Potential function based methods play significant role in both global and local path planning. While these methods are characterized with good reactive behaviour and implementation simplicity, they suffer from a well-...
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ISBN:
(纸本)9783030112929;9783030112912
Potential function based methods play significant role in both global and local path planning. While these methods are characterized with good reactive behaviour and implementation simplicity, they suffer from a well-known problem of getting stuck in local minima of a navigation function. In this paper we propose a modification of our original spline-based path planning algorithm for a mobile robot navigation, which succeeds to solve local minima problem and considers additional criteria of start and target points visibility to help optimizing the path selection. We apply a Voronoi graph based path as an input for iterative multi criteria optimization algorithm and present a path finding strategy within different homotopies that uses the new method. the algorithm was implemented in Matlab environment and demonstrated significantly better results than the original approach. the comparison was based on success rate, number of iterations and running time of the algorithms. In total, several thousands tests were performed in 18 different simulated environments.
the problem of discrete-time multi-agent systems governed by general MIMO dynamics is addressed. By employing a PID-like distributed protocol, we aim to solve two relevant consensus problems, namely the leaderless wei...
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ISBN:
(纸本)9783030112929;9783030112912
the problem of discrete-time multi-agent systems governed by general MIMO dynamics is addressed. By employing a PID-like distributed protocol, we aim to solve two relevant consensus problems, namely the leaderless weighted consensus under disturbances and the leader-follower weighted consensus under time-varying reference state. Sufficient conditions for stability as well as a LMI approach to tune the controller gains are provided. the two consensus techniques are then applied to solve two issues concerning the wind farm (WF) power maximization problem under wake effect. Leaderless consensus aims at averaging out zero-mean wind disturbance effects on the optimal WF power sharing, while leader-follower control is employed to restore it in the case of power reference errors. Simulations are carried out on a small WF example, whose wind turbines parameters are the ones of NREL's CART turbine.
Assistive devices for rehabilitation purposes have gained much attention in robotics research. Using actuation systems that include compliant elements, provide advantages such as natural motions of robotic devices and...
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ISBN:
(纸本)9783030319939;9783030319922
Assistive devices for rehabilitation purposes have gained much attention in robotics research. Using actuation systems that include compliant elements, provide advantages such as natural motions of robotic devices and safety in the interaction with people. these actuation systems are called soft actuators. there are parallel elastic actuators (PEA), series elastic actuators (SEA) and variable stiffness actuators (VSA), which differ among them by the position of the compliant element and the possibility of changing the stiffness online. We have designed a five-bar-linkage knee rehabilitation system which uses the advantages of soft actuation. To accomplish desired tasks in a proper manner, using automatic systems, control strategies are required. In our case, this means to reproduce the desired motions without affecting the patient. In this way, the control system is crucial. In this chapter, we present a combined feedback-feedforward control strategy for the knee rehabilitation device designed. this work was partially presented before in [16], where we discussed the strategy and presented some simulation results. In this chapter we extend the results, presenting experimental trials to validate the performance of the controller and the behavior of the system. the goal of the proposed strategy is to controlthe system while maintaining the intrinsic softness of the actuators when the patient is in the rehabilitation process. the feedback control strategy acts in a defined threshold to maintain the stiffness of the system, and it is combined with a feed-forward decision control to reject disturbances. the simulations and the experimental results presented are obtained from the analysis of a One-Degree-of-Freedom (DoF) soft actuated system, to allow us to have an insight of the controller and the system, without losing generality.
this paper proposes a path tracking strategy for wheeled mobile robots of type {1, 2} (i.e. equipped with two steering axles), withthe aim to ensure the convergence of the front and rear control points along a same t...
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ISBN:
(纸本)9783030319939;9783030319922
this paper proposes a path tracking strategy for wheeled mobile robots of type {1, 2} (i.e. equipped with two steering axles), withthe aim to ensure the convergence of the front and rear control points along a same trajectory, leading to reduce the required space to achieve maneuvers. the proposed approach considers front and rear steering axles as two separate systems withtheir own control variables: the front and the rear steering angles. the problem of managing two steering axles is solved without considering an explicit control of the robot's orientation, nor a relationship between the two steering angles which is generally a not optimal approach. the proposed control laws are based on adaptive and predictive control techniques in order to address phenomena acting when moving in unstructured context, such as bad grip conditions, low-level and inertial delays. As a result, this control algorithm enables to accurately control bi-steerable mobile robots, while increasing their maneuverability. this is particularly suitable for off-road applications, such as in agriculture where potentially large robots have to move in cluttered environments and face low grip conditions.
A Structure-Constrained Matrix Factorisation (SCMF) problem is considered where data and structural constraints on one of the matrix factors specific to an application are known. A simple two-step iterative optimisati...
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ISBN:
(纸本)9783030112929;9783030112912
A Structure-Constrained Matrix Factorisation (SCMF) problem is considered where data and structural constraints on one of the matrix factors specific to an application are known. A simple two-step iterative optimisation algorithm can produce unique solutions provided both matrix factors are full-ranked and constraints matrix satisfies certain additional rank conditions. Constraints matrix is apriori known and hence, it can be tested for these rank conditions. Graph theoretical approaches can be used to decompose a graph representing incompatible constraints matrix into compatible subgraphs. However, there is no method available in relevant literature to compute the rank of second matrix factor as it is apriori unknown. Previously, it has been argued that the second matrix factor will naturally be full-ranked, but we show that this is not necessarily true. We develop theoretical bounds on rank of the second matrix factor in terms of ranks of constraints matrix, data matrix and their dimensions. Withthis new result, uniqueness of a solution can be guaranteed solely based on available constraints and data. Furthermore, we propose Beaded Network Component Analysis algorithm that introduces necessary corrections to the available graph decomposition and mixing algorithms to obtain unique solutions by computing a convex-combination of full-rank factors of subgraphs. the key contributions in this paper are theoretical bounds on rank of a matrix factor and unique solutions to a general SCMF problem.
this chapter aims the broadcasting of the results achieved by the RoDyMan project about the task planning manipulation of deformable objects, and the nonprehensile manipulation control. the final demonstrator of the p...
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ISBN:
(纸本)9783030319939;9783030319922
this chapter aims the broadcasting of the results achieved by the RoDyMan project about the task planning manipulation of deformable objects, and the nonprehensile manipulation control. the final demonstrator of the project is a pizza-making process. After an introduction to the general topic of nonprehensile manipulation, the mechatronic design and the high-level software architecture are described. then, the smoothed particle hydrodynamic formulation is briefly introduced, along withthe description of a detection method for a deformable object. the task planning for stretching a modelling clay, emulating the pizza dough, is sketched. After, the problematic control objective is split into several nonprehensile motion primitives: holonomic and nonholonomic rolling, friction-induced manipulation, and tossing are the described primitives. this chapter highlights the achievements reached so far by the project, and pave the way towards future research directions.
this paper presents a reliable on-line re-optimization control of a fed-batch fermentation process using bootstrap aggregated extreme learning machine. In order to overcome the difficulty in developing detailed mechan...
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ISBN:
(纸本)9783030112929;9783030112912
this paper presents a reliable on-line re-optimization control of a fed-batch fermentation process using bootstrap aggregated extreme learning machine. In order to overcome the difficulty in developing detailed mechanistic models, extreme learning machine (ELM) based data driven models are developed. In building an ELM model, the hidden layer weights are randomly assigned and the output layer weights are obtained in a one step regression type of learning. this feature makes the development of ELM very fast. A single ELM model can lack of robustness due the randomly assigned hidden layer weights. To overcome this problem, multiple ELM models are developed from bootstrap re-sampling replications of the original training data and are then combined. In addition to enhanced model accuracy, bootstrap aggregated ELM can also give model prediction confidence bounds. A reliable optimal control policy is achieved by means of the inclusion of model prediction confidence bounds within the optimization objective function to penalize wide model prediction confidence bounds which are associated with uncertain predictions as a consequence of plant model-mismatch. Finally, in order to deal with unknown process disturbances, an on-line re-optimization control strategy is developed in that on-line optimization is carried out while the batch process is progression. the proposed technique is successfully implemented on a simulated fed-batch fermentation process.
作者：
Kamal, ElkhatibAdouane, LounisMenoufia Univ
Fac Elect Engn Dept Ind Elect & Control Engn Menoufia 32952 Egypt CNRS
UMR 6602 UCASIGMA Inst Pascal Innovat Mobil Smart & Sustainable Sol F-63000 Clermont Ferrand France
this chapter proposes an intelligent energy management for hydraulic-electric hybrid vehicle in order to minimize its total energy consumption while ensuring a better battery life. It proposes first to model the total...
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ISBN:
(纸本)9783030112929;9783030112912
this chapter proposes an intelligent energy management for hydraulic-electric hybrid vehicle in order to minimize its total energy consumption while ensuring a better battery life. It proposes first to model the total energy consumption of the vehicle and investigate the minimization of an expended energy function, formulated as the sum of electrical energy provided by on-board batteries and consumed fuel. More precisely, it is proposed in this chapter an intelligent hierarchical controller system which shows its capabilities of increasing the overall vehicle energy efficiency and therefore minimizing total energy consumption, permitting to increase the distance between refueling. the proposed strategy consists of fuzzy supervisory fault management at the highest level (third), that can detect and compensate the battery faults, regulate all of the possible vehicles operation modes. In the second level, an optimal controller is developed based on artificial intelligence to manage power distribution between electric motor and engine. then, in the first level, there are local fuzzy tuning proportional-integral-derivative controllers to regulate the set points of each vehicle subsystems to reach the best operational performance. TruckMaker/MATLAB simulation results confirm that the proposed architecture can satisfy the power requirement for any unknown driving cycles and compensate battery faults effects.
this paper proposes a new modelling framework for accurate predictions of arterial blood gases (ABG) of the previously developed SOPAVent model (Simulation of Patients under Artificial Ventilation). three ABG paramete...
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ISBN:
(纸本)9783030112929;9783030112912
this paper proposes a new modelling framework for accurate predictions of arterial blood gases (ABG) of the previously developed SOPAVent model (Simulation of Patients under Artificial Ventilation). three ABG parameters which were elicited from the SOPAVent model are the partial arterial pressure of oxygen (PaO2), the partial arterial pressure of carbon-dioxide (PaCO2) and the acid-base (pH). SOPAVent generate predictions of initial ABG and predictions of ABG after ventilator settings were modified. SOPAVent's sub-models, the relative dead space (Kd) and the carbon-dioxide production (VCO2) were designed using interval type-2 fuzzy logic system (IT2FLS). Further explorations of the models were carried out using fuzzy c-means clustering (FCM) and tuning of fuzzy parameters using 'new structure' particle swarm optimization algorithm (nPSO). the new models were integrated into the SOPAVent system for blood gas predictions. SOPAVent was validated using real intensive care unit (ICU) patient data, obtained from the Royal Hallamshire Hospital and Northern General Hospital, Sheffield (UK). the prediction accuracy of SOPAVent was compared withthe pre-existing SOPAVent model where the Kd and VCO2 sub-models were developed using machine learning algorithms. Significant improvements in accuracy and correlation were achieved under this frameworks for PaCO2 and pH for boththe initial ABG predictions and the post ventilator settings adjustments.
In the field of swarm robotics, the design and implementation of spatial density control laws has received much attention, with less emphasis being placed on performance evaluation. this work fills that gap by introdu...
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ISBN:
(纸本)9783030319939;9783030319922
In the field of swarm robotics, the design and implementation of spatial density control laws has received much attention, with less emphasis being placed on performance evaluation. this work fills that gap by introducing an error metric that provides a quantitative measure of coverage for use with any control scheme. the proposed error metric is continuously sensitive to changes in the swarm distribution, unlike commonly used discretization methods. We analyze the theoretical and computational properties of the error metric and propose two benchmarks to which error metric values can be compared. the first uses the realizable extrema of the error metric to compute the relative error of an observed swarm distribution. We also show that the error metric extrema can be used to help choose the swarm size and effective radius of each robot required to achieve a desired level of coverage. the second bench-mark compares the observed distribution of error metric values to the probability density function of the error metric when robot positions are randomly sampled from the target distribution. We demonstrate the utility of this benchmark in assessing the performance of stochastic control algorithms. We prove that the error metric obeys a central limit theorem, develop a streamlined method for performing computations, and place the standard statistical tests used here on a firm theoretical footing. We provide rigorous theoretical development, computational methodologies, numerical examples, and MATLAB code for both benchmarks.
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