Cooperative Online Path Planning for UAVs Based on DMPC and Improved Grey Wolf Optimizer

A multi-ship collision avoidance decision support and path following control formulation is studied in a distributed way. This paper proposes a complete set of solutions for multi-ship collision avoidance in intelligent navigation, by using a top-to-bottom organization to structure the system. The system is designed with two layers: the decision-making layer for path planning and the control layer for path following. Under the general requirements from the International Regulations for Preventing Collisions at Sea (COLREGs), the performance of distributed path planning decision-making for anti-collision is analyzed for both give-way and stand-on ships situations, and the Virtual Field Force method(VFF) is used for the path planning in details. The developed VFF method combined with the model of ship domain takes the target ships' speed and course into account, so that it can judge the moving characteristics of obstacles more accurately. Furthermore, the distributed Model Predictive Control (MPC) method of path following is analyzed, which form the control layer. The MPC method has the advantages of dealing with the under-actuated nonlinear system like the surface vessel. Simulation results indicate that the system proposed can work effectiveness.

Distributed trajectory optimization for multiple solar-powered UAVs target tracking in urban environment by Adaptive Grasshopper Optimization Algorithm

This paper proposes a distributed model predictive control (DMPC) method with guaranteed stability for system with control coupled information and coupled constraints among the subsystems, which are described by piecewise affine (PWA) model. According to the condition of control input coefficient matrix of the PWA model, two frameworks are established. The DMPC optimal problem of each subsystem is converted into mixed integer quadratic programming (MIQP) to solve and obtain the control actions. To ensure the stability, equality constraint is applied.

Distributed Model Predictive Control with Event-Based Optimization

To cope with the volatility and randomness of wind power, photovoltaic (PV) power, and load demands in the islanded microgrid, and also to ensure the safety and economic operation of the islanded microgrid system. A collaborative Distributed model predictive control (Di-MPC) based voltage optimization control strategy is proposed, which considers the strong coupling characteristic of active and reactive power due to the impedance ratio of islanded microgrid, and the requirements of real-time and robustness in the optimization as well. By coordinating the controllable devices in the source-grid-load side of the islanded microgrid, the proposed strategy aims to make full use of the voltage regulation capability of each controllable device. Firstly, by considering the different operating characteristics of the controllable devices, a multi-time scale distributed voltage optimal control model is established. It divides the optimal control process into long-time scale and short-time scale and optimizes for respective objective functions and control variables in different time scales. Secondly, a collaborative Di-MPC-based voltage optimal control strategy is proposed. With the proposed collaboration mechanism, the power output increments of the distributed generators (DGs) are solved in the short-time scale, and the errors in the long-time scale control are also fixed. Finally, the simulation results show that compared with a traditional optimal control method and a centralized model predictive control (CMPC) method, the proposed voltage optimization control strategy can effectively reduce the voltage deviation and fluctuation at each node while ensuring the economic operation of the islanded microgrid system.

This paper utilizes the distributed model predictive control (DMPC) method to investigate the formation control problem of unmanned aerial vehicles (UAVs) in the obstacle environment and establishes cooperative capability evaluation metrics of the swarm. Based on the DMPC approach, the formation cost function is constructed to adjust the relative positions and velocities of UAVs, ensuring the desired formation. Additionally, to address the obstacle avoidance problem in the formation, the obstacle avoidance function is designed to provide safe formation control in the obstacle environment. To evaluate the cooperative capability of UAVs, we design evaluation metrics from multiple dimensions to reflect the swarm’s cooperative capability. Finally, the simulation results show the effectiveness of the formation control method with obstacle avoidance and the applicability of the swarm’s cooperative capability evaluation metrics.

In a typical traffic scenario, automated vehicles are required to interact with surrounding traffic participants, e.g. human-driven vehicles, pedestrians, etc. In this paper, a novel adaptive authority allocation strategy considering the social behaviours of surrounding vehicles is proposed for the shared steering control (SSC) of automated vehicles. First, a Koopman-based potential-field-driven distributed model predictive control (K-PF-DMPC) method is proposed for the modelling of vehicle interaction to describe surrounding vehicle's social behaviour. This method effectively deals with the nonlinearity embedded in the non-cooperative game-based vehicle interaction model, so that the analytical form of Nash equilibrium can be derived for a fast online solution. Then, the SSC system of the automated vehicle is implemented by a weighted summation method. To balance the driving performance and intervention degree while considering the surrounding vehicles' social behaviours, a hybrid fuzzy strategy (HFS) is proposed for allocating the control authority between the driver and automation. The value of authority allocation coefficient is calculated by fusing the outputs from two shared fuzzy controllers, i.e. the enhanced and weakened shared fuzzy controllers, in accordance to the belief of the surrounding vehicle's type. Several numerical simulations and driver-in-the-loop simulator experiments are conducted for validation. Results show that the proposed strategy can provide appropriate steering intervention to the driver owing to the consideration of the surrounding vehicle's social behaviour and obtains the best driver-automation collaboration performance compared with the comparative groups.

In this paper, an observer‐based event‐triggered distributed model predictive control method is proposed for a class of nonlinear interconnected systems with bounded disturbances, considering unmeasurable states. First of all, the state observer is constructed. It is proved that the observation error is bounded. Second, distributed model predictive controller is designed by using observed value. Meanwhile, the event‐triggered mechanism is set by using the error between the actual output and the predicted output. The setting of event‐triggered mechanism not only ensures the error between the actual output and the predicted output within a certain range, but also reduces the calculation amounts of solving the optimization problem. The states of each subsystem enter the terminal invariant set by distributed model predictive control, and then are stabilized in the invariant set under the action of output feedback control law. In addition, sufficient conditions are given to ensure the feasibility of the algorithm and the stability of the closed‐loop system. Finally, the numerical example is given, and the simulation results verify the effectiveness of the proposed algorithm.

This paper is concerned with the distributed model predictive control (MPC) problem for a class of discrete-time Markovian jump linear systems (MJLSs) subject to actuator saturation and polytopic uncertainty in system matrices. The global system is decomposed into several subsystems which coordinate with each other. A set of distributed controllers is designed by solving a min-max optimization problem in terms of the solutions of linear matrix inequalities (LMIs). An iterative algorithm is developed to achieve the online computation. Finally, a simulation example is employed to show the effectiveness of the proposed algorithm.

A general distributed MPC framework for cooperative control

A robust distributed model predictive control algorithm

This work focuses on a class of nonlinear control problems that arise when new control systems which may use networked sensors and/or actuators are added to already operating control loops to improve closed‐loop performance. In this case, it is desirable to design the pre‐existing control system and the new control system in a way such that they coordinate their actions. To address this control problem, a distributed model predictive control method is introduced where both the pre‐existing control system and the new control system are designed via Lyapunov‐based model predictive control. Working with general nonlinear models of chemical processes and assuming that there exists a Lyapunov‐based controller that stabilizes the nominal closed‐loop system using only the pre‐existing control loops, two separate Lyapunov‐based model predictive controllers are designed that coordinate their actions in an efficient fashion. Specifically, the proposed distributed model predictive control design preserves the stability properties of the Lyapunov‐based controller, improves the closed‐loop performance, and allows handling input constraints. In addition, the proposed distributed control design requires reduced communication between the two distributed controllers since it requires that these controllers communicate only once at each sampling time and is computationally more efficient compared to the corresponding centralized model predictive control design. The theoretical results are illustrated using a chemical process example. © 2009 American Institute of Chemical Engineers AIChE J, 2009

With an increasing number of vehicles on the road, multiple‐vehicle platoons have become a new solution under communication constraints. However, the existing studies seldom consider the effect of the tail vehicle on the information transfer and platoon control over long distances, and thus the control performance under time‐varying delays is unacceptable. In this study, a hierarchical multi‐platoon control architecture considering the backbone platoon and sub‐platoons is proposed under non‐ideal communication conditions, where the backbone platoon layer communication topology is designed to achieve backbone platoon control and appropriate coordination among sub‐platoons. Distributed model predictive controllers (DMPCs) for backbone‐ and sub‐platoons that integrate delay compensation have been proposed. Buffers and delay compensators in the proposed DMPC are designed to reduce the interference caused by non‐ideal communications. The asymptotic stability is analyzed at the sub‐ and multi‐platoon levels. The results show that compared with traditional DMPC without a compensator, the proposed DMPC can eliminate the velocity lag and steady‐state spacing errors and maintain the maximum velocity and spacing errors within 0.11 m/s and 0.17 m, respectively, under certain operating conditions. Thus, the proposed method guarantees the stability of multiple platoons under both the constant and time‐varying delay conditions.

Generally, optimization is defined as the process of determining the minimum or maximum value that depends on the function of the goal, even now there are many problems regarding optimization. One of them is the problem regarding the selection of goods to be included in a limited storage medium called Knapsack problem. Knapsack problems have different types and variations. This study will solve the problem of bounded knapsack multiple constraints by implementing the Grey Wolf Optimizer (GWO) algorithm. The problem of bounded knapsack multiple constraints has more than one subject with the items that are inserted into the dimension storage media can be partially or completely inserted, but the number of objects is limited. The aim of this study is to determine the results of using the Grey Wolf Optimizer (GWO) algorithm for solving the problem of multiple constraints bounded knapsack and compare the optimal solutions obtained by the simplex method using the Solver Add-In in Microsoft Excel. The data used in this study is primary data. There are two parameters to be tested, namely population parameters and maximum iteration. The test results of the two parameters show that the population parameters and maximum iterations have the same effect, where the greater the value of the population parameters and the maximum iteration, the results obtained are also getting closer to the optimal value. In addition, based on the results of the final experiment it is known that the comparison of the results of the GWO algorithm and the simplex method has a fairly small percentage deviation which indicates that the GWO algorithm produces results that are close to the optimal value.
 Keywords: GWO algorithm, Knapsack, Multiple Constraints Bounded Knapsack.

The problem that it is difficult to balance vehicle stability and economy at the same time under the starting steering condition of a four-wheel independent drive electric vehicle (4WIDEV) is addressed. In this paper, we propose a coordinated optimal control method of AFS and DYC for a four-wheel independent drive electric vehicle based on the MAS model. Firstly, the angular velocity of the transverse pendulum at the center of mass and the lateral deflection angle of the center of mass are decoupled by vector transformation, and the two-degree-of-freedom eight-input model of the vehicle is transformed into four two-degree-of-freedom two-input models, and the reduced-dimensional system is regarded as four agents. Based on the hardware connection structure and communication topology of the four-wheel independent drive electric vehicle, the reduced-dimensional model of 4WIDEV AFS and DYC coordinated optimal control is established based on graph theory. Secondly, the deviation of the vehicle transverse swing angular velocity and mass lateral deflection angle from their ideal values is oriented by combining sliding mode variable structure control (SMC) with distributed model predictive control (DMPC). A discrete dynamic sliding mode surface function is proposed for the ith agent to improve the robustness of the system in response to parameter variations and disturbances. Considering the stability and economy of the ith agent, an active front wheel steering and drive torque optimization control method based on SMC and DMPC is proposed for engineering applications. Finally, a hardware-in-the-loop (HIL) test bench is built for experimental verification, and the results show that the steering angle is in the range of 0-5°, and the proposed method effectively weighs the system dynamic performance, computational efficiency, and the economy of the whole vehicle. Compared with the conventional centralized control method, the torque-solving speed is improved by 32.33 times, and the electrical consumption of the wheel motor is reduced by 16.6%.