Energy Consumption Optimization for Mobile Robots Motion Using Predictive Control

Operation of mobile robots in off-road environment requires the attention to the torque saturation problem that occurs in the wheels DC motors while climbing hills. In the present work, off-road conditions are utilized to benefit while avoiding torque saturation. Energy optimization algorithm using predictive control is implemented on a two-DC motor-driven wheels mobile robot while crossing a ditch. The predictive control algorithm is simulated and compared with the PID control and the open-loop control. Predictive control showed more capability to avoid torque saturation and noticeable reduction in the energy consumption. Furthermore, using the wheels motors armature current instead of the supply voltage as control variable in the predictive control showed more efficient speed control. Simulation results showed that in case of known ditch dimensions ahead of time, the developed algorithm is feasible. Experimental examination of the developed energy optimization algorithm is presented. The experimental results showed a good agreement with the simulation results. The effects of the road slope and the prediction horizon length on the consumed energy are evaluated. The analytical study showed that the energy consumption is reduced by increasing the prediction horizon until it reaches a limit at which no more energy reduction is obtained. This limit is proportional to the width of the ditch in front of the mobile robot. Curve fitting is applied to the obtained results to address further the effect of the parameters on the energy consumption.     

Hybrid metaheuristics for scheduling of machines and transport robots in job shop environment

In real manufacturing environments, the control of some elements in systems based on robotic cells, such as transport robots has some difficulties when planning operations dynamically. The Job Shop scheduling Problem with Transportation times and Many Robots (JSPT-MR) is a generalization of the classical Job Shop scheduling Problem (JSP) where a set of jobs additionally have to be transported between machines by several transport robots. Hence, the JSPT-MR is more computationally difficult than the JSP presenting two NP-hard problems simultaneously: the job shop scheduling problem and the robot routing problem. This paper proposes a hybrid metaheuristic approach based on clustered holonic multiagent model for the JSPT-MR. Firstly, a scheduler agent applies a Neighborhood-based Genetic Algorithm (NGA) for a global exploration of the search space. Secondly, a set of cluster agents uses a tabu search technique to guide the research in promising regions. Computational results are presented using two sets of benchmark literature instances. New upper bounds are found, showing the effectiveness of the presented approach.     

Adaptive synchronization between two non-identical BAM neural networks with unknown parameters and time-varying delays

International Journal of Control, Automation and Systems - In this paper, the synchronization of two non-identical bidirectional associative memory (BAM) neural networks with unknown parameters and...     

Existence of optimal controls for systems driven by FBSDEs

We prove the existence of optimal relaxed controls as well as strict optimal controls for systems governed by non linear forward-backward stochastic differential equations (FBSDEs). Our approach is based on weak convergence techniques for the associated FBSDEs in the Jakubowski S-topology and a suitable Skorokhod representation theorem.     

Improved Approximations for Guarding 1.5-Dimensional Terrains

We present a 4-approximation algorithm for the problem of placing the fewest guards on a 1.5D terrain so that every point of the terrain is seen by at least one guard. This improves on the previous best approximation factor of 5 (see King in Proceedings of the 13th Latin American Symposium on Theoretical Informatics, pp. 629–640, 2006). Unlike most of the previous techniques, our method is based on rounding the linear programming relaxation of the corresponding covering problem. Besides the simplicity of the analysis, which mainly relies on decomposing the constraint matrix of the LP into totally balanced matrices, our algorithm, unlike previous work, generalizes to the weighted and partial versions of the basic problem.     

Dejittering in the transport of MPEG-2 and MPEG-4 video

When multimedia information is transported over a packet-switched network, the quality of presentation can be degraded due to network delay variation or jitter. This paper presents a dejittering scheme that can be used in the transport of MPEG-4 and MPEG-2 video to absorb any introduced network jitter, thus preserving the presentation quality of transported media streams. The dejittering scheme is based on the statistical approximation of delay variation in the arrival times of video packets carrying encoded clock reference values and a filtering and re-stamping mechanism. In addition, a brief overview of the MPEG-4 system is presented.     

Closed loop identification and control loop reconfiguration: an industrial case study

The results of a joint university–industry collaborative project for control loop reconfiguration using closed loop experimental data from a fuel gas pressure control system are described in this paper. The fuel gas pressure was being regulated using a butane stream. For economic reasons, it was necessary to switch control to the ethane stream. Previous attempts at effecting this changeover had proved unsuccessful. In this study, a powerful system identification technique namely Canonical Variate Analysis (CVA) was employed to obtain the empirical plant models. A PI controller was then designed using the direct synthesis method. Acceptable closed loop behavior was obtained with little online tuning.     

Computational haemodynamics: geometry and non-newtonian modelling using spectral/hp element methods

The increased availability of large scale computational facilities is enabling the use of computational modelling for the treatment of complex problems of industrial interest. Computational haemodynamics is an area where improved computational power and algorithmic development have permitted the application in biomechanics of techniques originally devised for direct numerical simulations in others areas of scientific interest. In this paper we present a highly accurate algorithm for the numerical solution of the incompressible Navier–Stokes equations which employs a spectral/hp element spatial discretisation and a high-order time splitting. Two important developments discussed in this paper are the coupling of these techniques with unstructured mesh generation, used in many everyday engineering applications, and the introduction of a non-Newtonian viscosity model to account for the shear thinning properties of blood. The proposed method will be applied to the analysis of shear stress distributions in flows of interest in haemodynamics. Received: 5 May 1999 / Accepted: 21 september 1999     

Diagnosis and treatment of aortic valve stenosis

The present review has attempted to summarize the classic symptoms and signs of aortic valve stenosis, especially in an adult. It is emphasized that all the classic signs rarely are present and their absence may mislead an unwary clinician. The diagnostic help provided by noninvasive tests, including echocardiography and phonocardiography, has been emphasized. A need for cardiac catheterization and angiography in most patients prior to corrective surgery is stressed. The natural history of the disease without operative intervention is dim and a significant risk of sudden death exists. The current surgical approach with immediate and long-term results is summarized. Finally, attention has been drawn to the special clinical circumstances when the aortic valve stenosis provides a strinkingly different clinical picture. We cannot find a better way to end this review than by quoting a warning note given by Thomas Lewis in 1920: "It is the faint cry of an anguished and fast failing muscle, which, when it comes, all should strain to hear, for it is not long repeated. A few months, a few years at most, and the end comes."     

Selection of regression models for predicting strength and deformability properties of rocks using GA

Recently, many regression models have been presented for prediction of mechanical parameters of rocks regarding to rock index properties. Although statistical analysis is a common method for developing regression models, but still selection of suitable transformation of the independent variables in a regression model is difficult. In this paper, a genetic algorithm (GA) has been employed as a heuristic search method for selection of best transformation of the independent variables (some index properties of rocks) in regression models for prediction of uniaxial compressive strength (UCS) and modulus of elasticity (E). Firstly, multiple linear regression (MLR) analysis was performed on a data set to establish predictive models. Then, two GA models were developed in which root mean squared error (RMSE) was defined as fitness function. Results have shown that GA models are more precise than MLR models and are able to explain the relation between the intrinsic strength/elasticity properties and index properties of rocks by simple formulation and accepted accuracy.