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Multiple robot systems are employed in various applications to get the complex tasks carried out by a group of robots. When Autonomous Underwater Vehicles (AUVs) are employed for underwater missions, they provide higher quality data, more coverage and reduces the mission time, thus resulting in huge cost savings. However, the formation control of such robots depends to a great extent on the communication...
The multi-impulse and multi-criteria optimal trajectories of the spacecraft transfer between orbits of various types are considered. A practical goal is the inspection or service of a number of space objects moving along these orbits at the initial data set. It is required to define a set of the admissible solutions taking into account priorities and possible restrictions on time or on expenses of...
The design of a robotic vehicle of Autonomous Mobility Laboratory (LMA) at UNICAMP is developed in-vehicle platform FIAT-PUNTO. In addition to a set of sensors, actuators, mechanism and components (hardware and software), new technologies should be developed to support the Automation, Control, Perception, Localization and Navigation. The purposes of this work is to present a non-linear vehicle model...
This paper introduces the Langevin Monte Carlo Filter (LMCF), a particle filter with a Markov chain Monte Carlo algorithm which draws proposals by simulating Hamiltonian dynamics. This approach is well suited to non-linear filtering problems in high dimensional state spaces where the bootstrap filter requires an impracticably large number of particles. The simulation of Hamiltonian dynamics is motivated...
Methods of optimizing a single trajectory are mature enough for planning in many applications. Yet such optimization methods applied to high Degree-Of-Freedom robots either consume too much time to be real-time or approximate the dynamics such that they lack physical consistency. In this paper, we present a method of precomputing optimized trajectories and compressing the information to get a compact...
In this paper, we address the problem of gait generation for bipedal robots. We cast the determination of a Center of Mass (CoM) reference trajectory for a given Zero Moment Point (ZMP) desired behaviour as a stable inversion problem for non-minimum phase systems and obtain an analytical solution for any given ZMP trajectory. Our method exploits results from our previous research, in which we derived...
This paper presents an online motion planning method based on Continuation/Generalized Minimum Residual (C/GMRES) to solve a receding horizon optimization problem for unmanned aerial vehicles (UAVs). Hard constraints, such as nonlinear state, input saturation constraints and obstacle constraint are included into the problem formulation by introducing the dummy factors. The optimal control problem...
We propose a model reduction technique for quadratic-bilinear descriptor systems. The approach involves approximating the system by a bilinear descriptor system using Carleman bilinearization [1]. It is shown that, by assuming a particular structure of the matrix pencil, the bilinearization process preserves the structure of the matrix pencil in the bilinearized system. Further, we extend the use...
This contribution presents a novel approach for nonlinear time-optimal model predictive control (MPC) based on Timed-Elastic-Bands (TEB). The TEB merges the states, control inputs and time intervals into a joint trajectory representation which enables planning of time-optimal trajectories in the context of model predictive control. Model predictive control integrates the planning of the optimal trajectory...
We propose a distributed controller to solve the Cooperative Collision Avoidance problem. We consider a network of vehicles, each with its own dynamic constraints and objective. The problem is to minimize the total network objective function subject to the vehicles' individual constraints and their shared collision avoidance constraints over a given time horizon. The proposed controller, a proximal...
We present a Bayesian nonparametric trajectory optimization framework for systems with unknown dynamics using Gaussian Processes (GPs), called Gaussian Process Differential Dynamic Programming (GPDDP). Rooted in the Dynamic Programming principle and second-order local approximations of the value function, GPDDP learns time-varying optimal control policies from sampled data. Based on this framework,...
In this paper, we address the problem of optimal and safe coordination of autonomous vehicles through a traffic intersection. We state the problem as a finite time, constrained optimal control problem, a combinatorial optimization problem that is difficult to solve in real-time. A low complexity computational scheme is proposed, based on a hierarchical decomposition of the original optimal control...
We propose a framework for generating a control policy for a traffic network of signalized intersections to accomplish control objectives expressed in linear temporal logic. Traffic management indeed calls for a rich class of objectives and offers a novel domain for these formal methods tools. We show that traffic networks possess structural properties that allow significant reduction in the time...
We solve a minimum-energy, point-to-point optimal maneuver problem for satellite proximity flight in a perturbed orbit. We perform this control optimization on the nonlinear system using the Maximum Principle, and on a particular linear approximation using the minimum energy transfer theorem. In both cases we assume unperturbed dynamics. We then use a sliding mode controller to track the optimal trajectories...
In this paper, we present a framework for shaping pulses to control biological systems, and specifically systems in synthetic biology. By shaping we mean computing the magnitude and the length of a pulse, application of which results in reaching the desired control objective. Hence the control signals have only two parameters, which makes these signals amenable to wetlab implementations. We focus...
We consider robust predictive control of continuous-time, constrained, nonlinear systems by means of a discrete-time control scheme. The key idea is to discretize the system first and to explicitly bound the arising discretization error. Taking this error into account a robustly stabilizing predictive controller is proposed that guarantees constraint satisfaction at the sampling instances and via...
A class of singularly perturbed stochastic differential equations (SDE) with linear drift and nonlinear diffusion terms is considered. We prove that, on a finite time interval, the trajectories of the slow variables can be well approximated by those of a system with reduced dimension as the singular perturbation parameter becomes small. In particular, we show that when this parameter becomes small...
In this paper we present a new approach of using input-output linearization to control a single input, single output, input-affine nonlinear non-minimum phase system. We will show that, if the linearized system is stabilizable, we can redefine the output of the system such that the input-output linearized system is locally asymptotically stable. Furthermore we develop an LQR technique for designing...
In this paper, we consider the problem of model reduction of large scale systems, such as those obtained through the discretization of PDEs. We propose a randomized proper orthogonal decomposition (RPOD) technique to obtain the reduced order models by randomly choosing a subset of the inputs/outputs of the system to construct a suitable small sized Hankel matrix from the full Hankel matrix. It is...
This paper studies the switching control of differential-algebraic equations (DAEs). A specific problem concerned with switched DAEs is that jumps or impulses could be induced by mode switching, which is not well understood in many applications. We aim to find the control strategies that minimize the overall magnitude of undesirable jumps or impulses while rendering the systems achieve the expected...
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