Design Optimization, Analysis, and Control of Walking Robots

Passive dynamic walking refers to the dynamical behavior of mechanical devices that are able to naturally walk down a shallow slope in a stable manner, without using actuation or sensing of any kind. Such devices can attain motions that are remarkably human-like by purely exploiting their natural dynamics. This suggests that passive dynamic walking machines can be used to model and study human locomotion; however, there are two major limitations: they can be difficult to design, and they cannot walk on level ground or uphill without some kind of actuation. This thesis presents a mechanism design optimization framework that allows the designer to find the best design parameters based on the chosen performance metric(s). The optimization is formulated as a convex problem, where its solutions are globally optimal and can be obtained efficiently. To enable locomotion on level ground and uphill, this thesis studies a robot based on a passive walker: the rimless wheel with an actuated torso. We design and validate two control policies for the robot through the use of scalable methodology based on tools from mathematical analysis, optimization theory, linear algebra, differential equations, and control theory

Robust and Decentralized Control of Web Winding Systems

This research addresses the velocity and tension regulation problems in web handling, including those found in the single element of an accumulator and those in the large-scale system settings. A continuous web winding system is a complex large-scale interconnected dynamics system with numerous tension zones to transport the web while processing it. A major challenge in controlling such systems is the unexpected disturbances that propagate through the system and affect both tension and velocity loops along the way. To solve this problem, a unique active disturbance rejection control (ADRC) strategy is proposed. Simulation results show remarkable disturbance rejection capability of the proposed control scheme in coping with large dynamic variations commonly seen in web winding systems. Another complication in web winding system stems from its large-scale and interconnected dynamics which makes control design difficult. This motivates the research in formulating a novel robust decentralized control strategy. The key idea in the proposed approach is that nonlinearities and interactions between adjunct subsystems are regarded as perturbations, to be estimated by an augmented state observer and rejected in the control loop, therefore making the local control design extremely simple. The proposed decentralized control strategy was implemented on a 3-tension-zone web winding processing line. Simulation results show that the proposed control method leads to much better tension and velocity regulation quality than the existing controller common in industry. Finally, this research tackles the challenging problem of stability analysis. Although ADRC has demonstrated the validity and advantage in many applications, the rigorous stability study has not been fully addressed previously. To this end, stability characterization of ADRC is carried out in this work. The closed-loop system is first reformulated, resulting in a form that allows the application of the well established singular perturbation method. Based on the decom

Robust and Decentralized Control of Web Winding Systems

This research addresses the velocity and tension regulation problems in web handling, including those found in the single element of an accumulator and those in the large-scale system settings. A continuous web winding system is a complex large-scale interconnected dynamics system with numerous tension zones to transport the web while processing it. A major challenge in controlling such systems is the unexpected disturbances that propagate through the system and affect both tension and velocity loops along the way. To solve this problem, a unique active disturbance rejection control (ADRC) strategy is proposed. Simulation results show remarkable disturbance rejection capability of the proposed control scheme in coping with large dynamic variations commonly seen in web winding systems. Another complication in web winding system stems from its large-scale and interconnected dynamics which makes control design difficult. This motivates the research in formulating a novel robust decentralized control strategy. The key idea in the proposed approach is that nonlinearities and interactions between adjunct subsystems are regarded as perturbations, to be estimated by an augmented state observer and rejected in the control loop, therefore making the local control design extremely simple. The proposed decentralized control strategy was implemented on a 3-tension-zone web winding processing line. Simulation results show that the proposed control method leads to much better tension and velocity regulation quality than the existing controller common in industry. Finally, this research tackles the challenging problem of stability analysis. Although ADRC has demonstrated the validity and advantage in many applications, the rigorous stability study has not been fully addressed previously. To this end, stability characterization of ADRC is carried out in this work. The closed-loop system is first reformulated, resulting in a form that allows the application of the well established singular perturbation method. Based on the decom

Data-Driven Passivity-Based Control of Underactuated Robotic Systems

Classical control strategies for robotic systems are based on the idea that feedback control can be used to override the natural dynamics of the machines. Passivity-based control (Pbc) is a branch of nonlinear control theory that follows a similar approach, where the natural dynamics is modified based on the overall energy of the system. This method involves transforming a nonlinear control system, through a suitable control input, into another fictitious system that has desirable stability characteristics. The majority of Pbc techniques require the discovery of a reasonable storage function, which acts as a Lyapunov function candidate that can be used to certify stability. There are several challenges in the design of a suitable storage function, including: 1) what a reasonable choice for the function is for a given control system, and 2) the control synthesis requires a closed-form solution to a set of nonlinear partial differential equations. The latter is in general difficult to overcome, especially for systems with high degrees of freedom, limiting the applicability of Pbc techniques. A machine learning framework that automatically determines the storage function for underactuated robotic systems is introduced in this dissertation. This framework combines the expressive power of neural networks with the systematic methods of the Pbc paradigm, bridging the gap between controllers derived from learning algorithms and nonlinear control theory. A series of experiments demonstrates the efficacy and applicability of this framework for a family of underactuated robots

Lack of Pattern Separation in Sensory Inputs to the Olfactory Bulb during Perceptual Learning.

Recent studies revealed changes in odor representations in the olfactory bulb during active olfactory learning (Chu et al., 2016; Yamada et al., 2017). Specifically, mitral cell ensemble responses to very similar odorant mixtures sparsened and became more distinguishable as mice learned to discriminate the odorants over days (Chu et al., 2016). In this study, we explored whether changes in the sensory inputs to the bulb underlie the observed changes in mitral cell responses. Using two-photon calcium imaging to monitor the odor responses of the olfactory sensory neuron (OSN) axon terminals in the glomeruli of the olfactory bulb during a discrimination task, we found that OSN inputs to the bulb are stable during discrimination learning. During one week of training to discriminate between very similar odorant mixtures in a Go/No-go task, OSN responses did not show significant sparsening, and the responses to the trained similar odorants did not diverge throughout training. These results suggest that the adaptive changes of mitral cell responses during perceptual learning are ensured by mechanisms downstream of OSN input, possibly in local circuits within olfactory bulb

Assessing Trust in Online Collaboration in E-government during the COVID-19 pandemic: An Employee Perspective

Due to the outbreak of the COVID-19 pandemic, firms and institutions have to shift to work from home to prevent the spreading of the pandemic. As a public sector, employees in government institutions also collaborate online during the lockdown. Collaboration online has been identified as a challenge for employees. While our understanding of how employees’ perception and trust of the e-government is still limited. To address this research gap, this study intends to investigate the antecedents of employees’ trust in e-government during their work process in the new normal. By conducting a qualitative study with 14 in-depth interviews with employees with e-government experience during the COVID-19 pandemic, we extracted several key antecedents of employees’ trust in e-government. Based on the qualitative data analysis, a theoretical model of trust antecedents was proposed. Our study provides a deep understanding of the specific antecedents of employees’ trust in the e-government context

Data-Driven Design of Energy-Shaping Controllers for Swing-Up Control of Underactuated Robots

We propose a novel data-driven procedure to train a neural network for the swing-up control of underactuated robotic systems. Our approach is inspired by several recent developments ranging from nonlinear control theory to machine learning. We embed a neural network indirectly into the equations of motion of the robotic manipulator as its control input. Using familiar results from passivity-based and energy-shaping control literature, this control function is determined by the appropriate gradients of a neural network, acting as an energy-like (Lyapunov) function. We encode the task of swinging-up robotic systems through the use of transverse coordinates and goal sets; which drastically accelerates the rate of learning by providing a concise target for the neural network. We demonstrate the efficacy of the algorithm with both numerical simulations and experiments

Controllers, observers, and applications thereof

Controller scaling and parameterization are described. Techniques that can be improved by employing the scaling and parameterization include, but are not limited to, controller design, tuning and optimization. The scaling and parameterization methods described here apply to transfer function based controllers, including PID controllers. The parameterization methods also apply to state feedback and state observer based controllers, as well as linear active disturbance rejection (ADRC) controllers. Parameterization simplifies the use of ADRC. A discrete extended state observer (DESO) and a generalized extended state observer (GESO) are described. They improve the performance of the ESO and therefore ADRC. A tracking control algorithm is also described that improves the performance of the ADRC controller. A general algorithm is described for applying ADRC to multi-input multi-output systems. Several specific applications of the control systems and processes are disclosed

Convex multi-criteria design optimization of robotic manipulators via sum-of-squares programming

This paper presents a general framework for optimization of robotic manipulators via sums-of-squares (SoS) programming (semidefinite convex optimization) with multiple design objectives. Both kinematic and dynamic performance measures are discussed and an optimization problem for a proof-of-concept robotic manipulator has been formulated. SoS programming is shown to promise advantages as it can provide globally optimal results up to machine precision and scales much better with respect to the number of design variables than other methods which can obtain globally optimal solutions