Generalized symbolic trajectory evaluation (GSTE) is an extension of symbolic trajectory evaluation (STE). In GSTE, assertion graphs are used to specify properties in a special form of regular automata with antecedent and consequent pairs. This paper presents a new model characterization, called maximal models, for an assertion graph with important properties. Besides their own theoretical sign...

Identification of nonlinear stochastic processes via differential neural networks is discussed. A new "dead-zone" type learning law for the weight dynamics is suggested. By a stochastic Lyapunov-like analysis the stability conditions for the identification error as well as for the neural network weights are established. The adaptive trajectory tracking using the obtained neural network model is...

In our previous research, we experimentally implemented and evaluated the effect of dynamics compensation in modelbased control algorithms. In this paper, we evaluate the effect of changing the control sampling period on the performance of the computed-torque and independent joint control schemes. While the former utilizes the complete dynamics model of the manipulator, the latter assumes a dec...

In the context of Wireless Sensor Networks (WSNs), in this paper, the authors present a new agent based routing protocol named Fast Rumor Agents protocol (FRA). The FRA protocol optimizes the agent (respectively the query) propagation through the network preventing backward paths. With FRA, a rumor must have a straight trajectory, reducing the total overhead of the network. Performances compari...

Precise trajectory tracking is a crucial property for Micro Air Vehicles (MAVs) to operate in cluttered environment or under disturbances. In this paper we present a detailed comparison between two state-of-the-art model-based control techniques for MAV trajectory tracking. A classical Linear Model Predictive Controller (LMPC) is presented and compared against a more advanced Nonlinear Model Pr...

Abstract : This paper proposes a novel methodology that combines the differential flatness formalism for trajectory generation of nonlinear systems, and the use of a model predictive control (MPC) strategy for constraint handling. The methodology consists of a trajectory generator that generates a reference trajectory parameterised by splines, and with the property that it satisfies performance...

The lane change maneuver is among the most popular driving behaviors. It is also the basic element of important maneuvers like overtaking maneuver. Therefore, it is chosen as the focus of this study and novel multi-input multi-output adaptive neuro-fuzzy inference system models (MANFIS) are proposed for this behavior. These models are able to simulate and predict the future behavior of a Dri...

This paper employs nonlinear disturbance observer (NDO) for robust trajectory-free Nonlinear Model Predictive Control (NMPC) of biped robots. The NDO is used to reject the additive disturbances caused by parameter uncertainties, unmodeled dynamics, joints friction, and external slow-varying forces acting on the biped robots. In contrary to the slow-varying disturbances, handling sudden pushing ...

Erosion caused by sand transportation in flow changing devices is a serious concern in the hydrocarbon and mineral processing industry, which entail to failure and malfunction of flow devices. In this study, computational fluid dynamics (CFD) with discrete phase models (DPM) were employed for analysis of carbon steel long radius 90-Degree elbow erosion due to the sand concentration of 2, 5 and ...

When controlling a wheeled mobile robot with four independently steerable driving wheels, the control of the wheel coordination must be handled. Both the direction and velocity of the wheels must be coordinated to allow for proper operation of the robot. The focus of this work is on the coordination of the wheel directions. Such coordination is mostly done by solving constraint equations of the...