External confinement of concrete columns by means of carbon fiber reinforced polymer (CFRP) sheets can be considered as an efficient technique for their structural strengthening. An experimental research program including 18 circular short column specimens were tested under axial compression load, to investigate the gain strength of reinforced concrete (RC) columns confined with CFRP sheets. The parameters studied were both the volume and configurations of CFRP sheets, the size of cross-section, the percentage of main reinforcement, and the volume of internal stirrups. On the basis of the obtained results, mathematical models (Egyptian code and American Concrete Institute code) proposed to predict the axial compressive strength of non-slender RC column strengthened by means of CFRP sheets are evaluated. These codes showed an underestimation in predicting the axial compressive strength of RC strengthened columns. This, from the authors’ point of view, is attributed mainly to the fact that the proposed models overlooked the amount of internal stirrups when calculating the strength of strengthened columns. Therefore, modifications in the studied models were considered. The modifications take the effective lateral confining pressure due to presence of internal steel stirrups into account. The modified codes showed an acceptable approach to the experimental results.

External confinement of concrete columns by means of carbon fiber reinforced polymer (CFRP) sheets can be considered as an efficient technique for their structural strengthening. An experimental research program including 18 circular short column specimens were tested under axial compression load, to investigate the gain strength of reinforced concrete (RC) columns confined with CFRP sheets. The parameters studied were both the volume and configurations of CFRP sheets, the size of cross-section, the percentage of main reinforcement, and the volume of internal stirrups. On the basis of the obtained results, mathematical models (Egyptian code and American Concrete Institute code) proposed to predict the axial compressive strength of non-slender RC column strengthened by means of CFRP sheets are evaluated. These codes showed an underestimation in predicting the axial compressive strength of RC strengthened columns. This, from the authors’ point of view, is attributed mainly to the fact that the proposed models overlooked the amount of internal stirrups when calculating the strength of strengthened columns. Therefore, modifications in the studied models were considered. The modifications take the effective lateral confining pressure due to presence of internal steel stirrups into account. The modified codes showed an acceptable approach to the experimental results.

— The growing importance of the knowledge-intensive service-based knowledge organizations and resultant dynamic capabilities, and facilitate service innovation Interval model can be used to describe nonlinear dynamic systems. Control of an inverted pendulum on a carriage which moves in a horizontal path, is one of the classic problems in the area of control. The basic aim of our work was to design appropriate controller to control the angle of the pendulum and the position of the cart in order to stabilize the inverted pendulum system. The main objective of this paper to keep the stabilization of the inverted pendulum based on the simplification of rule base. The proposed fuzzy control scheme successfully fulfills the control objectives and also has an excellent stabilizing ability to overcome the external impact acting on the pendulum system. This paper presents an application of how to design and validate a real time neuro fuzzy controller of complex a nonlinear dynamic system using the Matlab-Simulink Real-Time Workshop environment. Once the controller is obtained and validated by simulation, it’s implemented to control the pendulum-cart system. The design and optimization process of neuro fuzzy controller are based on an extended learning technique derived from adaptive neuro fuzzy inference system (ANFIS). The design and implementation of this pendulum-cart control system has been realized under MATLAB/SIMULINK environment. The experimental results demonstrate the efficiency of the simplified design procedure and ensured stability of this system

Qualitative modeling is one promising approach to the solution of difficult tasks automation if qualitative process models are not available. This contribution presents a new concept of qualitative dynamic process modeling using so called Dynamic Adaptive Neuro fuzzy Systems. In contrast to common approaches of Adaptive Neuro Fuzzy modeling [1], the dynamic system is completely described in the neuro fuzzy domain: the neuro fuzzy information about the previous state is directly applied to compute the system’s current state, i.e. the delayed neuro fuzzy output is feedback to the input without defuzzification. Knowledge processing in such dynamic neuro fuzzy systems requires a new inference method, the inference with interpolating rules. This yields the framework of a new systems theory the essentials of which are given in further section of the paper. First, an identification method is presented, using a combination of linguistic knowledge. Next, a stability definition for dynamic neuro fuzzy systems as well as methods for stability analysis is given. Finally, a neuro fuzzy model-based neuro fuzzy controller design method is developed. The identification of real problems and neuro fuzzy controller design for inverted pendulum system demonstrate the significance of the new systems theory.

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- This paper attempts to present a neural inverse control design framework for a class of nonlinear multiple-input multiple-output (MIMO) system with uncertainties. This research effort is motivated by the following considerations: (a) An appropriate reference model that accurately represents the desired system dynamics is usually assumed to exist and to be available, and yet in reality this is not the case often times; (b) In real world applications, there are many cases where controls are constrained within a physically allowable range, which presents another layer of difficulties to directly apply the reference model based inverse control; (c) It is difficult to consider optimal control even for the reference model as in general the analytic solution to the optimal control problem is not available. The simulation study has been focused on the identification of Multiple Input, Single Output (MISO) non-linear complex systems. This paper concentrates on the identification of Multiple Input Multiple Output (MIMO) system by means of a hybrid-learning rule, which combines the back propagation and the Least Mean Squared (LMS) to identify parameters. We construct a neuro fuzzy model structure, and generate the membership function from the measured data. The MIMO system model is represented as a set of coupled input-output MISO models of the Takagi- Sugeno type. Neuro fuzzy model of the system structure is incorporated easily in the structure of the model. The simulation is used to implement a MIMO spacecraft system using Matlab for moment_yaw, moment_pitch, and moment_roll as input, and velocity in inertial axis as output. Experimental results are given to show the effectiveness of this Adaptive Neuro Fuzzy System (ANFIS) model.

In the past few years, considerable effort has been made to improve the power efficiency of electrohydraulic systems; many energy saving strategies have been successfully developed and used. However, most of them can only be useful in specific applications. For instance, displacement control and secondary control only focus on those systems in which the efficiency concerns are more important. Although these systems have very high efficiency, they are not designed for applications in which the flow rate is varied during the duty cycle. Compared with pump controlled systems and other energy efficient systems, the valve controlled system demonstrates good dynamic performance and controllability especially for inertia dominated loads but at the expense of power efficiency. For electrohydraulic circuits which employ load-sensing systems for example, the design objective has been made to combine the advantages of high dynamic performance with better energy utilization. However, this high efficiency can only be obtained under particular operating conditions, such as single-load or multi-loads with similar load pressure requirements. No one approach is available for general system design where both good dynamic performance and high-power efficiency are important. The objective of the present paper is to highlight the different technological processes used for improving the performance of efficient electro-hydraulic circuit in energy saving.

The development of a fuzzy-logic controller for a class of industrial hydraulic manipulator is described. The main element of the controller is a PI-type fuzzy control technique which utilizes a simple set of membership functions and rules to meet the basic control requirements of such robots. Using the triangle shaped membership function, the position of the servocylinder was successfully controlled. When the system parameter is altered, the control algorithm is shown to be robust and more faster compared to the traditional PID controller. The robustness and tracking ability of the controller were demonstrated through simulations.

A novel circuit for linearization of thermocouple signals using Analog – to – Digital converter (ADC) is proposed. The present method utilizes the ratio metric property of ADCs and the converter performs analog to digital conversion as well as linearization. The resulting circuit also has provision for scaling the linearized digital output to obtain a desired full-scale value. Computational studies carried out on the proposed method gives satisfactory results for thermocouples with monotonic concave upward and downward characteristics.

Previous research in HRI have shown that human's subjective evaluation of robot's abilities affect the way people interact with robots. Given that one of the major challenges in learning from demonstration in robotics is the limited number of training examples that the demonstrator is usually willing to provide, it would be beneficial to design the interaction context in such a way to increase human's subjective evaluation of the robot's imitative skills. We propose back imitation as a way to achieve that goal. This paper reports the results of a preliminary study that was conducted to evaluate the effect of back imitation on human's subjective evaluation of the robot along several dimensions including imitation skill, motion human likeness, interaction quality, humanness and likability.