Distributed Model Predictive Formation Control for UAVs and Cooperative Capability Evaluation of Swarm

A new consensus theory-based method for formation control and obstacle avoidance of UAVs

In this paper autonomous formation control for Unmanned Aerial Vehicles (UAVs) has been discussed and a real time solution has been put forward by benefiting General Purpose Graphical Processing Units (GPGPU) accelerated potential field approach while ensuring obstacle and collision avoidance in unknown environment by using real-time sensors. GPGPU accelerated real time formation control for UAVs was designed and the basic model of the approach has been explained in our previous work (Cetin and Yilmaz 2014). As the deficiencies of the previous approach, autonomous real time collision and coordinated obstacle avoidance features in unknown environments are also handled while maintaining formation flight conditions in this work. With these features, improved autonomous formation control approach is discussed as a real time solution. The computation is performed by using Graphical Processing Units (GPUs) as parallel computation architectures by benefiting from Single Instruction Multiple Data (SIMD) type parallel algorithms. Classic binary map conversation, connected component labeling and minimum bounding box algorithms which are commonly used for image processing applications, has been evaluated for real time obstacle detection and avoidance features by developing GPGPU suitable parallel algorithms. Real-time solution has been developed by integrated these parallel algorithms with parallel Artificial Potential Field (APF) computation algorithm. Simulation results are proved that this novel autonomous improved formation control approach is successful and it would be used in real time applications like UAV formation flight missions.

The obstacle avoidance problem of unmanned aerial vehicle (UAV) mainly refers to the design of a method that can safely reach the target point from the starting point in an unknown flight environment. In this paper, we mainly propose an obstacle avoidance method composed of three modules: environment perception, algorithm obstacle avoidance and motion control. Our method realizes the function of reasonable and safe obstacle avoidance of UAV in low-altitude complex environments. Firstly, we use the light detection and ranging (LiDAR) sensor to perceive obstacles around the environment. Next, the sensor data is processed by the vector field histogram (VFH) algorithm to output the desired speed of drone flight. Finally, the expected speed value is sent to the quadrotor flight control and realizes autonomous obstacle avoidance flight of the drone. We verify the effectiveness and feasibility of the proposed method in 3D simulation environment.

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.

This is a position paper on the current state of the art in distributed Model Predictive Control (MPC) and our view on its future potential. We present results from a recent survey of 35 distributed MPC approaches. For this, we propose a way in which distributed MPC approaches can be categorized for comparison. We also link the potential that these approaches have to the domain of other fields such as integrated environmental optimal decision making and cyber-physical systems. Many challenges for realizing these links have to be faced. We present our views on how the advances in several highly active research domains could be used to overcome these challenges. As such, this paper is intended as a starting point for the exploration of various novel research cooperations.

The model predictive control method can be effective for converter control in distributed power generation but requires a large amount of computation, leading to a considerable time delay in the actuation. If the delay is not reflected, the system performance could get worse. This paper presents a two-step (horizon) prediction algorithm of model predictive control technique for grid-tied inverters used in wind turbine systems. The control objectives such as active and reactive power flow and switching loss reduction are reflected in the objective function of the controller. The proposed model predictive control strategy is verified numerically by using MATLAB/Simulink.

A distributed approach based on virtual tube is proposed in this article to address the formation exploration of micro multirotor unmanned aerial vehicles (UAVs). A generic mathematical model for the virtual tube is established. Then, a path planning method for a formation cluster based on distributed model predictive control and the consensus theory is introduced. This method incorporates tube constraints by modeling the problem as a multiobjective optimization with linear constraints, then the considered problem is ultimately transformed into a quadratic programming problem that can be deployed on real UAV's onboard computers with limited computational resources. In addition, an assumed state constructor based on neighbor prediction information is proposed to enhance algorithm stability, thus eliminating the need for a priori knowledge of target points for all UAVs. Finally, the effectiveness and practicality of the algorithm are validated through simulations in three different tube environments and a real-world experiment involving six multirotor UAVs.

To improve the dynamic and steady-state control performance of permanent magnet synchronous motors under the three-vector model predictive current control method, this study proposes a switching current predictive control method based on the exponential moving average algorithm, which evaluates the magnitude of the change of the q-axis current slope in real time to discriminate the motor’s operating conditions and selects the optimal control method for different operating conditions. Meanwhile, the traditional three-vector model predictive current control method is improved by introducing a comparison mechanism for the q-axis current slope to select the second effective voltage vector, avoiding the secondary optimization calculation of the value function and reducing the computational complexity of the traditional method. By comparing the proposed method with the traditional three-vector model predictive current control method, the experimental results prove that the proposed method improves the system’s dynamic response and steady-state performance.

In this paper, a multi-agent formation control strategy with collision avoidance and obstacle avoidance in complex environments is proposed. Under a virtual leader structure, the multi-agent formation is realized through path planning and formation control. In path planning, a transitional region based on prediction horizon is used to replan reference trajectories and avoid obstacles in advance. The virtual-leader alternation strategy and feasible state searching strategy are adopted to avoid one side obstacles and crowded obstacles, respectively. Then the path planning optimization problem is formulated by mixed-integer quadratic programming (MIQP) to calculate trajectories collision-free with obstacles. In formation control, a distributed model predictive control (DMPC) algorithm is developed to form and maintain formation. In DMPC optimization problem, the non-convex obstacle avoidance constraints are replaced with deviation constraints and linear constraints to design terminal invariant set and reduce online computation burden. Under the whole formation control strategy, the tasks of obstacle avoidance and collision avoidance are both achieved and the agents maintain the formation as much as possible. Finally, the example with different obstacle situations is discussed to illustrate the effectiveness of the proposed strategy.

Model predictive current control has high control accuracy and good control performance, and has been widely applied in the control strategy of dual three-phase permanent magnet synchronous motors. This article proposes an improved deadbeat model predictive current control (DMPCC) method by analyzing the mathematical model of a dual three-phase open-winding permanent magnet synchronous motor. One inverter predicts and calculates the optimal voltage vector through the deadbeat model, while the other inverter calculates the optimal voltage vector through the value function rolling optimization. This method reduces the computational workload of the controller, avoids the delay problem of the traditional model predictive current control (MPCC) method, improves the system's response speed, effectively suppresses the current in the harmonic sub-plane, and reduces torque ripple. The simulation results have verified the accuracy and speed of the above control method.

The complicated formation flight for multi-unmanned aerial vehicles is a challenge, especially when multi-mission requirements are taken into account. This paper studies the adaptive differential evolution-based distributed model predictive control approach to deal with the multi-unmanned aerial vehicle flight achieving obstacle/collision avoidance and formation keeping simultaneously in the complex environment. Specifically, the distributed model predictive controller is designed to achieve stable flight for each unmanned aerial vehicle as well as taking the state and input saturation into account, where the local optimization problem is solved by the adaptive differential evolution algorithm. Besides, the adaptive adjustment to the prediction horizon for the model predictive controller is introduced, while the asymptotic convergence of the rolling optimization is analyzed as well. Finally, simulation examples are provided to illustrate the validity of the proposed control structure.

Formation control and obstacle avoidance for multi-agent systems have attracted more and more attention. In this paper, the problems of formation control and obstacle avoidance are investigated by means of a consensus algorithm. A novel distributed control model is proposed for the multi-agent system to form the anticipated formation as well as achieve obstacle avoidance. Based on the consensus algorithm, a distributed control function consisting of three terms (formation control term, velocity matching term, and obstacle avoidance term) is presented. By establishing a novel formation control matrix, a formation control term is constructed such that the agents can converge to consensus and reach the anticipated formation. A new obstacle avoidance function is developed by using the modified potential field approach to make sure that obstacle avoidance can be achieved whether the obstacle is in a dynamic state or a stationary state. A velocity matching term is also put forward to guarantee that the velocities of all agents converge to the same value. Furthermore, stability of the control model is proven. Simulation results are provided to demonstrate the effectiveness of the proposed control.

Multi-vector model predictive control of permanent magnet synchronous motor can effectively overcome the shortcomings of conventional single-vector model predictive control such as large motor current ripples and limited control accuracy, but its control performance is more susceptible to the influence of motor parameter perturbation. To improve the robustness of the multi-vector predictive control algorithm under the motor parameter perturbation, a novel nonparametric three-vector model predictive current control method for permanent magnet synchronous motors is proposed in this paper. Through constructing an ultra-local predictive current model based on the input and output signals of the permanent magnet synchronous motor, the influence of motor parameter perturbation on current prediction is avoided, and a disturbance observer is designed to estimate the non-modeled and perturbed parts of the ultra-local model. In addition, the direct calculation method of vector duty cycles based on current error is introduced to suppress the influence of motor parameter uncertainty on the duty cycle calculation link, which further improves the system robustness. Finally, simulations and experiments of the proposed method are demonstrated to compare with the conventional parametric three-vector model predictive current control, and the results show that the proposed control strategy can effectively suppress the disturbances caused by the motor parameters and ensure the steady-state performance during motor operation.

To improve the load frequency control (LFC) performance of power systems incorporating virtual power plants (VPPs) while reducing network resource consumption, a model predictive control (MPC) method based on a mixed time/event-triggered mechanism (MTETM) is proposed. This mechanism integrates an event-triggered mechanism (ETM) with a time-triggered mechanism (TTM), where ETM avoids unnecessary signal transmission and TTM ensures fundamental control performance. Subsequently, for the LFC system incorporating VPPs, a state hard constrained MPC problem is formulated and transformed into a “min-max” optimisation problem. Through linear matrix inequalities, the original optimisation problem is equivalently transformed into an auxiliary optimisation problem, with the optimal control law solved via rolling optimisation. Theoretical analysis demonstrates that the proposed auxiliary optimisation problem possesses recursive feasibility, whilst the closed-loop system satisfies input-to-state stability. Finally, validation through case studies of two regional power systems demonstrates that the MPC approach based on MTETM outperforms the ETM-based MPC approach in terms of control performance while maintaining a triggering rate of 33.3%. Compared with the TTM-based MPC algorithm, the MTETM-based MPC method reduces the triggering rate by 66.7%, while maintaining nearly equivalent control performance. Consequently, the results validate the effectiveness of the MTETM-based MPC approach in conserving network resources while maintaining control performance.

This paper proposes a highly efficient single-phase three-level neutral point clamped (NPC) converter operated by a model predictive control (MPC) method with reduced commutations of switches. The proposed method only allows switching states with none or a single commutation at the next step as candidates for future switching states for the MPC method. Because the proposed method preselects switching states with reduced commutations when selecting an optimal state at a future step, the proposed method can reduce the number of switchings and the corresponding switching losses. Although the proposed method slightly increases the peak-to-peak variations of the two dc capacitor voltages, the developed method does not deteriorate the input current quality and input power factor despite the reduced number of switching numbers and losses. Thus, the proposed method can reduce the number of switching losses and lead to high efficiency, in comparison with the conventional MPC method.