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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.

Humanoid robots have - by definition - some level of human-likeness in body form. According to previous research in HRI, this leads to a higher expectation of human-like behavior. Nevertheless, human-likeness is not an easy notion to define for motion even in a task as straight forward as real-time motion copying (the shadowing task) as this paper will try to argue. The main hypothesis of this paper is that subjective evaluation of robot's motion's human-likeness depends not only on the objective similarity between robot's motion and human motion but also on the interaction context (e.g, whether or not the human have previously engaged in mutual or back imitation with the robot). Moreover, the paper proposes two features of motion similarity that affect subjective evaluation of human-likeness and accuracy in the shadowing task and shows that human-likeness is a different attribution dimension form both accuracy and humanness (measured using human-nature traits). The paper reports a controlled user study involving 36 participants and 108 HRI sessions to support these claims.

Our goal is to make a system to detect the times at which one almost laughed but he or she did not show their laughter on his/her face. We define this kind of laughter as hidden laughter. To accomplish this goal, we first tried making decision trees to detect one's amusement, the input data of which were physiological indices. We used 10-fold cross validation to evaluate the trees, and their accuracy was more than 70%. In addition, we investigated the effect of cultural background on the accuracy.