International Journal of Robotics
Volume:3 Issue: 1, Winter-Spring 2013

Stability and performance are two main issues in motion of bipeds. To ensure stability of motion, a biped needs to follow specific pattern to comply with a stability criterion such as zero moment point. However, there are infinity many patterns of motion which ensure stability, so one might think of achieving better performance by choosing proper parameters of motion. Step length and step period are among major parameters through which we control our motion. Change of these parameters results in change in pattern of motion and consequently affects major characteristics of motion such as stability, speed and energy consumption. In this paper we used genetic algorithm for stable path planning for motion with different values of step length and step period. Considering actuators limit, feasible domain of motion is found. Then maximum feasible speed and consumed power is calculated and reported.

In this paper a robust road departure avoidance system based on a closed-loop driver decision estimator (DDE) is presented. The main idea is that of incorporating the driver intent in the control of the vehicle. The driver decision estimator computes the vehicle look ahead lateral position based on the driver input and uses this position to establish the risk of road departure. To induce a risk of road departure, the proposed system is implemented on a driving simulator and thirty test drivers were asked to avoid a pylon-confined area (obstacle) while keeping the vehicle within the road limits. The RDA systems intervened by applying a haptic-feedback and correcting the steering angle in the event that a vehicle road departure was likely to occur. The experimental results showed that the proposed system reduced workload and effectively helped drivers to stay within road limits.

There have been many researches on object grasping in cooperating systems assuming no object slippage and stable grasp and the control system is designed to keep the contact force inside the friction cone to prevent the slippage. However undesired slippage can occur due to environmental conditions and many other reasons. In this research, dynamic analysis and control synthesis of a cooperating system, considering slipping conditions are performed. Equality and inequality equations of the frictional contact conditions are replaced by a single second order differential equation with switching coefficients in order to facilitate the dynamical modeling and control synthesis. Using this new modeling of friction, a conventional approach in grasping control is modified and presented to control any undesired slippage of the end-effectors on the object.

This paper proposes a hybrid control scheme for the synchronization of two chaotic Duffing oscillator system, subject to uncertainties and external disturbances. The novelty of this scheme is that the Linear Quadratic Regulation (LQR) control, Sliding Mode (SM) control and Gaussian Radial basis Function Neural Network (GRBFNN) control are combined to chaos synchronization with respect to external disturbances. By Lyapunov stability theory, SM control is presented to ensure the stability of the controlled system. GRBFNN control is trained during the control process. The learning algorithm of the GRBFNN is based on the minimization of a cost function which considers the sliding surface and control effort. Simulation results demonstrate the ability of the hybrid control scheme to synchronize the chaotic Duffing oscillator systems through the application of a single control signal.

This paper focuses on the effects of closed- control on the calculation of the dynamic load carrying capacity (DLCC) for mobile-base flexible-link manipulators. In previously proposed methods in the literature of DLCC calculation in flexible robots, an open-loop control scheme is assumed, whereas in reality, robot control is achieved via closed loop approaches which could render the calculated DLCC value inaccurate. The aim of this research is to investigate the necessity of considering the effect of closed loop control in the calculation of the DLCC of mobile-based flexible link manipulators. Finite elements modeling and the Lagrange method have been used for modeling a mobile-base manipulator with two flexible links link. After that, a control scheme based on the feedback linearization method has been devised. A method for calculating the DLCC from a previously published study has then been utilized, with the difference that closed-loop motion control has been assumed as opposed to open-loop control. Finally, three simulation case studies have been presented for which the results have been compared with those reported in a previously published study which ignores the closed-loop control effects. The comparisons show that the effect of closed-loop control on the DLCC needs to be taken into account.

This paper evaluates a new and simple controller design method based on QFT (quantitative feedback theory) for a two-link manipulator whose first link is rigid and the second is flexible. A piezoelectric patch is attached to the surface of the flexible link for vibration suppression of it. This system is modeled as a nonlinear multi-input multi-output (MIMO) control systems whose inputs are two motor torques which are applied on the joints and a voltage which is applied on the piezoelectric patch. To control the manipulator’s end point position, motion of the manipulator is divided to two rigid and flexible parts. To control both parts, nonlinear equations of the motion is replaced by a family of uncertain linear time-invariant equivalent systems using Rafeeyan-Sobhani’s method(RS method) which results in three decoupled transfer functions established in the Laplace domain. Then the QFT method is used to design a diagonal matrix as the prefilter of the system an another diagonal matrix as the system controller. Results demonstrate the remarkable performance of the proposed controllers in reduction of residual vibration of elastic link and tracking a circular trajectory by the manipulator end point.

In this study, a new adaptive controller is proposed for position control of pneumatic systems. Difficulties associated with the mathematical model of the system in addition to the instability caused by Pulse Width Modulation (PWM) in the learning-based controllers using gradient descent, motivate the development of a new approach for PWM pneumatics. In this study, two modified Feedback Error Learning (FEL) methods are suggested and the their effectiveness are validated by experimental tracking data. The first one is a combination of PD (Proportional–Derivative) and RBF (Radial Basis Function) and in the second one RBF is replaced by ANFIS (Adaptive Neuro-Fuzzy Inference System). The robustness to varying mass is also examined. The experimental results show that the proposed algorithms, especially with ANFIS, are able to give good performance regardless of any uncertainties.