Trajectory planning and control for drone replacement for multidrone cinematography

Numerical Methods for Solution of the Extended Linear Quadratic Control Problem

Frenet-Cartesian model representations for automotive obstacle avoidance within nonlinear MPC

A novel distributed model predictive control scheme based on dynamic integrated system optimization and parameter estimation (DISOPE) was proposed for nonlinear cascade systems under network environment. Under the distributed control structure, online optimization of the cascade system was composed of several cascaded agents that can cooperate and exchange information via network communication. By iterating on modified distributed linear optimal control problems on the basis of estimating parameters at every iteration the correct optimal control action of the nonlinear model predictive control problem of the cascade system could be obtained, assuming that the algorithm was convergent. This approach avoids solving the complex nonlinear optimization problem and significantly reduces the computational burden. The simulation results of the fossil fuel power unit are illustrated to verify the effectiveness and practicability of the proposed algorithm.

This paper presents a novel function of energy-optimal adaptive cruise control (EACC) for electric vehicles based on model predictive control (MPC), which optimally plans the host car's speed trajectory for higher energy efficiency through taking the surrounding traffic information and the road conditions ahead into account. To cope with the nonlinearity of the system dynamics, nonlinear MPC (NMPC) is designed in this paper. Afterwards, the nonlinear equality constraints are relaxed so that the optimization problem has a convex feasible set. Moreover, it is proven that the nonlinear problem can be recast as a second-order cone programming (SOCP) problem. The performance of NMPC is evaluated in the real driving condition and compared to both linear MPC and receding horizon dynamic programming (RH-DP). In order to enable real-time control, the interior point method is used to solve both linear and nonlinear MPC problems and the computation time is investigated in this paper.

In this work, we obtain a closed form solution to a nonlinear model predictive control problem when the controller is developed using a class of black-box Wiener type model. The linear dynamic component of the Wiener model parameterized using orthonormal basis filters. The nonlinear state to output map is constructed using quadratic polynomials. The resulting nonlinear state space model is then used to formulate an unconstrained NMPC problem. A closed form solution to this problem is constructed analytically using the theory of solutions of quadratic operator polynomials. The efficacy of the proposed quadratic control law is demonstrated by conducting servo control studies at a singular operating point on a benchmark experimental setup.

In this study, a collision avoidance method with model predictive control is proposed for a nonlinear model of a four-wheeled vehicle. The C/GMRES algorithm is used for solving the nonlinear model predictive control (NMPC) problem within a short sampling period. A nonlinear tire model is employed to represent the realistic behavior of a vehicle. Here, to consider whether it is possible to avoid an obstacle physically, an unavoidable region (UR) is constructed as the region in which the vehicle cannot avoid the obstacle owing to physical limitations. Even if the distance between the vehicle and the obstacle is short, the vehicle is controlled by NMPC not only to avoid the UR but also to stay on the road. A performance index for satisfying the above constraints is proposed, and control responses are investigated through numerical simulations.

In this paper, the nonlinear optimal control problem is investigated for spacecraft formation flying with collision avoidance. Based on a nonlinear model of formation flying, two optimal tracking control laws are proposed to ensure the formation pattern converges to the predetermined configuration. The first optimal control law which combines Lyapunov optimizing control with a trajectory-following optimization technique is developed to solve the finite-time nonlinear optimal control problem. For the second controller, an extended [Formula: see text]-[Formula: see text] method is applied to design a closed-form feedback control scheme for nonlinear control problem of spacecraft formation flying with non-standard cost functions. By taking into account the flying safety, a repulsive control scheme is incorporated in the optimal controller to ensure collision avoidance and minimize the performance index. Finally, a numerical example is performed to demonstrate the effectiveness of the proposed approaches.

In this paper, we provide a method for a quadrotor to track a ground moving target (GMT). The least squares method is used to obtain a series of reference trajectories generated by the GMT. The tracking is accomplished by applying a novel nonlinear model predictive control (MPC) scheme. The nonlinear MPC scheme applied can deal with the dynamic trajectory tracking problem of nonlinear systems by using reference generic terminal ingredients. After obtaining the reachable reference trajectories and calculating the terminal ingredients of the nonlinear MPC problem, the nonlinear MPC problem can be solved online to obtain the control inputs that enable the quadrotor to accurately track the GMT. A simulation experiment is conducted to verify the efficacy of this method.

Notice of Violation of IEEE Publication Principles<BR><BR>"Sequential Convex Programming for Collaboration of Connected and Automated Vehicles," <BR>by Xiaoxue Zhang, Jun Ma, Zilong Cheng, Frank L. Lewis, and Tong Heng Lee, <BR>in IEEE Transactions on Intelligent Vehicles, Early Access <BR><BR>After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles. <BR><BR>This paper contains significant portions of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission. <BR><BR>"Networked Model Predictive Control for Vehicle Collision Avoidance" <BR> by Bassam Alrifaee, <BR>in RWTH Aachen University, Dissertation, 04 May 2017 <BR>BR>"Sequential convex programming MPC for dynamic vehicle collision avoidance" <BR>by Bassam Alrifaee, Janis Maczijewski, Dirk Abel, <BR>in the Proceedings of the IEEE Conference on Control Technology and Applications, 2017, pp. 2202-2207 <BR><BR> <br/> This paper investigates the collaboration of multiple connected and automated vehicles (CAVs) in different scenarios. In general, the collaboration of CAVs can be formulated as a nonlinear and nonconvex MPC problem. Most of the existing approaches available for utilization to solve such an optimization problem suffer from the drawback of considerable computational burden, which hinders the practical implementation in real time. This paper proposes the use of SCP, which is a powerful approach to solving the nonlinear and nonconvex MPC problem in real time. To appropriately deploy the methodology, as a first stage, SCP requires linearization and discretization when addressing the nonlinear dynamics of the system model adequately. Based on the linearization and discretization, the original MPC problem can be transformed into a QCQP problem. Besides, SCP also involves convexification to handle the associated nonconvex constraints. Thus, the nonconvex QCQP can be reduced to a quadratic programming (QP) problem that can be solved rather quickly. Therefore, the computational efficiency is suitably improved despite the existence of nonlinear and nonconvex characteristics, whereby the implementation is realized in real time. Furthermore, simulation results in three different scenarios of autonomous driving are presented to validate the effectiveness and efficiency of our proposed approach.

This paper presents an adaptation of the Ant System method to find the optimal control input for blood glucose regulation using Model Predictive Control (MPC). The Ant System optimization method was implemented to solve a linear MPC problem and performance was compared with the interior point method for optimization. The Ant System was found to perform well for the linear MPC problem and has the advantage over the interior point method as it can extended for use with non-linear MPC problems.

This paper proposed an algorithm to solve major challenges in the domain of autonomous transportation systems. These challenges are trajectory planning and collision avoidance. Generally, in a shared infrastructure where several agents aim to use limited capacity resources, finding a set of optimal and conflict-free paths for each single agent is the most critical part. In such a dynamic environment where continual planning and scheduling are required, we have adopted a coupled (centralized) technique and face the problem by breaking it into two distinct phases: path planning and collision avoidance. In the first phase, the lowest cost path is planned for each agent by a modified version of Dijkstra algorithm. Following that, in the second phase, the head-on collisions can be foreseen and avoided by detecting the agents' common segments of the path choices. Finally, based on the capacity of each resource and the feasibility of each path, the task assignment and scheduling algorithm allocates different tasks between agents.

In this paper, we tackle the problem of trajectory planning and control of a vehicle under locally varying traction limitations, in the presence of suddenly appearing obstacles. We employ concepts from adaptive model predictive control for run-time adaptation of tire force constraints that are imposed by local traction conditions. To solve the resulting optimization problem for real-time control synthesis with such time varying constraints, we propose a novel numerical scheme based on Real Time Iteration Sequential Quadratic Programming (RTI-SQP), which we call Sampling Augmented Adaptive RTI (SAA-RTI). Sampling augmentation of conventional RTI-SQP provides additional feasible candidate trajectories for warm-starting the optimization procedure. Thus, the proposed SAA-RTI algorithm enables real time constraint adaptation and reduces sensitivity to local minima. Through extensive numerical simulations we demonstrate that our method increases the vehicle's capacity to avoid accidents in scenarios with unanticipated obstacles and locally varying traction, compared to equivalent non-adaptive control schemes and traditional planning and tracking approaches.

To be applicable to real world scenarios trajectory planning schemes for mobile autonomous systems must be able to efficiently deal with obstacles in the area of operation. In the context of optimization based trajectory planning and control a number of different approaches to formulate collision avoidance constraints can be found in the literature. This work presents a novel formulation building on constructive solid geometry (CSG) to describe collision avoidance constraints. It is highly efficient due to a very low number of nonlinear inequality constraints required for a given number of obstacles and sample points and in contrast to the original CSG formulation allows to consider the controlled system’s shape. To allow for a comparison, popular methods to represent obstacles from the literature are summarized and characterized, namely the simple ellipsoidal representation, the original CSG method as well as a direct and an indirect implementation of a signed distance based approach. A benchmark example shows the good performance of the proposed formulation. Here, optimal trajectory planning for marine surface vessels formulated as a nonlinear programming problem is used, where the scenario is designed based on the maritime test field in Kiel, Germany.

In order to better realize the optimal trajectory planning and trajectory control in industrial robots, a method based on ADAM algorithm is proposed. Taking PUMA 560 industrial robot as the research object, using nonlinear data sets and mathematical ADAM algorithm function planning, an optimal calculation method for time trajectory planning of industrial robot is explored. Finally, the programming, optimization, and simulation of the program code are implemented using MATLAB, and a standardized optimal trajectory planning is established. The experimental results show that the running time difference of the trajectory corresponding to the three joint points is small. In order to synchronize the position of each joint point in time, it is necessary to choose the optimal joint point position according to the time trajectory, so as to ensure the synchronization between each key node. Therefore, the joint node position is adjusted so that the total time and the final simulation results are basically synchronized in time, and both are 10.35 s. It proves that the improved ADAM algorithm realizes the trajectory optimization of industrial robots in terms of time planning, which can make the various joints of industrial robots basically synchronized in the time trajectory.

Drilling automation has become one of the principal goals that the drilling industry is working to achieve in the following years. Particularly the analysis of the trajectory control in realtime is a challenging topic where a simulator can be handy. Our study has developed the Rotary Steerable System (RSS) Simulator, where the drilling calculations, including bit force, bit position, trajectory control under uncertainty, and path corrections, are handled without any human interaction. The in-house RSS Simulator, developed in Python, is based on two general functions: the Trajectory Control Optimizer (TCO) and the RSS model. The TCO builds a planned and optimized trajectory (low dogleg severity and short length), controls the bit’s location, and corrects the trajectory if it has deviated from the plan. The RSS model simulates the bit’s behavior and displacement following the three-axial rate of penetration (ROP) model developed by the University of Stavanger (UiS). The RSS Simulator has been tested using one well from the Industry Steering Committee on Wellbore Survey Accuracy (ISCWSA) examples. The simulation results show that the proposed trajectory control algorithm can correct and steer, automatically and optimally, the bit direction. The simulations show the importance of the automated deviation control of the simulator in reaching the final target as close as possible. Finally, implementing the RSS Simulator methods could represent a novel approach within the automation control area that would benefit the drilling industry.