and multiple-output (MIMO) model predictive controller (MPC) has been designed to control all outputs by manipulating inputs. The aim of the autopilot of UAV is to keep the UAV around trim condition and to track airspeed commands.

The purpose of using this control method is to decrease the control effort under the certain constraints and deal with interactions between each output and input while tracking airspeed commands.

By using constraint, multivariable (four inputs and seven outputs) MPC unlike the relevant literature in this field, the UAV tracked airspeed commands with minimum control effort dealing with interactions between each input and output under disturbances such as wind.

This paper aims to present a comprehensive review in major research areas of unmanned air vehicles (UAVs) navigation, i.e. three degree-of-freedom (3D) path planning, routing algorithm and routing protocols. The paper is further aimed to provide a meaningful comparison among these algorithms and methods and also intend to find the best ones for a particular application.

The major UAV navigation research areas are further classified into different categories based on methods and models. Each category is discussed in detail with updated research work done in that very domain. Performance evaluation criteria are defined separately for each category. Based on these criteria and research challenges, research questions are also proposed in this work and answered in discussion according to the presented literature review.

The research has found that conventional and node-based algorithms are a popular choice for path planning. Similarly, the graph-based methods are preferred for route planning and hybrid routing protocols are proved better in providing performance. The research has also found promising areas for future research directions, i.e. critical link method for UAV path planning and queuing theory as a routing algorithm for large UAV networks.

The proposed work is a first attempt to provide a comprehensive study on all research aspects of UAV navigation. In addition, a comparison of these methods, algorithms and techniques based on standard performance criteria is also presented the very first time.

The purpose of this paper is to discuss motion planning about crossing obstacles and welding trajectory for a new-model mobile obstacle-crossing welding robot system. The robot can cross the obstacle in this way that one of the three adhesion mobile parts is pulled off the ground in turn. An optimal obstacle-crossing approach needs to be studied to improve the welding efficiency.

According to the characteristics of this mobile welding robot, two methods for crossing obstacles are compared. A special method is used for obstacle-crossing and welding. The kinematic model is established. By the optimization method, the optimum parameters for crossing obstacles are calculated. The welding speed when the robot is crossing the obstacle is very important, so its value must be in a certain range. Finally, the tracks of the wheels when the robot is crossing the obstacle are analyzed in order to observe the obstacle-crossing process.

According to the analysis, the maximum speed of the vehicle in the obstacle-crossing is determined. When crossing the obstacle, the robot can do welding simultaneously. The welding speed cannot exceed a certain value. In the obstacle-crossing process, the tracks of the wheels can reflect the process. According to the obtained conclusion, the obstacle-crossing experiments are successfully completed, and the welding effect is good. The results can prove that the proposed method is feasible.

The speed of obstacle-crossing is not very large. It has some relationships with the lifting speed of the wheels, which is determined by the quality of drive motor. More efficient robot must be developed to meet the needs of industrial robot.

Based on the excellent obstacle-crossing and welding capabilities, the robot with the new mechanism has a widely applying prospect in the field of welding and inspecting large equipment.

The obstacle-crossing approach has certain innovation. The way that the robot can maintain continuous welding when crossing the obstacle is of a great significance.