Control Technology and Applications. Control Technology and Applications最新文献

This paper focuses on joint state and parameter estimation in partially observed Boolean dynamical systems (POBDS), a hidden Markov model tailored for modeling complex networks with binary state variables. The majority of current techniques for parameter estimation rely on computationally expensive gradient-based methods, which become intractable in most practical applications with large size of networks. We propose a gradient-free approach that uses Gaussian processes to model the expensive log-likelihood function and utilizes Bayesian optimization for efficient likelihood search over parameter space. Joint state estimation is also achieved alongside parameter estimation using the Boolean Kalman filter. The performance of the proposed method is demonstrated using gene regulatory networks observed through synthetic gene-expression data. The numerical results demonstrate the scalability and effectiveness of the proposed method in the joint estimation of the model parameters and genes' states.

Gene Regulatory Networks (GRNs) are pivotal in governing diverse cellular processes, such as stress response, DNA repair, and mechanisms associated with complex diseases like cancer. The interventions in GRNs aim to restore the system state to its normal condition by altering gene activities over time. Unlike most intervention approaches that rely on the direct observability of the system state and assume no response of the cell against intervention, this paper models the fight between intervention and cell dynamic response using a partially observed zero-sum Markov game with binary state variables. The paper derives a stochastic intervention policy under partial state observability of genes. The optimal Nash equilibrium intervention policy is first obtained for the underlying system. To overcome the challenges of partial state observability, the paper employs the optimal minimum mean-square error (MMSE) state estimator to estimate the system state, given all available information. The proposed intervention policy utilizes the optimal Nash intervention policy associated with the optimal MMSE state estimator. The performance of the proposed method is examined using numerical experiments on the melanoma regulatory network observed through gene-expression data.

This paper presents a new model and phase-variable controller for sit-to-stand motion in above-knee amputees. The model captures the effect of work done by the sound side and residual limb on the prosthesis, while modeling only the prosthetic knee and ankle with a healthy hip joint that connects the thigh to the torso. The controller is parametrized by a biomechanical phase variable rather than time and is analyzed in simulation using the model. We show that this controller performs well with minimal tuning, under a range of realistic initial conditions and biological parameters such as height and body mass. The controller generates kinematic trajectories that are comparable to experimentally observed trajectories in non-amputees. Furthermore, the torques commanded by the controller are consistent with torque profiles and peak values of normative human sit-to-stand motion. Rise times measured in simulation and in non-amputee experiments are also similar. Finally, we compare the presented controller with a baseline proportional-derivative controller demonstrating the advantages of the phase-based design over a set-point based design.

Human gait involves a repetitive cycle of movements, and the phase of gait represents the location in this cycle. Gait phase is measured across many areas of study (e.g., for analyzing gait and controlling powered lower-limb prosthetic and orthotic devices). Current gait phase detection methods measure discrete gait events (e.g., heel strike, flat foot, toe off, etc.) by placing multiple sensors on the subject's lower-limbs. Using multiple sensors can create difficulty in experimental setup and real-time data processing. In addition, detecting only discrete events during the gait cycle limits the amount of information available during locomotion. In this paper we propose a real-time and continuous measurement of gait phase parameterized by a mechanical variable (i.e., phase variable) from a single sensor measuring the human thigh motion. Human subject experiments demonstrate the ability of the phase variable to accurately parameterize gait progression for different walking/running speeds (1 to 9 miles/hour). Our results show that this real-time method can also estimate gait speed from the same sensor.

State-of-art powered prosthetic legs are often controlled using a collection of joint impedance controllers designed for different phases of a walking cycle. Consequently, finite state machines are used to control transitions between different phases. This approach requires a large number of impedance parameters and switching rules to be tuned. Since one set of control parameters cannot be used across different amputees, clinicians spend enormous time tuning these gains for each patient. This paper proposes a virtual constraint-based control scheme with a smaller set of control parameters, which are automatically tuned in real-time using an extremum seeking controller (ESC). ESC, being a model-free control method, assumes no prior knowledge of either the prosthesis or human. Using a singular perturbation analysis, we prove that the virtual constraint tracking errors are small and the PD gains remain bounded. Simulations demonstrate that our ESC-based method is capable of adapting the virtual-constraint based control parameters for amputees with different masses.

Hybrid zero dynamics-based control is a promising framework for controlling underactuated biped robots and powered prosthetic legs. In this control paradigm, stable walking gaits are implicitly encoded in polynomial output functions of the robot configuration variables, which are to be zeroed via feedback. The biped output functions are parameterized by a suitable mechanical phasing variable whose evolution determines the biped gait progression during each step. Determining a proper phase variable, however, might not always be a trivial task. In this paper, we present a method for generating output functions from given parametric walking gaits without any explicit knowledge of the phase variables. Our elimination method is based on computing the resultant of polynomials, an algebraic tool widely used in computer algebra.