1,721,073 research outputs found
Sensorless environment stiffness and interaction force estimation for impedance control tuning in robotized interaction tasks
Full text linkIndustrial robots are increasingly used to perform tasks requiring an interaction with the surrounding environment (e.g., assembly tasks). Such environments are usually (partially) unknown to the robot, requiring the implemented controllers to suitably react to the established interaction. Standard controllers require force/torque measurements to close the loop. However, most of the industrial manipulators do not have embedded force/torque sensor(s) and such integration results in additional costs and implementation effort. To extend the use of compliant controllers to sensorless interaction control, a model-based methodology is presented in this paper. Relying on sensorless Cartesian impedance control, two Extended Kalman Filters (EKF) are proposed: an EKF for interaction force estimation and an EKF for environment stiffness estimation. Exploiting such estimations, a control architecture is proposed to implement a sensorless force loop (exploiting the provided estimated force) with adaptive Cartesian impedance control and coupling dynamics compensation (exploiting the provided estimated environment stiffness). The described approach has been validated in both simulations and experiments. A Franka EMIKA panda robot has been used. A probing task involving different materials (i.e., with different - unknown - stiffness properties) has been considered to show the capabilities of the developed EKFs (able to converge with limited errors) and control tuning (preserving stability). Additionally, a polishing-like task and an assembly task have been implemented to show the achieved performance of the proposed methodology
FIR Approximation of Linear Systems from Quantized Records
No full textIn this paper we consider the problem of identifying a fixed-order FIR approximation of linear systems with unknown structure, assuming that both input and output measurements are subjected to quantization. In particular, a FIR model of given order which provides the best approximation of the input-output relationship is sought by minimizing the worst-case distance between the output of the true system and the modeled output, for all possible values of the input and output data consistent with their quantized measurements. First we show that the considered problem can be formulated in terms of robust optimization. Then, we present two different algorithms to compute the optimum of the formulated problem by means of linear programming techniques. The effectiveness of the proposed approach is illustrated by means of a simulation example
Maximum-a-posteriori estimation of jump Box-Jenkins models
No full textComplex dynamical systems and time series can often be described by jump models, namely finite collections of local models where each sub-model is associated to a different operating condition of the system or segment of the time series. Learning jump models from data thus requires both the identification of the local models and the reconstruction of the sequence of active modes. This paper focuses on maximum-a-posteriori identification of jump Box-Jenkins models, under the assumption that the transitions between different modes are driven by a stochastic Markov chain. The problem is addressed by embedding prediction error methods (tailored to Box-Jenkins models with switching coefficients) within a coordinate ascent algorithm, that iteratively alternates between the identification of the local Box-Jenkins models and the reconstruction of the mode sequence
Bounded-Error Identification of Linear Systems with Input and Output Backlash
Full text linkIn this paper we present a single-stage procedure for computing bounds on the parameters of linear systems with input and output backlash from output data corrupted by bounded measurement noise. By properly selecting a sequence of input/output measurements, the problem of evaluating parameter bounds is formulated as a collection of sparse nonconvex optimization problems. Convex-relation techniques are exploited to efficiently compute guaranteed bounds on system parameters by means of semidefinite programming
Identification of hybrid and linear parameter varying models via recursive piecewise affine regression and discrimination
No full textPiecewise affine (PWA) regression is a supervised learning method which aims at estimating, from a set of training data, a PWA map approximating the relationship between a set of explanatory variables (commonly called regressors) and continuous-valued outputs. In this paper, we describe a recursive and numerically efficient PWA regression algorithm, and discuss its application to the identification of multi-input multi-output PWA dynamical models in autoregressive form and to the identification of linear parameter-varying models
Two-stage robot controller auto-tuning methodology for trajectory tracking applications
No full textAutonomy is increasingly demanded of industrial manipulators. Robots have to be capable of regulating their behavior to different operational conditions, without requiring high time/resource-consuming human intervention. Achieving an automated tuning of the control parameters of a manipulator is still a challenging task. This paper addresses the problem of automated tuning of the manipulator controller for trajectory tracking. A Bayesian optimization algorithm is proposed to tune firstly the low-level controller parameters (i.e., robot dynamics compensation), then the high-level controller parameters (i.e., the joint PID gains), providing a two-stage robot controller auto-tuning methodology. In both the optimization phases, the algorithm adapts the control parameters through a data-driven procedure, optimizing a user-defined trajectory tracking cost. Safety constraints ensuring, e.g., closed-loop stability and bounds on the maximum joint position errors, are also included. The performance of the proposed approach is demonstrated on a torque-controlled 7-degree-of-freedom FRANKA Emika robot manipulator. The 4 robot dynamics parameters (i.e., 4 link-mass parameters) are tuned in 40 iterations, while the robot control parameters (i.e., 21 PID gains) are tuned in 90 iterations. Comparable trajectory tracking-errors results with respect to the FRANKA Emika embedded position controller are achieved
Piecewise affine regression via recursive multiple least squares and multicategory discrimination
No full textIn nonlinear regression choosing an adequate model structure is often a challenging problem. While simple models (such as linear functions) may not be able to capture the underlying relationship among the variables, over-parametrized models described by a large set of nonlinear basis functions tend to overfit the training data, leading to poor generalization on unseen data. Piecewise-affine (PWA) models can describe nonlinear and possible discontinuous relationships while maintaining simple local affine regressor-to-output mappings, with extreme flexibility when the polyhedral partitioning of the regressor space is learned from data rather than fixed a priori. In this paper, we propose a novel and numerically very efficient two-stage approach for PWA regression based on a combined use of (i) recursive multi-model least-squares techniques for clustering and fitting linear functions to data, and (ii) linear multi-category discrimination, either offline (batch) via a Newton-like algorithm for computing a solution of unconstrained optimization problems with objective functions having a piecewise smooth gradient, or online (recursive) via averaged stochastic gradient descent
Kalman filtering for energy disaggregation
No full textProviding the users information on the energy consumed in the household at the appliance level is of major importance for increasing their awareness of their consumption behavior. In this paper, we propose a technique based on Kalman filters to estimate the devices’ consumption patterns from aggregate readings, i.e., to solve the so called disaggregation problem. The method is suited for on-line disaggregation and the proposed results show that it is robust against modelling errors and unmodelled appliances
Learning hybrid models with logical and continuous dynamics via multiclass linear separation
No full textHybrid dynamical models are a powerful tool for describing the behaviour of many industrial processes and physical phenomena in which logical (discrete) and analog (continuous) dynamics exist and interact. Black-box identification of hybrid models from input/output observations and no information on the operating mode of the system is a challenging problem, as both the logical and the continuous dynamics must be retrieved. In this work, we consider the identification of discrete hybrid automata (DHA), which represent a mathematical abstraction of hybrid models whose logical dynamics are described by a finite state machine (FSM) and the continuous dynamics are represented through affine discrete-time dynamical models. We propose a two-stage estimation algorithm based on the joint use of clustering, multi-model recursive least-squares and linear multicategory discrimination techniques, which allows us to estimate both the affine models describing the continuous dynamics and the FSM governing the logical dynamics of the system
Recursive Bias-Correction Method for Identification of Piecewise Affine Output-Error Models
No full textLearning PieceWise Affine Output-Error (PWA-OE) models from data requires to estimate a finite set of affine output-error sub-models as well as a partition of the regressors space over which the sub-models are defined. For an output-error type noise structure, the algorithms based on ordinary least squares (LS) fail to compute a consistent estimate of the sub-model parameters. On the other hand, the prediction error methods (PEMs) provide a consistent parameter estimate, however, they require to solve a non-convex optimization problem for which the numerical algorithms may get trapped in a local minimum, leading to inaccurate estimates. In this letter, we propose a recursive bias-correction scheme for identifying PWA-OE models, retaining the computational efficiency of the standard LS algorithms while providing a consistent estimate of the sub-model parameters, under suitable assumptions. The proposed approach allows one to recursively update the estimates of the sub-models parameters and to cluster the regressors. Linear multi-category techniques are then employed to estimate a partition of the regressor space based on the estimated clusters. The performance of the proposed algorithm is demonstrated via an academic example
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