Actuator Noise Research Articles

Active disturbance rejection control of drag-free satellites considering the effect of micro-propulsion noise

The space gravitational wave detection mission requires a super "static and precise" scientific experiment environment. In order to solve the non-conservative force disturbance variation and the actuator noise and measurement noise, this paper designs a drag-free control scheme based on active disturbance rejection control (ADRC) framework to achieve the high-precision index. According to the ultra-high accuracy, low bandwidth limitation, and robustness requirements of drag-free satellite, the H∞ controller satisfying the robustness constraint is designed as an active disturbance rejection feedback controller to achieve the high-precision index. Meanwhile, the non-conservative force disturbance with a wide range of variations is estimated and feedforward compensated by an extended state observer to improve the system robustness. Simulation results show that the control system can achieve the relative displacement of 2nm/Hznm/Hz1/2 for the drag-free satellite platform and the residual acceleration of 1×10-15 m/sm/s2/Hz1/2 for the test mass.

Towards a Constructive Framework for Control Theory

This work presents a framework for control theory based on constructive analysis to account for discrepancy between mathematical results and their implementation in a computer, also referred to as computational uncertainty. In control engineering, the latter is usually either neglected or considered submerged into some other type of uncertainty, such as system noise, and addressed within robust control. However, even robust control methods may be compromised when the mathematical objects involved in the respective algorithms fail to exist in exact form and subsequently fail to satisfy the required properties. For instance, in general stabilization using a control Lyapunov function, computational uncertainty may distort stability certificates or even destabilize the system despite robustness of the stabilization routine with regards to system, actuator and measurement noise. In fact, battling numerical problems in practical implementation of controllers is common among control engineers. Such observations indicate that computational uncertainty should indeed be addressed explicitly in controller synthesis and system analysis. The major contribution here is a fairly general framework for proof techniques in analysis and synthesis of control systems based on constructive analysis which explicitly states that every computation be doable only up to a finite precision thus accounting for computational uncertainty. A series of previous works is overviewed, including constructive system stability and stabilization, approximate optimal controls, eigenvalue problems, Caratheodory trajectories, measurable selectors. Additionally, a new constructive version of the Danskin's theorem, which is crucial in adversarial defense, is presented.

Time-Scale Redesign-Based Saturated Controller Synthesis for a Class of MIMO Nonlinear Systems

A consideration of actuator saturation is an important aspect to study the effectiveness of a designed controller in practice. However, the conventional Lyapunov theory-based design is not always suitable to analyze the quantitative behavior of closed-loop system. This article presents a time-scale redesign-based saturated tracking controller for a class of feedback linearizable multi-input–multi-output (MIMO) nonlinear systems. The proposed controller is built upon the frameworks of contraction and partial contraction theories which ensures that bounded tracking performance as well as quantify the steady-state error bounds in terms of the various control design parameters. Notably, in contrast to the existing Lyapunov-method-based designs, the proposed approach allows to tune the controller performance without arbitrary reduction of singular perturbation parameters. Therefore, the vulnerability of the controller toward actuator saturation and noise, due to the ill-effects of high-gains stemming from the conventional high-gain controllers, are reduced. The extensive experimental results using a wheeled mobile robot are provided to demonstrate the effectiveness of the proposed controller.

IEEE SSCI 2020

The guidance of a large swarm is a challenging control problem. Shepherding offers one approach to guide a large swarm using a few shepherding agents (sheepdogs). While noise is an inherent characteristic in many real-world problems, the impact of noise on shepherding is not a well-studied problem. We study two forms of noise. First, we evaluate noise in the sensorial information received by the shepherd about the location of sheep. Second, we evaluate noise in the ability of the sheepdog to influence sheep due to disturbance forces occurring during actuation. We study both types of noise in this paper, and investigate the performance of Str\"{o}mbom's approach under these actuation and perception noises. To ensure that the parameterisation of the algorithm creates a stable performance, we need to run a large number of simulations, while increasing the number of random episodes until stability is achieved. We then systematically study the impact of sensorial and actuation noise on performance. Str\"{o}mbom's approach is found to be more sensitive to actuation noise than perception noise. This implies that it is more important for the shepherding agent to influence the sheep more accurately by reducing actuation noise than attempting to reduce noise in its sensors. Moreover, different levels of noise required different parameterisation for the shepherding agent, where the threshold needed by an agent to decide whether or not to collect astray sheep is different for different noise levels.

Eco-Driving Optimal Controller for Autonomy Tracking of Two-Wheel Electric Vehicles

The eco-driving profiles are algorithms able to use additional information in order to create recommendations or limitation over the driver capabilities. They increase the autonomy of the vehicle but currently their usage is not related to the autonomy required by the driver. For this reason, in this paper, the eco-driving challenge is translated into two-layer optimal controller designed for pure electric vehicles. This controller is oriented to ensure that the energy available is enough to complete a demanded trip, adding speed limits to control the energy consumption rate. The mechanical and electrical models required are exposed and analyzed. The cost function is optimized to correspond to the needs of each trip according to driver behavior, vehicle, and traject information. The optimal controller proposed in this paper is a nonlinear model predictive controller (NMPC) associated with a nonlinear unidimensional optimization. The combination of both algorithms allows increasing around 50% the autonomy with a limitation of the 30% of the speed and acceleration capabilities. Also, the algorithm is able to ensure a final autonomy with a 1.25% of error in the presence of sensor and actuator noise.

Orbit design and thruster requirement for various constant arm space mission concepts for gravitational-wave observation

In previous papers, we have addressed the issues of orbit design and thruster requirement for the constant arm versions of Astrodynamical Middle-frequency Interferometric Gravitational-wave Observatory (AMIGO) mission concept and for the constant arm gravitational wave (GW) mission concept of Atom Interferometric Gravitational-wave Space Observatory (AIGSO). In this paper, we apply similar methods to the orbit design and thruster requirement for the constant arm GW missions B-DECIGO and DECIGO, and estimate the yearly propellant requirements at the specific impulse [Formula: see text][Formula: see text]s and [Formula: see text][Formula: see text]s. For the geocentric orbit options of B-DECIGO which we have explored, the fuel mass requirement is a concern. For the heliocentric orbit options of B-DECIGO and DECIGO, the fuel requirement to keep the arm equal and constant should be easily satisfied. Furthermore, we explore the thruster and propellant requirements for constant arm versions of LISA and TAIJI missions and find the fuel mass requirement is not a show stopper either. The proof mass actuation noise is a concern. To have enough dynamical range, an alternate proof mass is required. Detailed laboratory study is warranted.

LISA Pathfinder Performance Confirmed in an Open-Loop Configuration: Results from the Free-Fall Actuation Mode.

We report on the results of the LISA Pathfinder (LPF) free-fall mode experiment, in which the control force needed to compensate the quasistatic differential force acting on two test masses is applied intermittently as a series of “impulse” forces lasting a few seconds and separated by roughly 350 s periods of true free fall. This represents an alternative to the normal LPF mode of operation in which this balancing force is applied continuously, with the advantage that the acceleration noise during free fall is measured in the absence of the actuation force, thus eliminating associated noise and force calibration errors. The differential acceleration noise measurement presented here with the free-fall mode agrees with noise measured with the continuous actuation scheme, representing an important and independent confirmation of the LPF result. An additional measurement with larger actuation forces also shows that the technique can be used to eliminate actuation noise when this is a dominant factor.

Learning Navigation Behaviors End-to-End With AutoRL

We learn end-to-end point-to-point and path-following navigation behaviors that avoid moving obstacles. These policies receive noisy lidar observations and output robot linear and angular velocities. The policies are trained in small, static environments with AutoRL, an evolutionary automation layer around reinforcement learning (RL) that searches for a deep RL reward and neural network architecture with large-scale hyper-parameter optimization. AutoRL first finds a reward that maximizes task completion and then finds a neural network architecture that maximizes the cumulative of the found reward. Empirical evaluations, both in simulation and on-robot, show that AutoRL policies do not suffer from the catastrophic forgetfulness that plagues many other deep reinforcement learning algorithms, generalize to new environments and moving obstacles, are robust to sensor, actuator, and localization noise, and can serve as robust building blocks for larger navigation tasks. Our path-following and point-to-point policies are, respectively, 23% and 26% more successful than comparison methods across new environments.

Design of an online-tuned model based compound controller for a fully automated artificial pancreas.

This paper deals with the development of a control algorithm that can predict optimal insulin doses without patients' intervention in fully automated artificial pancreas system. An online-tuned model based compound controller comprising an online-tuned internal model control (IMC) algorithm and an enhanced IMC (eIMC) algorithm along with a meal detection module is proposed. Volterra models, used to develop IMC and eIMC algorithms, are developed online using recursive least squares (RLS) filter. The time domain kernels, computed online using RLS filter, are converted into frequency domain to obtain Volterra transfer function (VTF). VTFs are used to develop both IMC and eIMC algorithms. The compound controller is designed in such a way that eIMC predicts insulin doses when the glucose rate increase detector of meal detection module is positive, otherwise conventional IMC takes the control action. Experimental results show that the compound controller performs robustly in the presence of higher and irregular amounts of meal disturbances at random times, very high actuator and sensor noises and also with the variation in insulin sensitivity. The combination of compound control strategy and meal detection module compensates the shortcomings of both slow subcutaneous insulin action that causes postprandial hyperglycemia, and delayed peak of action that causes hypoglycaemia. Graphical Abstract A fully-automated artificial pancreas system containing glucose sensor, insulin pump and control algorithm. Block diagram showing the control algorithm i.e., online-tuned compound IMC comprising enhanced IMC, conventional IMC and meal detection module, developed in the present work.

An untethered soft chemo-mechanical robot with composite structure and optimized control

Endowing the animals with agile actions, natural muscle functions as a soft actuator, which is powered by chemical reactions of metabolism and controlled by electrical nerve-signals. Inspired by natural muscle, various types of soft chemical machines (SCM) have been recently developed, possessing advantages of light weight, fast actuation, compact integration, high power and low noise. In this paper, we propose a composite structure as a soft chemical engine (SCE), which is driven by the chemical decomposition of hydrogen peroxide (H2O2). The SCE directly converts chemical energy into mechanical work, achieving excellent performance of large actuation (∼33% volume expansion) and fast response (∼1 s). We advance the SCE with excellent performance to a soft chemical machine (SCM) with well-controlled functions, as a mechatronic control system. The soft robot possesses robust control with untethered operation and stable actuating cycles. We develop a model for the locomotion mechanisms, revealing that optimized mechatronic controls can significantly enhance the performance of SCM.

A new combined actuator fault estimation and accommodation for linear parameter varying system subject to simultaneous and multiple faults: an LMIs approach

This paper proposes a novel fuzzy observer scheme-based active fuzzy fault-tolerant tracking control (FFTTC) combined with a parallel distributed compensation control law for a linear parameter varying system subject to actuator faults and noise measurement. This system consists of a doubly fed induction generator-based wind energy conversion system described by a Takagi–Sugeno fuzzy model. The developed scheme is able to force system states to track their desired reference signal in finite time and obtain a better dynamic response and performance. A fuzzy state observer is also developed to simultaneously estimate the generator states and the actuator faults. Based on the obtained online fault estimation information, the FFTTC will be reconfigured to compensate for the fault impact. The closed-loop stability is established based on a Lyapunov function. Sufficient conditions are formulated for the existence of fuzzy fault-tolerant control parameters in the form of linear matrix inequalities (LMIs), which can be easily solved by means of a standard YALMIP toolbox. The fuzzy observer and fuzzy fault-tolerant tracking control gains can be computed based on the solutions of a set of LMIs. Computer simulations were performed using the MATLAB/Simulink environment to highlight the performance and robustness of the proposed design procedure with respect to actuator fault occurrences.

An mHealth System for Monitoring Medication Adherence in Obstructive Respiratory Diseases Using Content Based Audio Classification

Asthma and chronic obstructive pulmonary disease are obstructive respiratory diseases that affect negatively the quality of life for patients and their families worldwide. Despite the significance of these diseases, their management has been considered suboptimal around the world, whereas the improper inhaler use has been underlined as one of the main causes. Toward this direction, this paper presents an integrated mHealth system that provides real-time personalized feedback to patients for assessing the proper medication use and educating them and helping them avoid common mistakes. The identification of proper inhaler use is based on conventional and data-driven feature extraction and classification methods employed for the identification of four events (inhaler actuation, inhalation, exhalation, and background noise). The proposed scheme reaches 98% classification accuracy significantly outperforming recent and relevant state-of-the-art approaches. Finally, intuitive feedback interfaces were implemented in the form of a virtual guidance agent integrated with the mobile application, which can help patients follow their action plan and assess their inhaler technique in a more engaging manner. Extensive simulation studies, carried out using 12 subjects, demonstrated the efficiency of the proposed approaches in both indoor and outdoor environments.

A Real-Time Smooth Weighted Data Fusion Algorithm for Greenhouse Sensing Based on Wireless Sensor Networks.

Wireless sensor networks are widely used to acquire environmental parameters to support agricultural production. However, data variation and noise caused by actuators often produce complex measurement conditions. These factors can lead to nonconformity in reporting samples from different nodes and cause errors when making a final decision. Data fusion is well suited to reduce the influence of actuator-based noise and improve automation accuracy. A key step is to identify the sensor nodes disturbed by actuator noise and reduce their degree of participation in the data fusion results. A smoothing value is introduced and a searching method based on Prim’s algorithm is designed to help obtain stable sensing data. A voting mechanism with dynamic weights is then proposed to obtain the data fusion result. The dynamic weighting process can sharply reduce the influence of actuator noise in data fusion and gradually condition the data to normal levels over time. To shorten the data fusion time in large networks, an acceleration method with prediction is also presented to reduce the data collection time. A real-time system is implemented on STMicroelectronics STM32F103 and NORDIC nRF24L01 platforms and the experimental results verify the improvement provided by these new algorithms.

How a life-like system emerges from a simple particle motion law.

Self-structuring patterns can be observed all over the universe, from galaxies to molecules to living matter, yet their emergence is waiting for full understanding. We discovered a simple motion law for moving and interacting self-propelled particles leading to a self-structuring, self-reproducing and self-sustaining life-like system. The patterns emerging within this system resemble patterns found in living organisms. The emergent cells we found show a distinct life cycle and even create their own ecosystem from scratch. These structures grow and reproduce on their own, show self-driven behavior and interact with each other. Here we analyze the macroscopic properties of the emerging ecology, as well as the microscopic properties of the mechanism that leads to it. Basic properties of the emerging structures (size distributions, longevity) are analyzed as well as their resilience against sensor or actuation noise. Finally, we explore parameter space for potential other candidates of life. The generality and simplicity of the motion law provokes the thought that one fundamental rule, described by one simple equation yields various structures in nature: it may work on different time- and size scales, ranging from the self-structuring universe, to emergence of living beings, down to the emergent subatomic formation of matter.

Adaptive behaviors in multi-agent source localization using passive sensing.

In this paper, the role of adaptive group cohesion in a cooperative multi-agent source localization problem is investigated. A distributed source localization algorithm is presented for a homogeneous team of simple agents. An agent uses a single sensor to sense the gradient and two sensors to sense its neighbors. The algorithm is a set of individualistic and social behaviors where the individualistic behavior is as simple as an agent keeping its previous heading and is not self-sufficient in localizing the source. Source localization is achieved as an emergent property through agent’s adaptive interactions with the neighbors and the environment. Given a single agent is incapable of localizing the source, maintaining team connectivity at all times is crucial. Two simple temporal sampling behaviors, intensity-based-adaptation and connectivity-based-adaptation, ensure an efficient localization strategy with minimal agent breakaways. The agent behaviors are simultaneously optimized using a two phase evolutionary optimization process. The optimized behaviors are estimated with analytical models and the resulting collective behavior is validated against the agent’s sensor and actuator noise, strong multi-path interference due to environment variability, initialization distance sensitivity and loss of source signal.

A prototype piecewise-linear dynamic attenuator

The piecewise-linear dynamic attenuator has been proposed as a mechanism in CT scanning for personalizing the x-ray illumination on a patient- and application-specific basis. Previous simulations have shown benefits in image quality, scatter, and dose objectives. We report on the first prototype implementation. This prototype is reduced in scale and speed and is integrated into a tabletop CT system with a smaller field of view (25 cm) and longer scan time (42 s) compared to a clinical system. Stainless steel wedges were machined and affixed to linear actuators, which were in turn held secure by a frame built using rapid prototyping technologies. The actuators were computer-controlled, with characteristic noise of about 100 microns. Simulations suggest that in a clinical setting, the impact of actuator noise could lead to artifacts of only 1 HU. Ring artifacts were minimized by careful design of the wedges. A water beam hardening correction was applied and the scan was collimated to reduce scatter. We scanned a 16 cm water cylinder phantom as well as an anthropomorphic pediatric phantom. The artifacts present in reconstructed images are comparable to artifacts normally seen with this tabletop system. Compared to a flat-field reference scan, increased detectability at reduced dose is shown and streaking is reduced. Artifacts are modest in our images and further refinement is possible. Issues of mechanical speed and stability in the challenging clinical CT environment will be addressed in a future design.

Model reference adaptive PID control with anti-windup compensator for an autonomous underwater vehicle

Model uncertainty and saturation in actuators are among some of the practical challenges in the controller design of autonomous vehicles. Incorporating adaptive control with anti-windup (AW) compensators can provide a convenient combination to counteract the challenge. In this manuscript, an adaptive control with a dynamic anti-windup compensator is proposed for an Autonomous Underwater Vehicle (AUV). Due to industrial and academic interests, the proposed method is embedded with a Proportional–Derivative–Integral (PID) controller. A modern AW technique is employed to cope with the saturation problem. Typical performance of the adaptive control system is achieved in the absence of actuator saturation. The performance is shown to degrade when the saturation has occurred. However the quality of the adaptive controller is improved when it is combined with an anti-windup compensator. Primarily six degrees of freedom (DOF) nonlinear motion equations of the vehicle are derived. Then, the proposed scheme is applied to this nonlinear model. Performance of the modified system is compared by the baseline controller. The effectiveness of the presented method in the presence of the actuator saturation, considering uncertainties, noise and disturbance is assessed and verified through simulation scenarios.

Autonomous Multiagent Space Exploration with High-Level Human Feedback

Robotic space-exploration missions have always pushed the limits of science and technology, and will continue to do so by their very nature. Such missions are particularly challenging, as they operate in environments with high uncertainty, light-time delays, and high mission costs. Artificial-intelligence-based multiagent systems can alleviate these concerns by 1) creating autonomous multirobot teams that can function in uncertain environments, 2) navigating and operating without time-sensitive commands from Earth-bound scientists, and 3) spreading the mission cost across multiple platforms that will eliminate the danger of total mission loss in the case of a malfunctioning robot. In this work, a novel human in-the-loop cooperative coevolutionary algorithm is presented to train a multirobot system exploring an unknown environment. Autonomous robots learn to make low-level control decisions to maximize scientific data acquisition, whereas human scientists on Earth learn the changing mission profiles and provide high-level objectives to the robots. Results demonstrate that the algorithm reduces the number of robots needed for a particular performance level tenfold compared to traditional cooperative coevolutionary algorithms for configurations of 10 or more rovers, resulting in significantly lower mission costs. Further, the trained multirobot system is extremely robust to noise, and 10% sensor and actuator noise (with and without sensor bias) has no statistically significant impact on system performance. Finally, the system is extremely robust to robot failures; for any percentage of robot failures between 10 and 90%, the percentage loss in performance is less than the percentage of failed robots.

Using modelling and simulation to improve dynamic balancing of biped mobile robots

Dynamic balancing is a hard problem for biped mobile robots, as is requires real-time processing of complex formulas from noisy sensors. In this paper we demonstrate how an offline modelling and simulation step can help to improve balancing, by providing a first approximation for a biped walking pattern. In order to achieve this goal, we design a physics model of the biped robot, using spline functions for all joint movements. Then we optimise the system parameters using genetic algorithms with the help of a rigid body simulation library. Transferring the evolved control system from the simulation to the physical world poses a number of challenges, especially because of complex sensor and actuator noise in the real world and inaccuracies in the physics model. Methods to minimise these problems are the injection of artificial noise into the simulation process and/or the use of multiple dynamic simulation systems simultaneously. The larger variance in the resulting simulation will result in a generally smaller reality gap and subsequently a control algorithm that is more easily adapted to a real robot.

Motion Planning of Uncertain Ordinary Differential Equation Systems

This work presents a novel motion planning framework, rooted in nonlinear programming theory, that treats uncertain fully and underactuated dynamical systems described by ordinary differential equations. Uncertainty in multibody dynamical systems comes from various sources, such as system parameters, initial conditions, sensor and actuator noise, and external forcing. Treatment of uncertainty in design is of paramount practical importance because all real-life systems are affected by it, and poor robustness and suboptimal performance result if it is not accounted for in a given design. In this work uncertainties are modeled using generalized polynomial chaos and are solved quantitatively using a least-square collocation method. The computational efficiency of this approach enables the inclusion of uncertainty statistics in the nonlinear programming optimization process. As such, the proposed framework allows the user to pose, and answer, new design questions related to uncertain dynamical systems. Specifically, the new framework is explained in the context of forward, inverse, and hybrid dynamics formulations. The forward dynamics formulation, applicable to both fully and underactuated systems, prescribes deterministic actuator inputs that yield uncertain state trajectories. The inverse dynamics formulation is the dual to that of forward dynamics, and is only applicable to fully actuated systems; deterministic state trajectories are prescribed and yield uncertain actuator inputs. The inverse dynamics formulation is more computationally efficient as it requires only algebraic evaluations and completely avoids numerical integration. Finally, the hybrid dynamics formulation is applicable to underactuated systems where it leverages the benefits of inverse dynamics for actuated joints and forward dynamics for unactuated joints; it prescribes actuated state and unactuated input trajectories that yield uncertain unactuated states and uncertain actuated inputs. The benefits of the ability to quantify uncertainty when planning the motion of multibody dynamic systems are illustrated through several case studies. The resulting designs determine optimal motion plans—subject to deterministic and statistical constraints—for all possible systems within the probability space.