Umeå University's logo

Motivated by problems in robotic interaction control, we present a model-based method for robust orbital stabilization. Our objective is to design a time-invariant feedback law for a model of a nonlinear system, or for its digital twin, that makes the distance between its solutions and a planned periodic trajectory decay exponentially. The method uses transverse coordinates, which are functions that vanish on the orbit and remain independent in the firstorder approximation. We regulate the linearized dynamics of transverse coordinates to zero. The novelty of the method is that it replaces the projection-based modification of a stabilizing time-periodic controller with a combination of a time-invariant control law for a subsystem and a discontinuous sliding-mode term. The sliding-mode part forces the state to a switching manifold in finite time and provides robustness to matched uncertainties. We develop a stepby-step procedure and demonstrate its use by an academic example that consists of two masses coupled by a spring and actuated by an external control force. Although the procedure usually requires numerical approximations, this example allows all steps to be carried out analytically. We also discuss the corresponding design for the velocity-controlled case.

This paper addresses the problem of orbital stabilization of a periodic walking gait for a model or a digital twin of a three-link planar biped robot with a single actuator. A Lyapunov equation-based approach is proposed for the synthesis of a stabilizing controller for the corresponding impulsive mechanical system. The method ensures exponential vanishing of transverse coordinates, defining deviations from the nominal periodic trajectory, by solving Lyapunov matrix inequalities, which provide sufficient conditions for orbital stability of the closed-loop dynamics in the nominal case of no disturbances. The proposed approach allows systematic feedback controller design for impulsive systems, taking into account the discontinuities associated with a simplified model of the impact phase of walking. To ensure robustness against matched disturbances, an additional integral sliding mode (ISM) control law is introduced. The ISM component guarantees exact disturbance compensation (for a solution understood in the Filippov’s sense) from the initial moment of motion, ensuring that the perturbed system behaves identically to the nominal model from the very start. Theoretical results are validated through numerical simulations on a model of a three-link biped robot. The obtained results demonstrate that the proposed control law ensures stable periodic walking and significant reduction of deviations from the nominal gait, even under external perturbations.

This pilot study investigates how personalized content can be used in banking applications to encourage shaping good saving habits and increase overall financial literacy among young people. The preliminary result are recommendations including providing clear and personalized saving goals, incorporating educational content on investing and financial planning, and implementing features for tracking and categorizing expenses. Some trade-offs in usage that are presented are to minimize the use of personal data to what is necessary for personalization purposes, treat the data on a group level or increase transparency of data usage. The pilot study concludes that personalization is likely to be beneficial for both banks and their customers, given personal data is handled carefully and used in a sound financial consumer protection framework. 

The paper explores the challenging task of performing a non-prehensile manipulation of several balls synchronously rolling on the curved hands of Butterfly robots. Each Butterfly robot represents a standard benchmark hardware setup, comprising a DC motor rotating a butterfly-shaped frame in a vertical plane, with a ball moving freely upon it, equipped with integrated computer vision, communication, programmable control, and computation interfaces. The combined dynamics of the considered system, consisting of N ≥ 2 such robots, is inherently underactuated, characterized by N active and N passive degrees of freedom, as well as N independent unilateral constraints that model the interactions between the frames and the balls, assuming no slipping. We focus on designing a model-based centralized feedback controller to achieve synchronized rotations of the balls. We assume the accuracy of our mathematical model and the feasibility of implementing a discretized version of the proposed continuous-time controller with a sufficiently small sampling time, that, in particular, is necessary for numerical differentiation. Relying on orbital stability of nominal periodic solution of the closed-loop system, we will experimentally check robustness to various inevitable challenges such as noises, disturbances, uncertainties, and communication delays. Hence, our concentration lies in designing an orbitally stabilizing controller for the underactuated models. The primary contribution is proposing one set of transverse coordinates, enabling transverse-linearization-based controller design, accompanied by pertinent closed-loop system analysis tools, thereby enhancing the efficacy of solving the manipulation task. Analytical and model-based arguments are validated through successful simulations and experiments conducted on two Butterfly robots, thereby emphasizing the validity and practicality of the proposed approach.

Sliding-mode control (SMC) is a powerful robust control design technique that, when appropriately implemented, ensures insensitivity to the so-called matched bounded disturbances and finite-time convergence. However, the insensitivity requires an ideal implementation of discontinuous signals, often based on the sign function that, in practice, results in the presence of parasitic oscillations called chattering. Chattering is unavoidable in systems with SMC, including continuous and higher-order SMC (HOSM) approaches. One of the simplest and commonly used solutions to alleviate chattering is the approximation of the SMC by substituting the sign function with its approximation by a sigmoid function or a saturation function, although this obviously transforms the insensitivity property into a reduction of the influence of the disturbances. In fact, the accuracy of approximating discontinuity creates a trade-off between the reduction of the influence of the disturbances and the amount of chattering. Hence, the following question appears: Is it possible to systematically design a SMC-approximation avoiding a blind search requiring a huge number of numerical and/or hardware experiments? This chapter presents a method to design the boundary-layer parameter of the saturation function. The design is based on the describing function (DF) and harmonic balance (HB) techniques for estimating the parameters of chattering, i.e., frequency and amplitude of the parasitic oscillations.

In an open world with a long-tail distribution of input samples, Deep Neural Networks (DNNs) may make unpredictable mistakes for Out-of-Distribution (OOD) inputs at test time, despite high levels of accuracy obtained during model training. OOD detection can be an effective runtime assurance mechanism for safe deployment of machine learning algorithms in safety–critical applications such as medical imaging and autonomous driving. A large number of OOD detection algorithms have been proposed in recent years, with a wide range of performance metrics in terms of accuracy and execution time. For real-time safety–critical applications, e.g., autonomous driving, timing performance is of great importance in addition to accuracy. We perform a comprehensive and systematic benchmark study of multiple OOD detection algorithms in terms of both accuracy and execution time on different hardware platforms, including a powerful workstation and a resource-constrained embedded device, equipped with both CPU and GPU. We also profile and analyze the internal details of each algorithm to identify the performance bottlenecks and potential for GPU acceleration. This paper aims to provide a useful reference for the practical deployment of OOD detection algorithms for real-time safety–critical applications.

The knowledge of parameters, defining interaction of a robotic manipulator with environment, is crucial when robots execute contact-tasks involving tracking of trajectories while desired forces are applied on the environment. For contact tasks on planar surfaces, the inclination and stiffness of the surface are key parameters since the first one defines the direction of the desired force and trajectory, which are typically defined relative to a frame attached to the environment, and the second one is required to compute the control signal. There exist methods for estimation of inclination and stiffness, whenever they are constant. The estimation of time-varying stiffness and inclination is less studied. In this paper, we propose a method to estimate on-line the stiffness and inclination of the planar surface, when they are varying during the task execution. The method is based on adaptive observers that ensure asymptotic or finite-time convergence of the estimates to the real values of the parameters.

Electro-hydraulic actuators are complex systems with uncertainties in their parameters and disregarded dynamics due to its complexity. This paper presents a disturbance observer-based controller method for the accurate position regulation of an electro-hydraulic actuator. To this aim, a super-twisting algorithm-based observer identifies the plant uncertainties and neglected dynamics, theoretically, in finite-time. Thus, a compensation based controller is designed to counteract the uncertainty and neglected dynamics effects through feedback, improving the position regulation accuracy. The closed-loop analysis is carried out using Lyapunov theory. The feasibility of the controller is validated through high-fidelity simulations and experiments in a forestry crane.

The presence of chattering, i.e. high frequency oscillations with finite amplitude, is unavoidable in systems driven by conventional and higher order sliding mode (HOSM) control. A widely used technique to attenuate chattering is the boundary layer (BL) method; it is based on the approximation of discontinuous terms by a saturation function. However, sliding mode control (SMC) systems with BL approximation still presents chattering due to unmodeled dynamics. Amplitude and frequency of chattering can be estimated applying describing function (DF) and harmonic balance (HB) equation techniques. In this paper, HOSM controllers such as Twisting, Nested second order, and Super Twisting (ST) extension to relative degree two, with BL approximation, are analyzed. The effect of the BL value in the parameters of chattering is studied. When the BL value increments, chattering in systems driven by Twisting and Nested may decrease in amplitude, whereas in the case of ST-extension the variation in amplitude and frequency is minimum. Analysis for the case when the derivative in Twisting and ST-extension algorithms is computed via a linear differentiator is also included. Examples and simulations verifying the results are presented.

Deep Neural Networks (DNNs) are extensively deployed in today's safety-critical autonomous systems thanks to their excellent performance. However, they are known to make mistakes unpredictably, e.g., a DNN may misclassify an object if it is used for perception, or issue unsafe control commands if it is used for planning and control. One common cause for such unpredictable mistakes is Out-of-Distribution (OOD) input samples, i.e., samples that fall outside of the distribution of the training dataset. We present a framework for OOD detection based on outlier detection in one or more hidden layers of a DNN with a runtime monitor based on either Isolation Forest (IF) or Local Outlier Factor (LOF). Performance evaluation indicates that LOF is a promising method in terms of both the Machine Learning metrics of precision, recall, F1 score and accuracy, as well as computational efficiency during testing.