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This paper proposes an adaptive control for a rigidlink, flexible-joint robot using the Cerebellar Model Articulation Controller (CMAC) and the backstepping method, which is a suitable method when joints are underdamped and exhibit a large amount of flexibility. A previously proposed robust weight update method, deemed the introspective method, is placed into a Lyapunov-stable framework. In the introspective...
When used as part of a hybrid controller, finite-memory strategies synthesizedfrom linear-time temporal logic (LTL) specifications rely on an accurate dynamics model in order to ensure correctness of trajectories. In the presence of uncertainty about the underlying model, there may exist unexpected trajectories that manifest as unexpected transitions under control of the strategy. While some disturbances...
We present a framework for online generation of robust motion plans for robotic systems with nonlinear dynamics subject to bounded disturbances, control constraints, and online state constraints such as obstacles. In an offline phase, one computes the structure of a feedback controller that can be efficiently implemented online to track any feasible nominal trajectory. The offline phase leverages...
Assisted control seeks to help subjects to perform physical movements of the body, which they cannot do by themselves. Passive rehabilitation therapy is very important and vital after the stroke accident. In this functioning mode, the subject is completely passive during the movement. The robot brings the injured upper arm of the patient to perform repetitive therapeutic exercises. In this case, the...
Automating assembly processes outside controlled factory environments is still rare, mostly because of the inherent position uncertainties. The use of compliant motions allows robustness against the uncertainty, but automatic planning of compliant motion sequences is not computationally feasible. In this paper, we show how compliant assembly motions can be learned from human demonstrations. A human...
This paper presents the implementations of Model Predictive Control for the standing balance control of a humanoid to reject external disturbances. The strategies allow the robot to have a compliant behaviour against external forces resulting in a stable and smooth response. The first, ZMP based controller, compensates for the center of mass deviation while the second, attitude controller, regulates...
In this paper, a new blending method for Graphene nanoplatelets was developed. A chaotic system based robust delta robot was designed. Both the speed of the mixer motor and position of the propeller were chaotically changed. Performance of the systems was evaluated by a material analysis method. The results showed that the proposed method has a better performance than statically mounted mixers.
The paper designs an appropriate iterative learning control(ILC) algorithm based on the trajectory characteristics of lower extremity exoskeleton robot, and the algorithm utilizes variable forgetting factor not only does not affect the system in the convergence speed, but also it can enhance the robustness of the system. Considering the lower extremity exoskeleton robot trajectory is periodic motion,...
This paper presents a study of distributed cooperative formation control of a generic multiple agent robots which is applicable for underwater system application such as multiple-autonomous surface vehicles (ASV). The control objective of the group of ASV agent is to maintain a certain formation in a predefined geometric pattern, notably a symmetric formation shape. In addition, the centroid of this...
Highly dynamic tasks that require large accelerations and precise tracking usually rely on precise models and/or high gain feedback. While movement primitives allow for efficient representation of such tasks from demonstrations, the optimization of the required motor commands for systems with inaccurate dynamic models remains an open problem. To achieve accurate tracking for such tasks, we investigate...
If the control authority of a running system is insufficient to reach a target state in a single step, i.e. if deadbeat control is not possible, then a stabilizing controller is faced with the decision on how to plan intermediate steps. In this work, we compare the performance of a simple greedy control policy (that computes deadbeat inputs and simply caps them) with the optimal performance found...
We present an algorithm for generating open-loop trajectories that solve the problem of rearrangement planning under uncertainty. We frame this as a selection problem where the goal is to choose the most robust trajectory from a finite set of candidates. We generate each candidate using a kinodynamic state space planner and evaluate it using noisy rollouts. Our key insight is we can formalize the...
In this paper we exploit Iterative Learning Controllers (ILC) schemes in force adaptation tasks. We propose to encode the control signal with Radial Basis Functions (RBF), which enhances the robustness of the ILC scheme and allows to vary the execution speed of the learned motion. For that a novel control scheme is proposed, which updates the feedforward compensation signals based on current iteration...
A robust tracking control problem is considered for an unknown Euler-Lagrange system to track a desired time-varying trajectory. Unknown dynamics including friction effects and impact forces from the environment are considered in the paper. It is challenging to model these two types of uncertain forces for Euler-Lagrange Systems such as robot manipulators and motors. A robust controller with adaptive...
In this paper, we show that the existence of centrally synergistic potential functions on the n-dimensional sphere, denoted by Sn, is a sufficient condition for the global asymptotic stabilization of a point in Sn. Additionally, if these functions decrease exponentially fast during flows and are bounded from above and from below by some polynomial function of the tracking error, then the reference...
We consider the problem of learning from demonstrations to manipulate deformable objects. Recent work [1], [2], [3] has shown promising results that enable robotic manipulation of deformable objects through learning from demonstrations. Their approach is able to generalize from a single demonstration to new test situations, and suggests a nearest neighbor approach to select a demonstration to adapt...
This paper considers the problem of approximating a kernel matrix in an autoregressive Gaussian process regression (AR-GP) in the presence of measurement noises or natural errors for modeling complex motions of pedestrians in a crowded environment. While a number of methods have been proposed to robustly predict future motions of humans, it still remains as a difficult problem in the presence of measurement...
Reliable precision grasping is a pre-condition for manipulation tasks e.g. in assembly and packaging tasks. Especially for small and light objects robust grasping is extremely challenging since even slight errors in the object pose or dimensions lead to irreparable failures caused by unintended finger-object contacts. State of the art techniques address the problem of grasping in the presence of uncertainty...
The WRSC (World Robotic Sailing Championship) / IRSC (International Robotic Sailing Conference) is an international and annual competition and conference that aims at stimulating the development of autonomous marine robotics and its applications. The competition, originally designed for sailboats, is also opened to motorboats as a separate category since 2013. In this paper, we will present the competition...
In this paper, we consider the problem of tracking a reference trajectory for a simplified car model based on unicycle kinematics, whose position only is measured, and where the control input and the measurements are corrupted by independent Gaussian noises. To tackle this problem we devise a novel observer-controller: the invariant Linear Quadratic Gaussian controller (ILQG). It is based on the Linear...
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