Benito R. Fernandez

Robust fault detection in nonlinear systems using sliding mode observers

A model-based scheme for robust detection and isolation of faults in a twin continuously-stirred tank reactor is presented. The scheme uses sliding mode observers for robust fault detection in the presence of parameter uncertainties in the system model. The fault detection and isolation scheme is validated by simulated faults in the sensors, actuators and plant operating parameters. The fault detection and isolation technique based on sliding mode observers is shown to be robust to parameter uncertainty in the model. The technique also provides a method for estimating the parameter error in the system.<<ETX>>

Active Vibration Control in Structures Using Magnetostrictive Terfenol with Feedback and/or Neural Network Controllers

Experimental results are described in which a rod of magnetostrictive terfenol was used in the dual capacity of a passive structural support element and an active vibration control actu ator and artificial neural networks were used for the adaptive real-time control algorithm. Tests were performed on a three-legged table, where the terfenol actuators mentioned above are the table legs. For the table experiment, shaker vibrations generated in the ground and transmitted to the tabletop (via the legs) were attenuated by counter vibrations synthesized in the table leg actuators. The goal of this experiment was to maintain a quiescent tabletop in the presence of floor vibrations. Utilizing a proportional-integral derivative and a neural network controller, actuated forces were used to cancel applied disturbance forces. The neural network architecture identifies (learns) and adapts to the tabletop forced disturbance through a fast adaptation law known as Adaptive Back Propagation, generating the required counter vibration. The architecture and hence the control was designed to be modular so cross talk (coupling in the control signal) is minimized. This puts an extra burden on the controller to decouple the spillovers but maintains modularity, an important feature for large scale implementations. This article describes work in this area and demonstrates the ability to cancel disturbances from static to the hundred hertz frequency range.

Bond Graph Based Automated Modeling for Computer-Aided Design of Dynamic Systems

This paper introduces research leading to a computer-aided design tool in which engineering designers can test various design concepts (topologies) in an environment equipped to automatically model the dynamics and conveniently optimize the specified components (given the evaluation criteria defined by human designers). A component repository is developed to store not only the component dynamics models, but also other information such as typical component design constraints and physical constitutive laws. In this paper, automated modeling of design configurations is introduced through a design representation called a conceptual dynamics graph (CD graph) and generic models of various components. CD graphs contain the information on how physical components as well as their generic models are topologically connected. A generic component model can accommodate various types of coupling between this component and its environment. This paper also discusses a systematic approach to automatically prepare a mechatronic design problem for the use of optimization to tune the parameters for optimum dynamics. Since genetic algorithms are used for this optimization, this preparation decodes and encodes proper design variables into design genotypes while taking into consideration the design constraints and physical constitutive laws.

A neural network based adaptive fault detection scheme

An adaptive neural network augmented observer for fault detection in nonlinear systems is presented. The key feature of this fault detection scheme is the use of a sliding mode observer to characterize the unmodeled dynamics, and facilitate the training of the neural network. The scheme provides robust fault detection in the presence of modeling errors. The fault detection scheme is validated by simulating faults in a section of a thermal power plant model. Simulations show that the adaptive fault detection scheme learns the unmodeled dynamics, and is able to distinguish between faults, and modeling errors.

Immuno-inspired robotic applications

Graphical abstractDisplay Omitted HighlightsImmunity-based robotic applications are reviewed according to immunological models; old and new.Mathematical details of reported literature are tabulated genealogically.Issues pertaining to validity of immunological models are raised.We have suggested immunological equivalents of various support functions in these applications.Modern trends in robotics are emphasized in conjunction with those in immunology. The ability of artificial immune systems to adapt to varying pathogens makes such systems a suitable choice for various robotic applications. Generally, immunity-based robotic applications map local instantaneous sensory information into either an antigen or a co-stimulatory signal, according to the choice of representation schema. Algorithms then use relevant immune functions to output either evolved antibodies or maturity of dendritic cells, in terms of actuation signals. It is observed that researchers do not try to replicate the biological immunity but select necessary immune functions instead, resulting in an ad-hoc manner these applications are reported. On the other hand, the paradigm shift in robotics research from reactive to probabilistic approaches is also not being reflected in these applications. Authors, therefore, present a detailed review of immuno-inspired robotic applications in an attempt to identify the possible areas to explore. Moreover, the literature has been categorized according to the underlying immuno-definitions. Implementation details have been critically reviewed in terms of corresponding mathematical expressions and their representation schema that include binary, real or hybrid approaches. Limitations of reported applications have also been identified in light of modern immunological interpretations including the danger theory. As a result of this study, authors suggest a renewed focus on innate immunity, action contextualization prior to B/T cell invocation and behavior evolution instead of arbitration. In this context, a multi-tier immunological framework for robotics research, combining innate and adaptive components together is also suggested and skeletonized.

Stabilizing Series-Elastic Point-Foot Bipeds Using Whole-Body Operational Space Control

Whole-body operational space controllers (WBOSCs) are versatile and well suited for simultaneously controlling motion and force behaviors, which can enable sophisticated modes of locomotion and balance. In this paper, we formulate a WBOSC for point-foot bipeds with series-elastic actuators (SEA) and experiment with it using a teen-size SEA biped robot. Our main contributions are on devising a WBOSC strategy for point-foot bipedal robots, 2) formulating planning algorithms for achieving unsupported dynamic balancing on our point-foot biped robot and testing them using a WBOSC, and 3) formulating force feedback control of the internal forces-corresponding to the subset of contact forces that do not generate robot motions-to regulate contact interactions with the complex environment. We experimentally validate the efficacy of our new whole-body control and planning strategies via balancing over a disjointed terrain and attaining dynamic balance through continuous stepping without a mechanical support.

Effect of process uncertainties on generic model control: a geometric approach

Abstract Techniques available in the literature to enhance the robustness of generic model control (GMC) are reviewed. We show that none of these techniques utilize a rigorous analysis of the process-model mismatch. This paper presents GMC from the geometric perspective in order to analyze the effect of process uncertainties in the closed-loop response. The geometric approach defines a performance surface for the closed-loop response with no model error. When the model is not perfect, the system leaves the surface, making the response diffrent from the reference. A technique based on sliding mode control, called sliding generic model control, is developed here to take into account the process uncertainties and to keep the system close to the GMC performance surface.

Robust Phase-Space Planning for Agile Legged Locomotion over Various Terrain Topologies

In this study, we present a framework for phasespace planning and control of agile bipedal locomotion while robustly tracking a set of non-periodic keyframes. By using a reduced-order model, we formulate a hybrid planning framework where the center-of-mass motion is constrained to a general surface manifold. This framework also proposes phase-space bundles to characterize robustness and a robust hybrid automaton to effectively design planning algorithms. A newly defined phasespace locomotion manifold is used as a Riemannian metric to measure the distance between the disturbed state and the planned manifold. Based on this metric, a dynamic programming based hybrid controller is introduced to produce robust locomotions. The robustness of the proposed framework is validated by using simulations of rough terrain locomotion recovery from external disturbances. Additionally, the agility of this framework is demonstrated by using simulations of the dynamic locomotion over random rough terrains.

Phase space planning and robust control for data-driven locomotion behaviors

We utilize here regression tools to plan dynamic locomotion in the Phase Space of the robots center of mass behavior and state feedback controllers to accomplish the desired plans. In real robotic systems, simplified locomotion models and disturbances in the control processes result in deviations from the actual closed loop dynamics with respect to the desired locomotion trajectories. To tackle these challenges, we propose here the use of two control strategies: (1) support vector regression to approximate complex nonlinear center of mass dynamics and plan the feet contact transitions, and (2) sliding mode control to track feet trajectories given the contact timing and location plans. First, support vector regression is utilized to learn a data set obtained through numerical simulation, providing an analytical approximation of the center of mass behavior. To approximate Phase Plane curves, which are characterized by vertical tangents and loop or cyclic behaviors, we use implicit functions for regression as opposed to explicit methods. Based on the proposed regression approximations of the dynamics, we develop contact transition plans and apply robust controllers to converge to the desired feet trajectories. In particular, state feedback controllers might be more convenient than time based controllers in terms of robustness to disturbances. Overall, our methods are capable of learning complex center of mass trajectories and might benefit from the use of robust control techniques. Various case studies are analyzed to validate the effectiveness of the methods including single and multi step planning in a numerical simulation, and swing leg trajectory control on our Hume bipedal robot.

A Mixed Signal (Analog-Digital) Integrator Design

Presented is a design of a mixed signal integrator, consisting of a traditional analog integrator (op-amp with capacitor in feedback) augmented with comparators, logic, counter registers, and a precharged capacitor that can be switched into, and out of, the input of the analog integrator. This device integrates with analog, but stores results in analog and digital. Whenever an analog integrator voltage equals or exceeds a threshold voltage, an amount of charge equivalent to the threshold voltage is cleared from the feedback capacitor of the analog integrator, and the counter register is incremented or decremented accordingly. With the threshold voltage representing one in a counting system, these actions convert the integrator contents to ±1. At any instant, the counter register holds the integer portion of the integral, and the analog integrator holds any fractional portion, permitting the integral to be internally stored as a real number. The mixed signal integrator accumulates integral beyond supply rails without op-amp saturation; has analog integrator bandwidth that extends into the limited gain low frequency region; avoids drift due to low pass filter effects and unbalanced input biases; and exhibits no sampled data or discrete system effects. Tests on a PCB prototype verified the design.