Feasible Input Research Articles

Task and Motion Planning for Manipulator Arms With Metric Temporal Logic Specifications

The aim is to synthesize control inputs for robotic manipulator arms that ensure a desired task specification is executed while optimizing a desired performance objective. We use metric temporal logic (MTL) to express the task specifications defined in terms of manipulating objects and implemented a hierarchical method combining mixed integer-linear programming (at high-level) to obtain a candidate MTL task specification defined in terms of the location of the arm end-effector and gradient descent based optimization (at low-level) for automatic synthesis of the motion plans to execute such candidate task specifications. The gradient descent algorithm is performed over the feasible input space to optimize the manipulator arm trajectory from a given arbitrary initial condition to perform the task at hand. We demonstrate the efficacy of our method by simulating a task specification on a Baxter robot.

Determination of Periodic Trajectories of Dynamic Systems Subject to Switching Input Constraints

The present paper is concerned with the determination of periodic state trajectories of discrete-time dynamic systems resulting from the application of bidirectional on/off input sequences subject to switching constraints. The main contribution consists of characterizing the feasible input sequences and associated state trajectories in terms of a set of linear constraints involving integer variables. For practical purposes, the resulting solutions obtained by an integer linear solver can be evaluated in terms of engineering criteria such as amplitude or fuel expense in order to choose an appropriate trajectory to be used as target in a control law. A numerical example involving a double-integrator system is presented to illustrate the use of the proposed constraint formulation for the determination of feasible periodic state trajectories.

A real-time, MPC-based Motion Cueing Algorithm with Look-Ahead and driver characterization

The use of dynamic driving simulators is nowadays common practice in the automotive industry. The effectiveness of such devices is strongly related to their capabilities of well reproducing the driving sensations, hence it is crucial that the motion control strategies generate both realistic and feasible inputs to the platform. Such strategies are called Motion Cueing Algorithms (MCAs). Model Predictive Control (MPC) has been successfully applied to MCAs, being well suited to solve constrained optimal control problems. However, the predictive aspect of the algorithm has not been exploited effectively yet, mainly due to the hard real-time requirement when using a significantly long prediction window. In this paper, a real time implementation of the so called Look-Ahead (LA) strategy is presented, that is based on an effective manipulation of the reference along the prediction horizon, and on an on-line switching policy to a Non-Look-Ahead strategy when the expected driver behavior is not reliable. An optimal tuning of the MCA is computed by means of a multi-objective optimization, where both performance improvement due to the prediction exploitation, and robustness to varying driver behavior are taken into account. Finally, a characterization of the driver skill level is proposed and validated in experimental environment.

On the Structure of the Optimal Solution to a Periodic Drug-Delivery Problem

This paper examines the shaping of a drug's delivery—in this case, nicotine—to maximize its efficacy. Previous research: (i) furnishes a pharmacokinetic–pharmacodynamic (PKPD) model of this drug's metabolism; (ii) shows that the drug-delivery problem is proper, meaning that its optimal solution is periodic; (iii) shows that the underlying PKPD model is differentially flat; and (iv) exploits differential flatness to solve the problem by optimizing the coefficients of a truncated Fourier expansion of the flat output trajectory. In contrast, the work in this article provides insight into the structure of the theoretical solution to this optimal periodic control (OPC) problem. First, we argue for the existence of a bijection between feasible periodic input and state trajectories of the problem. Second, we exploit Pontryagin's maximum principle to show that the optimal periodic solution has a bang–singular–bang structure. Building on these insights, this article proposes two different numerical methods for solving this OPC problem. One method uses nonlinear programming (NLP) to optimize the states at which the optimal solution transitions between the different solution arcs. The second method approximates the control input trajectory as piecewise constant and optimizes the discrete values of the input sequence. The paper concludes by discussing the computational costs of these two algorithms as well as the importance of the associated insights into the structure of the optimal solution trajectory.

Application-Specific Computational Materials Design via Multiscale Modeling and the Inductive Design Exploration Method (IDEM).

The development of materials is a laborious, iterative, expensive, and intuitive process, often requiring decades to transition from early laboratory studies to commercial applications. This research seeks to address this issue by demonstrating a systematic process for linking process-structure-property-performance (PSPP) relations. We argue that the limitations on time for the material development process arise in large part due to lack of effective approaches for exploring the material design space that anticipates application requirements, objectives, and constraints. The material design process employed here utilizes hierarchical multiscale modeling, analytical models, and associated metamodels to construct a set of bottom-up deductive mappings, along with the inductive design exploration method (IDEM) to account for uncertainty in pursuing top-down inductive decision support problems that address application-specific design objectives. The demonstrated problem considers the simultaneous design of ultra-high-performance concrete material and a structural panel able to withstand a 1.5-MPa-ms reflected blast wave impulse. A set of PSPP mappings were constructed across micro-, meso-, and macro-length-scales using analytical expressions and a hierarchical multiscale finite element model at the single fiber, multiple fiber, and structural length scales. The set of PSPP deductive mappings considered seven design variables-panel thickness, fiber pitch, ratio of water to cementitious materials, curing temperature, and volume fractions of fibers, cement, and silica fume-across four hierarchical levels. After the set of deductive PSPP mappings were constructed and validated, ranged sets of feasible values for each design variable were determined via IDEM. Starting with the highest and next-to-higher hierarchical levels as the output and input spaces, respectively, IDEM was implemented via application of three steps-discretization of input variables, projection of discretized sets of input variables with account of uncertainty to a range in the output space, and determination of which sets of discrete input values satisfy the output space requirement(s). By recursively applying these three steps, the PSPP relations were robustly searched for properties, structures, and processes that satisfy the performance requirement(s). The advantages of this approach are the identification of ranged sets of values of design variables and the ability to account for propagated uncertainty. By defining additional mass and cost objectives, the feasible input space was then searched to find the preferred combination of values of design variables that minimized mass and minimized cost while maintaining a robust material and structural design.

A Nonlinear, MPC-Based Motion Cueing Algorithm for a High-Performance, Nine-DOF Dynamic Simulator Platform

The use of dynamical driving simulators is nowadays common in many different application fields, such as driver training, vehicle development, and medical studies. Platforms with different mechanical structures have been designed, depending on the particular application and the corresponding targeted market. The effectiveness of such devices is related to their capabilities of well reproducing the driving sensations, and hence, it is crucial that the motion control strategies generate both realistic and feasible inputs to the platform, to ensure that it is kept within its limited operation space. Such strategies are called motion cueing algorithms (MCAs). In this brief, we describe an MCA based on nonlinear model predictive control (MPC) techniques, for a nine-degree of freedom simulator based on a hexapod mounted on a flat base moved by a tripod, exhibiting highly nonlinear behavior. The algorithm has been evaluated in a simulation environment. Simulation results show that the full exploitation of the working area is achieved, while managing at best all the limitations given by the particular structure and preserving the easiness and intuitiveness of tuning, which is typical of linear MPC-based approaches.

A fast implementation of MPC-based motion cueing algorithms for mid-size road vehicle motion simulators

ABSTRACTThe use of dynamic driving simulators is constantly increasing in the automotive community, with applications ranging from vehicle development to rehab and driver training. The effectiveness of such devices is related to their capabilities of well reproducing the driving sensations, hence it is crucial that the motion control strategies generate both realistic and feasible inputs to the platform. Such strategies are called motion cueing algorithms (MCAs). In recent years several MCAs based on model predictive control (MPC) techniques have been proposed. The main drawback associated with the use of MPC is its computational burden, that may limit their application to high performance dynamic simulators. In the paper, a fast, real-time implementation of an MPC-based MCA for 9 DOF, high performance platform is proposed. Effectiveness of the approach in managing the available working area is illustrated by presenting experimental results from an implementation on a real device with a 200 Hz control frequency.

A ratio-based method for ranking production units in profit efficiency measurement

Evaluating profit efficiency measure needs input costs and output prices as well as input–output quantities. It is important to rank production units based on relative or absolute values of their production when costs and prices are available. Ranking production units in data envelopment analysis constitutes ranking individual units based on their profit efficiency ratio measures. Two novel models are presented in this paper for evaluating units based on a profit-dominance criterion. Models consider not only self-appraisal DEA optimal weights, but also all feasible input and output weights. A novel ratio-based and complete ranking method is introduced that is based on computing upper and lower boundaries for profit performance of observed units. An illustrative application of the models is then presented and results are discussed.

Online Regenerator Placement

Connections between nodes in optical networks are realized by lightpaths. Due to the decay of the signal, a regenerator has to be placed on every lightpath after at most d hops, for some given positive integer d. A regenerator can serve only one lightpath. The placement of regenerators has become an active area of research during recent years, and various optimization problems have been studied. The first such problem is the Regeneration Location Problem (Rlp), where the goal is to place the regenerators so as to minimize the total number of nodes containing them. We consider two extreme cases of online Rlp regarding the value of d and the number k of regenerators that can be used in any single node. (1) d is arbitrary and k unbounded. In this case a feasible solution always exists. We show an O(log|X|⋅ logd)-competitive randomized algorithm for any network topology, where X is the set of paths of length d. The algorithm can be made deterministic in some cases. We show a deterministic lower bound of ${\Omega } \left (\frac {\log (|{E}|/d) \cdot \log d}{\log (\log (|{E}|/d) \cdot \log d)}\right )$ , where E is the edge set. (2) d = 2 and k = 1. In this case there is not necessarily a solution for a given input. We distinguish between feasible inputs (for which there is a solution) and infeasible ones. In the latter case, the objective is to satisfy the maximum number of lightpaths. For a path topology we show a lower bound of $\sqrt {l}/2$ for the competitive ratio (where l is the number of internal nodes of the longest lightpath) on infeasible inputs, and a tight bound of 3 for the competitive ratio on feasible inputs.

Nonlinear observer-based Lyapunov boundary control of distributed heat transfer mechanisms for membrane distillation plant

This paper presents a nonlinear observer-based Lyapunov control for a membrane distillation (MD) process. The control considers the inlet temperatures of the feed and the permeate solutions as inputs, transforming it to boundary control process, and seeks to maintain the temperature difference along the membrane boundaries around a sufficient level to promote water production. MD process is modeled with advection diffusion equation model in two dimensions, where the diffusion and convection heat transfer mechanisms are best described. Model analysis, effective order reduction and parameters physical interpretation, are provided. Moreover, a nonlinear observer has been designed to provide the control with estimates of the temperature evolution at each time instant. In addition, physical constraints are imposed on the control to have an acceptable range of feasible inputs, and consequently, better energy consumption. Numerical simulations for the complete process with real membrane parameter values are provided, in addition to detailed explanations for the role of the controller and the observer.

A dual-mode online optimization method for trajectory tracking of redundant manipulators

Purpose – The purpose of this paper is to present a dual-mode online optimization method (OOM) for trajectory tracking of the redundant manipulators. This method could be used to resolve the problem of the kinematics redundancy effectively when the manipulator moves in a limited space or its movements go through a singular point. Design/methodology/approach – In the proposed method, the physical limits of the manipulator in the torque level is considered as inequality constraints for the optimal scheme. Besides, a dual-mode optimal scheme is developed to yield a feasible input in each control period during the path tracking task of the manipulator, especially when it moves under the limited space or around the singular point. Then, the scheme is formulated as a quadratic programming; the computationally efficient quadratic programming solver based on interior method is formulated to solve the kinematic redundancy problem. Findings – The traditional pseudo inverse method (PIM) for the kinematic resolution to the redundant manipulator has some limitations, such as slow computation speed, unable to take joint physical limits into consideration, etc. Relatively, the OOM could be used to conquer the deficits of the PIM method. Combining with the dual-mode optimal scheme and considering the physical constraints in the torque level, the online method proposed in this paper is more robust and efficient than the existing method. Originality/value – In this paper, dual-mode OOM is first proposed for the resolution of the kinematics redundancy problem. Specific design of its model and the discussion of its performance are also presented in this paper.

Architecture of an Ultra-Low-Power Fully Autonomous Universal Power Conditioner of Energy Harvester for Wireless Sensor Networks: A Review

A fully autonomous Universal Power Conditioner (UPC) that can accept energy from any micro-energy harvester (e.g., photovoltaic, thermoelectric, electromagnetic, piezoelectric etc.) is proposed. Generally, UPC removes the need of multiple optimized source specific converters. But, the proposed UPC architecture will be Ultra-Low-Power (ULP) with maximized efficiency to support the low energy levels generated by harvesting sources. This UPC uses a universal source detection block consisting of source converter block, boost converter, storage unit and buck regulation. A voltage threshold block reduces the energy processing stage. A kick-start circuit enables self-starting operation. A power management block achieves efficiency in energy storage and charge cycles. The UPC will be modeled, designed and simulated in PSPICE. A behavioral model will be designed and simulated on Modelsim. The UPC will be realized on FPGA board, validated and analyzed in layout using 0.13 µm CMOS process technology. The expected result is a feasible arbitrary input source detector capable of 2-3 different sources with minimum 1 mW output power for WSN nodes or charging battery sets. The power converters are expected to consume 90% peak efficiency with input dc voltage at 0.02-5 V and output dc voltage from 0.5-5 V.

Online optimization scheme with dual-mode controller for redundancy-resolution with torque constraints

In this paper, an online optimization scheme is proposed for the real time path-tracking control of the redundant robot manipulator. In the proposed scheme, the inequality constraints are extended to the torque level to avoid the torque saturation. Besides, a dual-mode optimal controller is used to yield a feasible input during each control period, to resolve the solution space decreasing problem. Then the scheme is formulated as a Quadratic Program (QP), the computationally efficient Knitro-Based solver is applied to remedy the inescapable redundancy-resolution problem. Furthermore, the comparisons based on the different path tracking simulations between the proposed method and the velocity-level schemes demonstrate that the former is safer and more applicable. The experiment on a physical robot system further verifies the physical realizability of the proposed method. A dual-mode online optimization method for the redundancy-resolution with torque constraints is proposed in this paper, which extends the constraints in the kinematics level to the dynamics level.The inequality constraints model is designed in the torque level, synthetically considered the constraints in the joint velocity and acceleration level.Besides, a Kintro instrument is applied for redundancy-resolution first time.

SAR Image Segmentation Using the Roughness Information

Image segmentation can be applied to a broad class of different problems. However, it is not usually a simple task for synthetic aperture radar (SAR) images due to the presence of speckle. Given the importance of SAR images in remote sensing problems, this letter introduces a simplified and general methodology to achieve SAR image segmentation by using the estimated roughness parameters of SAR data modeled by G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> and G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> distributions, instead of directly processing the speckled images. In this letter, we adopted the log-cumulants method for the roughness parameter estimation. The performance evaluation of the results was attained in terms of the error of segmentation and cross-region fitting measures for synthetic and real SAR images, respectively. With regard to synthetic images, we performed Monte Carlo experiments which confirmed the suitability of SAR image segmentation by means of roughness parameters. The results showed that the methodology provides a feasible input to SAR image segmentation algorithms which also include thresholding-based methods. The proposed approach accomplished satisfactory results for the most interesting and critical study case, i.e., the single-look images, which are markedly affected by speckle.

Amplification uncertainty relation for probabilistic amplifiers

Traditionally, quantum amplification limit refers to the property of inevitable noise addition on canonical variables when the field amplitude of an unknown state is linearly transformed through a quantum channel. Recent theoretical studies have determined amplification limits for cases of probabilistic quantum channels or general quantum operations by specifying a set of input states or a state ensemble. However, it remains open how much excess noise on canonical variables is unavoidable and whether there exists a fundamental trade-off relation between the canonical pair in a general amplification process. In this paper we present an uncertainty-product form of amplification limits for general quantum operations by assuming an input ensemble of Gaussian distributed coherent states. It can be derived as a straightforward consequence of canonical uncertainty relations and retrieves basic properties of the traditional amplification limit. In addition, our amplification limit turns out to give a physical limitation on probabilistic reduction of an Einstein-Podolsky-Rosen uncertainty. In this regard, we find a condition that probabilistic amplifiers can be regarded as local filtering operations to distill entanglement. This condition establishes a clear benchmark to verify an advantage of non-Gaussian operations beyond Gaussian operations with a feasible input set of coherent states and standard homodyne measurements.

SIMULATION MODEL PROPOSED FOR ESTIMATION OF MODAL DISPLACEMENT OF RC BUILDING LOCATED IN SEISMIC ZONE-V (IS-1893-2002) FROM KNOWN PARAMETERS

The seismic analysis and design of reinforced concrete (RC) frames is still an unresolved issue due to its complex behavior as the performance of RC building is linked to the features of the structural system, which have many influencing parameters. In this study, an attempt has been made to investigate the displacement scenario of RC residential building seismically located in Zone-V due to the influence of some basic parameters (column dimension, beam dimension, grade of concrete, floor height and seismic weight) in order to obtain a relation which could be used to estimate the expected lateral displacement owing to those selected parameters. In this study, a total of 297 RC buildings designed as per the Indian codal provisions [IS-1893-2002] were analyzed by linear static method using SAP2000 by varying selected and feasible input parameters chosen. The resulting displacements for the change of each parameter were obtained and finally a generalized equation relating the expected displacement as a function of those selected parameters was established using the regression method. The results of the comparison between the displacements obtained from analysis with that of the equation so formed shows that the proposed relation can be used directly to determine the expected displacement with high reliability.

A fundamental control limitation for linear positive systems with application to Type 1 diabetes treatment

This paper presents a fundamental design trade-off applicable to a class of linear positive systems. The result connects the maximum and minimum output response peaks due to a disturbance for all feasible inputs. A core consequence of the result is that, when the disturbance pulse response peaks faster than the input pulse response, then attempts to minimise the maximum peak response to the disturbance are necessarily accompanied by unavoidable undershoot at a later time. The result has potential application in many areas. For example, it provides a defensible benchmark for comparison of all possible insulin treatment strategies for Type 1 diabetes patients. This includes any control strategy implemented in the context of an Artificial Pancreas (AP). The lack of such a benchmark has been a deficiency in the existing AP literature.

Offline Model Predictive Control Based on Weighted Projection over Polytopes

This work presents a novel offline model predictive control technique for tracking of constrained systems. The quadratic programming problem, commonly found in constrained control methods, is replaced by sequential offline set projections based on priority given to the decision variables. If a preference is established in terms of which decision variables are more desirable, the optimization problem can be solved by sequentially choosing the most important variables and performing a membership test with the projection of the constraint closed set over the related dimensions. Thus, real-time optimization is replaced by offline projection operations and online one-dimensional membership tests. This concept of decision variable prioritization is then applied to a form of model predictive control: feasible target tracking. Three quadratic programming problems are replaced by the proposed method. In the first problem, attainable steady-state demands are computed based on the performance of the plant. The reachable target command is then filtered in terms of dynamic admissibility, creating feasible inputs to the plant. Finally, the control is computed considering the current state and disturbance vectors along with the feasible and attainable command. Simulations of the method executing a path-following task are presented, demonstrating its benefits with negligible online computational burden.

Applicability of video-derived bathymetry estimates to nearshore current model predictions

Radermacher, M., Wengrove, M.E., Van Thiel de Vries, J.S.M., Holman, R.A., 2014. Applicability of video-derived bathymetry estimates to nearshore current model predictions. In: Green, A.N. and Cooper, J.A.G. (eds.), Proceedings 13th International Coastal Symposium (Durban, South Africa), Journal of Coastal Research, Special Issue No. 70, pp. 290–295, ISSN 0749-0208.In the framework of swimmer safety, coastal managers desire accurate nearshore current predictions obtained from numerical models. To this end, detailed and up-to-date bathymetry is a necessity. Remote sensing techniques for bathymetry estimation are a promising solution. The focus of this paper is to assess the performance of wavenumber-based bathymetric inversion using Argus imagery (also known as the cBathy algorithm) as a feasible input bathymetry for numerical models to make reasonable nearshore current predictions. Numerical flow simulations on a cBathy bed are compared to simulations on an in-situ surveyed bathymetry. Results demonstrate that simulated nearshore currents on a cBathy bathymetry have a root-mean-square error in the order of 10 cm/s (magnitude) and 40 degrees (direction) when compared to simulated currents on a surveyed bathymetry. In the intertidal zone cBathy should be combined with a different method for bathymetry estimation in order to decrease these errors.

On the tip of the tongue: learning typing and pointing with an intra-oral computer interface

Purpose: To evaluate typing and pointing performance and improvement over time of four able-bodied participants using an intra-oral tongue-computer interface for computer control. Background: A physically disabled individual may lack the ability to efficiently control standard computer input devices. There have been several efforts to produce and evaluate interfaces that provide individuals with physical disabilities the possibility to control personal computers. Method: Training with the intra-oral tongue-computer interface was performed by playing games over 18 sessions. Skill improvement was measured through typing and pointing exercises at the end of each training session. Results: Typing throughput improved from averages of 2.36 to 5.43 correct words per minute. Pointing throughput improved from averages of 0.47 to 0.85 bits/s. Target tracking performance, measured as relative time on target, improved from averages of 36% to 47%. Path following throughput improved from averages of 0.31 to 0.83 bits/s and decreased to 0.53 bits/s with more difficult tasks. Conclusions: Learning curves support the notion that the tongue can rapidly learn novel motor tasks. Typing and pointing performance of the tongue-computer interface is comparable to performances of other proficient assistive devices, which makes the tongue a feasible input organ for computer control.Implications for RehabilitationIntra-oral computer interfaces could provide individuals with severe upper-limb mobility impairments the opportunity to control computers and automatic equipment.Typing and pointing performance of the tongue-computer interface is comparable to performances of other proficient assistive devices, but does not cause fatigue easily and might be invisible to other people, which is highly prioritized by assistive device users.Combination of visual and auditory feedback is vital for a good performance of an intra-oral computer interface and helps to reduce involuntary or erroneous activations.