Mustafa Mert Ankarali

Koopman operator recently gained increasing attention in the control systems community for its abilities to bridge linear and nonlinear systems. Data driven Koopman operator approximations have established themselves as key enablers for system identification and model predictive control. Nonetheless, such methods commonly entail a preselected definition of states in the function space leading to high dimensional overfitting models and degraded long term prediction performances. We address this problem by proposing a hierarchical probabilistic approach for the Koopman model identification problem. In our method, elements of the model are treated as random variables and the posterior estimates are found using variational Bayesian (VB) inference updates. Our model distinguishes from others in the integration of inclusion flags. By the help of the inclusion flags, we intuitively threshold the probability of each state in the model. We then propose a graph search based algorithm to reduce the preselected states of the Koopman model. We demonstrate that our reduction method overcomes numerical instabilities and the reduced models maintain performance in simulated and real experiments.

Animals use active sensing movements to shape the spatiotemporal characteristics of sensory signals to better perceive their environment under varying conditions. However, the underlying mechanisms governing the generation of active sensing movements are not known. To address this, we investigated the role of active sensing movements in the refuge tracking behavior of Eigenmannia virescens, a species of weakly electric fish. These fish track the longitudinal movements of a refuge in which they hide by swimming back and forth in a single linear dimension. During refuge tracking, Eigenmannia exhibits stereotyped whole-body oscillations when the quality of the sensory signals degrades. We developed a closed-loop feedback control model to examine the role of these ancillary movements on the task performance. Our modeling suggests that fish may use active sensing to minimize predictive uncertainty in state estimation during refuge tracking. The proposed model generates simulated fish trajectories that are statistically indistinguishable from that of the actual fish, unlike the open-loop noise generator and stochastic resonance generator models in the literature. These findings reveal the significance of closed-loop control in active sensing behavior, offering new insights into the underlying mechanisms of dynamic sensory modulation.

This study demonstrates a cascaded model predictive control (C-MPC) method for input constrained control of underactuated planar bipedal walking with any predefined sta-bilizable trajectory. Our approach aims to increase the trajectory tracking performance of the system and produce realistic and applicable responses while respecting friction constraints and considering impact dynamics. Primarily focusing on zero dynamics with PD (ZD+PD) control, this proposed control method constitutes a second layer controller on top of the well-known trajectory tracking controllers for underactuated bipedal walking systems. We successfully implement this model-based controller and test against large modeling errors, noises, and disturbances, where the conventional ZD+PD control fails to maintain stability.

Animals use active sensing movements to shape the spatiotemporal characteristics of sensory signals to better perceive their environment under varying conditions. However, the underlying mechanisms governing the generation of active sensing movements are not known. To address this, we investigated the role of active sensing movements in the refuge tracking behavior of Eigenmannia virescens , a species of weakly electric fish. These fish track the longitudinal movements of a refuge in which they hide by swimming back and forth in a single linear dimension. During refuge tracking, Eigenmannia exhibits stereotyped whole-body oscillations when the quality of the sensory signals degrades. We developed a feedback control model to examine the role of these ancillary movements on the task performance. Here, we show that the proposed model generates fish trajectories that are statistically indistinguishable from the actual fish, implying that active sensing movements are regulated to minimize the predictive uncertainty in state estimation.

In this manuscript, we present a novel method for estimating the stochastic stability characteristics of metastable legged systems using the unscented transformation. Prior methods for stability analysis in such systems often required high-dimensional state space discretization and a broad set of initial conditions, resulting in significant computational complexity. Our approach aims to alleviate this issue by reducing the dimensionality of the system and utilizing the unscented transformation to estimate the output distribution. This technique allows us to account for multiple sources of uncertainty and high-dimensional system dynamics, while leveraging prior knowledge of noise statistics to inform the selection of initial conditions for experiments. As a result, our method enables the efficient assessment of controller performance and analysis of parametric dependencies with fewer experiments. To demonstrate the efficacy of our proposed method, we apply it to the analysis of a one-dimensional hopper and an underactuated bipedal walking simulation with a hybrid zero dynamics controller.

Unmanned Surface Vehicles (USV) have gained significant attention in military, science, and research applications in recent years. The development of new USV systems and increased application domain of these platforms has necessitated the development of new motion planning methods to improve the autonomy level of USVs and provide safe and robust navigation across unpredictable marine environments. This study proposes a feedback motion planning and control methodology for dynamic fully-and under-actuated USV models built on the recently introduced sparse random neighborhood graphs and constrained nonlinear Model Predictive Control (MPC). This approach employs a feedback motion planning strategy based on sparsely connected obstacle-free regions and the sequential composition of MPC policies. The algorithm generates a sparse neighborhood graph consisting of connected rectangular zones in the discrete planning phase. Inside each node (rectangular region), an MPC-based online feedback control policy funnels the USV with nonlinear dynamics from one rectangle to the other in the network, ensuring no constraint violation on state and input variables occurs. We systematically test the proposed algorithms in different simulation scenarios, including an extreme actuator noise scenario, to test the algorithm’s validity, effectiveness, and robustness.

In this manuscript, we present a novel method for estimating the stochastic stability characteristics of metastable legged systems using the unscented transformation. Prior methods for stability analysis in such systems often required high-dimensional state space discretization and a broad set of initial conditions, resulting in significant computational complexity. Our approach aims to alleviate this issue by reducing the dimensionality of the system and utilizing the unscented transformation to estimate the output distribution. This technique allows us to account for multiple sources of uncertainty and high-dimensional system dynamics, while leveraging prior knowledge of noise statistics to inform the selection of initial conditions for experiments. As a result, our method enables the efficient assessment of controller performance and analysis of parametric dependencies with fewer experiments. To demonstrate the efficacy of our proposed method, we apply it to the analysis of a one-dimensional hopper and an underactuated bipedal walking simulation with a hybrid zero dynamics controller.

Bu çalışmada, sensörsüz elastik seri tekniği ile kablo sürümlü bir düzlemsel mekanizmanın kontrolü incelenmektedir. Kablo sürümlü robotlar, gösteri alanlarında örümcek kamera olarak yatay düzlemde çalışan şekilde hali hazırda kullanılmaktadırlar. Bu çalışmada ise düşey düzlemde çalışan bir kablo sürümlü robot üzerinde çalışılmaktadır. Mekanizmanın düşey düzlemde çalışması sebebiyle de örümcek kameralardaki gibi her motora eşit kuvvet uygulanmamaktadır. Kablo sürümlü robotların kontrolü için kullanılan, sensörlerden alınan bilgilerin işlenmesine dayalı yöntemleri kullanmak yerine, sensörlerden elde edilen bilgilerde oluşabilecek herhangi bir gecikmeden veya hatadan etkilenmemek için bu çalışmada sensörsüz bir kontrol tekniği kullanılmaktadır. Sırasıyla, mekanizmanın mekanik, donanım ve yazılım kısımlarının açıklanmasının ardından, kontrol stratejimizin matematiksel olarak derinlemesine çalışılacağı modelleme ve varsayımlar sunulmaktadır. Çalışmanın sonuç bölümünde simülasyon ve gerçek çıktılar karşılaştırılmaktadır.