This paper presents a PID fast terminal sliding mode dynamic inverse control method for wheeled mobile robots. Because of the nonlinear and nonholonomic properties, it is difficult to establish an appropriate model of the mobile robot system for trajectory tracking. The PID Control is based on a fast terminal sliding mode control to ensure asymptotic stabilization of the robot's position and orientation around the desired trajectory, taking into account the kinematics and dynamics of the robot. The idea behind this strategy is to use the terminal sliding mode control approach to assure the finite time convergence of tracking errors to zero. Simulation works demonstrate the efficacy of the proposed system for mobile robots robust tracking trajectory.

This paper presents a PID fast terminal sliding mode dynamic inverse control method for wheeled mobile robots. Because of the nonlinear and nonholonomic properties, it is difficult to establish an appropriate model of the mobile robot system for trajectory tracking. The PID Control is based on a fast terminal sliding mode control to ensure asymptotic stabilization of the robot's position and orientation around the desired trajectory, taking into account the kinematics and dynamics of the robot. The idea behind this strategy is to use the terminal sliding mode control approach to assure the finite time convergence of tracking errors to zero. Simulation works demonstrate the efficacy of the proposed system for mobile robots robust tracking trajectory.

This paper presents a PID fast terminal sliding mode dynamic inverse control method for wheeled mobile robots. Because of the nonlinear and nonholonomic properties, it is difficult to establish an appropriate model of the mobile robot system for trajectory tracking. The PID Control is based on a fast terminal sliding mode control to ensure asymptotic stabilization of the robot's position and orientation around the desired trajectory, taking into account the kinematics and dynamics of the robot. The idea behind this strategy is to use the terminal sliding mode control approach to assure the finite time convergence of tracking errors to zero. Simulation works demonstrate the efficacy of the proposed system for mobile robots robust tracking trajectory.

This article deals with the study of a new swimming microrobot behavior using an analytical investigation. The analyzed microrobot is associated by a spherical head and hybrid tail. The principle of modeling is based on solving of the coupled elastic/fluidic problems between the hybrid tail’s deflections and the running environment. In spite of the resulting nonlinear model can be exploited to enhance both the sailing ability and also can be controlled in viscous environment using nonlinear control investigations. The applications of the micro-robot have required the precision of control for targeting the running area in terms of response time and tracking error. Due to these limitations, the Flatness-ANFIS based control is used to ensure a good control behavior in hazardous environment. Our control investigation is coupled the differential flatness and adaptive neuro-fuzzy inference techniques, in which the flatness is used to planning the optimal trajectory and eliminate the nonlinearity effects of the resulting model. In other hand, the neuro-fuzzy inference technique is used to build the law of control technique and minimize the dynamic error of tracking trajectory. In particular, we deduct from a non linear model to an optimal model of the design parameter’s using Multi-Objective genetic algorithms (MOGAs). In addition, Computational fluid dynamics modeling of the microrobot is also carried out to study the produced thrust and velocity of the microrobot displacement taking into account the fluid parameters. Our analytical results have been validated by the recorded good agreement between the numerical and analytical results.

This article deals with the study of a new swimming microrobot behavior using an analytical investigation. The analyzed microrobot is associated by a spherical head and hybrid tail. The principle of modeling is based on solving of the coupled elastic/fluidic problems between the hybrid tail’s deflections and the running environment. In spite of the resulting nonlinear model can be exploited to enhance both the sailing ability and also can be controlled in viscous environment using nonlinear control investigations. The applications of the micro-robot have required the precision of control for targeting the running area in terms of response time and tracking error. Due to these limitations, the Flatness-ANFIS based control is used to ensure a good control behavior in hazardous environment. Our control investigation is coupled the differential flatness and adaptive neuro-fuzzy inference techniques, in which the flatness is used to planning the optimal trajectory and eliminate the nonlinearity effects of the resulting model. In other hand, the neuro-fuzzy inference technique is used to build the law of control technique and minimize the dynamic error of tracking trajectory. In particular, we deduct from a non linear model to an optimal model of the design parameter’s using Multi-Objective genetic algorithms (MOGAs). In addition, Computational fluid dynamics modeling of the microrobot is also carried out to study the produced thrust and velocity of the microrobot displacement taking into account the fluid parameters. Our analytical results have been validated by the recorded good agreement between the numerical and analytical results.

This article deals with the study of a new swimming microrobot behavior using an analytical investigation. The analyzed microrobot is associated by a spherical head and hybrid tail. The principle of modeling is based on solving of the coupled elastic/fluidic problems between the hybrid tail’s deflections and the running environment. In spite of the resulting nonlinear model can be exploited to enhance both the sailing ability and also can be controlled in viscous environment using nonlinear control investigations. The applications of the micro-robot have required the precision of control for targeting the running area in terms of response time and tracking error. Due to these limitations, the Flatness-ANFIS based control is used to ensure a good control behavior in hazardous environment. Our control investigation is coupled the differential flatness and adaptive neuro-fuzzy inference techniques, in which the flatness is used to planning the optimal trajectory and eliminate the nonlinearity effects of the resulting model. In other hand, the neuro-fuzzy inference technique is used to build the law of control technique and minimize the dynamic error of tracking trajectory. In particular, we deduct from a non linear model to an optimal model of the design parameter’s using Multi-Objective genetic algorithms (MOGAs). In addition, Computational fluid dynamics modeling of the microrobot is also carried out to study the produced thrust and velocity of the microrobot displacement taking into account the fluid parameters. Our analytical results have been validated by the recorded good agreement between the numerical and analytical results.