Move blocking (MB) is a widely used strategy to reduce the degrees of freedom (DoFs) of the optimal control problem (OCP) arising in receding horizon control. The size of the OCP is reduced by forcing the input variables to be constant over multiple discretisation steps. In this study, the authors focus on developing computationally efficient MB schemes for multiple shooting‐based non‐linear model predictive control (NMPC). The DoFs of the OCP is reduced by introducing MB in the shooting step, resulting in a smaller but sparse OCP. Therefore, the discretisation accuracy and level of sparsity are maintained. A condensing algorithm that exploits the sparsity structure of the OCP is proposed, that allows to reduce the computation complexity of condensing from quadratic to linear in the number of discretisation nodes. As a result, active‐set methods with warm‐start strategy can be efficiently employed, thus allowing the use of a longer prediction horizon. A detailed comparison between the proposed scheme and the non‐uniform grid NMPC is given. Effectiveness of the algorithm in reducing computational burden while maintaining optimisation accuracy and constraints fulfilment is shown by means of simulations with two different problems.

Unmanned aerial vehicle (UAV) light shows (UAV-LS) have a wow factor due to their advantages in terms of environment friendliness and controllability compared to traditional fireworks. In this paper, a UAV-LS system is developed including a collision-free formation transformation trajectory planning algorithm, a software package that facilitates animation design and real-time monitoring and control, and hardware design and realization. In particular, a dynamic task assignment algorithm based on graph theory is proposed to reduce the impact of UAV collision avoidance on task assignment and the frequency of task assignment in the formation transformation. In addition, the software package consists of an animation interface for formation drawing and 3D animation simulation, which helps the monitoring and control of UAVs through a real-time monitoring application. The developed UAV-LS system hardware consists of subsystems of decision-making, real-time kinematic (RTK) global positioning system (GPS), wireless communication, and UAV platforms. Outdoor experiments using six quadrotors are performed and details of implementations of high-accuracy positioning, communication, and computation are presented. Results show that the developed UAV-LS system can successfully complete a light show and the proposed task assignment algorithm performs better than traditional static ones.

In recent years, efficient optimization algorithms for Nonlinear Model Predictive Control (NMPC) have been proposed, that significantly reduce the on-line computational time. In particular, direct multiple shooting and Sequential Quadratic Programming (SQP) are used to efficiently solve Nonlinear Programming (NLP) problems arising from continuous-time NMPC applications. One of the computationally demanding steps for on-line optimization is the computation of sensitivities of the nonlinear dynamics at every sampling instant, especially for systems of large dimensions, strong stiffness, and when using long prediction horizons. In this paper, within the algorithmic framework of the Real-Time Iteration (RTI) scheme based on multiple shooting, an inexact sensitivity updating scheme is proposed, that performs a partial update of the Jacobian of the constraints in the NLP. Such update is triggered by using a Curvature-like Measure of Nonlinearity (CMoN), so that only sensitivities exhibiting highly nonlinear behaviour are updated, thus adapting to system operating conditions and possibly reducing the computational burden. An advanced tuning strategy for the updating scheme is provided to

The task priority planning problem is addressed in the task supervisor of null-space behavioral (NSB) control for multi-agent systems. Traditional methods rely on pre-defined logic-based or fuzzy rules to adjust task priority. In this work, a novel task supervisor is proposed using model predictive control (MPC) techniques. At each sampling instant, the task priority planning problem is formulated as a switching mode optimal control problem (OCP), which can be solved by efficient mixed-integer optimal control algorithms. The optimal task priority order is obtained based on current and predictive information of agents, without the need for a pre-defined rule. By explicitly introducing slack variables into constraints, the proposed MPC method is flexible to cope with dynamic obstacles in unknown environments. Simulations with static and dynamic obstacles show that the proposed method can provide significantly better control performance than the traditional logic-based method using less priority switchings.

In this paper, a novel human decision-making behavior model is proposed for human-robot interaction (HRI) for the control of multi-robot systems (MRS). The proposed human drift diffusion model (HDDM) combines the traditional drift diffusion model (DDM) and the null-space-based behavioral control (NSBC) method by introducing a data-processing station and a human cognitive system. In the HDDM, the evolution of human-decision information is computed. By using a threshold of such information to trigger human interaction, accurate human decision-making timing can be obtained. In addition, a cooperative controller is designed for robots to follow human instructions. Simulations under various scenarios show that by using the proposed HDDM and the controller, robots can complete human instructions more accurately comparing to traditional methods. An experiment using a group of quadrotors subject to external wind disturbances also demonstrate the effectiveness of the proposed HDDM in real-world uncertain environments.

Space situational awareness (SSA) plays an important role in maintaining space advantages. Task planning is one of the key technologies in SSA to allocate multiple tasks to multiple satellites, so that a satellite may be allocated to supervise multiple space objects, and a space object may be supervised by multiple satellites. This paper proposes a hierarchical and distributed task-planning framework for SSA systems with focus on fast and effective task planning customized for SSA. In the framework, a global task-planner layer performs satellite and object clustering, so that satellites are clustered into multiple unique clusters on the basis of their positions, while objects are clustered into multiple possibly intersecting clusters, hence allowing for a single object to be supervised by multiple satellites. In each satellite cluster, a local task planner performs distributed task planning using the contract-net protocol (CNP) on the basis of the position and velocity of satellites and objects. In addition, a customized discrete particle swarm optimization (DPSO) algorithm was developed to search for the optimal task-planning result in the CNP. Simulation results showed that the proposed framework can effectively achieve task planning among multiple satellites and space objects. The efficiency and scalability of the proposed framework are demonstrated through static and dynamic orbital simulations.