Nardênio Almeida Martins

Trajectory tracking of a nonholonomic mobile robot with parametric and nonparametric uncertainties: A proposed neural control

In this paper, a trajectory tracking control for a nonholonomic mobile robot by the integration of a kinematic neural controller (KNC) and a torque neural controller (TNC) is proposed, where both the kinematic and dynamic models contains parametric and nonparametric uncertainties. The proposed neural controller (PNC) is constituted of the KNC and the TNC, and designed by use of a modeling technique of Gaussian radial basis function neural networks (RBFNNs). The KNC is applied to compensate the parametric uncertainties of the mobile robot kinematics. The TNC, based on the sliding mode theory, is constituted of a dynamic neural controller (DNC) and a robust neural compensator (RNC), and applied to compensate the mobile robot dynamics, significant uncertainties, bounded unknown disturbances, neural network modeling errors, influence of payload, and unknown kinematic parameters. To alleviate the problems met in practical implementation using classical sliding mode controllers and to eliminate the chattering phenomenon is used the RNC of the TNC, which is nonlinear and continuous, in lieu of the discontinuous part of the control signals present in classical forms. Also, the PNC neither requires the knowledge of the mobile robot kinematics and dynamics nor the time-consuming training process. Stability analysis and convergence of tracking errors to zero as well as the learning algorithms for weights are guaranteed with basis on Lyapunov method. Simulations results are provided to show the effectiveness of the proposed approach.

Trajectory Tracking of a Nonholonomic Mobile Robot with Kinematic Disturbances: A Variable Structure Control Design

In this paper, a trajectory tracking control for a nonholonomic mobile robot subjected to kinematic disturbances is proposed. A variable structure controller based on the sliding mode theory is designed, and applied to compensate these disturbances. To minimize the problems found in practical implementations of the classical variable structure controllers, and eliminate the chattering phenomenon, is used a neural compensator, which is nonlinear and continuous, in lieu of the discontinuous portion of the control signals present in classical forms. This proposed neural compensator is designed by the Gaussian radial basis function neural networks modeling technique and it does not require the time-consuming training process. Stability analysis is guaranteed based on the Lyapunov method. Simulation results are provided to show the effectiveness of the proposed approach.

Neural Dynamic Control of a Nonholonomic Mobile Robot Incorporating the Actuator Dynamics

In this paper, a trajectory tracking control for a nonholonomic mobile robot by the integration of a kinematic controller and neural dynamic controller is investigated, where the wheel actuator (e.g., dc motor) dynamics is integrated with mobile robot dynamics and kinematics so that the actuator input voltages are the control inputs. The proposed neural dynamic controller (PNDC), based on the sliding mode theory, is applied to compensate the mobile robot dynamics, bounded unknown disturbances, and influence of payload. This controller is obtained by modeling the Radial Basis Functions Neural Networks (RBFNNs) of the centripetal and Coriolis matrix through of the inertia matrix of the mobile robot dynamics. Thus, PNDC is constituted of static RBFNNs only, what makes possible the reduction of the size of the RBFNNs, of the computational load and the implementation in real time. Stability analysis and numerical simulations are provided to show the effectiveness of the PNDC.

Neural Control Applied to the Problem of Trajectory Tracking of Mobile Robots with Uncertainties

In this paper, a trajectory tracking control for a nonholonomic mobile robot by the integration of a kinematic neural controller (KNC) and a torque neural controller (TNC) is proposed, where both the kinematic and dynamic models contains parametric and nonparametrics uncertainties. The proposed neural controller (PNC) is constituted of the KNC and the TNC, and were designed by use of a modeling technique of Gaussian radial basis function neural networks (RBFNNs). The KNC is applied to compensate the uncertainties of the mobile robot. The TNC, based on the computed torque control, is applied to compensate the mobile robot dynamics, significant uncertainties, bounded unknown disturbances, neural networks modeling errors, influence of payload, and unknown kinematic parameters. Also, the PNC are not dependent of the mobile robot kinematics and dynamics neither require the off-line training process. Stability analysis and convergence of tracking errors to zero, as well as the learning algorithms for weights, centers, and variances (becoming nonlinearly parameterized RBFNNs) are guaranteed with basis on Lyapunov theory. In addition, the simulations results are provided to show the efficiency of the PNC.

Control of Mobile Robot Considering Actuator Dynamics with Uncertainties in the Kinematic and Dynamic Models

In this paper, a trajectory tracking control for a nonholonomic mobile robot by the integration of a neural kinematic controller (NKC) and neural dynamic controller (NDC) is investigated, where the wheel actuator (e.g., dc motor) dynamics is integrated with mobile robot dynamics and kinematics so that the actuator input voltages are the control inputs, as well as both the kinematic and dynamic models contains parametric and/or nonparametric uncertainties. The proposed neural controller (PNC) is constituted of the NKC and the NDC, and were designed by use of a modelling technique of Gaussian radial basis function neural networks (RBFNNs). The NKC is applied to compensate the uncertainties in the kinematic parameters of the mobile robot. The NDC, based on the sliding mode theory, is applied to compensate the mobile robot dynamics, and parametric and/or nonparametric uncertainties. Also, the PNC are not dependent of the mobile robot kinematics and dynamics neither require the off-line training process. Stability analysis with basis on Lyapunov theory and numerical simulation is provided to show the effectiveness of the PNC.

Design of an Autopilot for Cessna 182

There is currently an increasing demand for automatic control systems due to their several applications in the civilian and military domains. Autopilot systems improve the guidance and piloting mechanisms of the aircrafts. This paper presents the design of an autopilot system and its respective simulation. In particular, our design is simulated for the Cessna 182. The implementation of the designed controllers for the proposed autopilot was undertaken using an Arduino board which was tested together with a flight simulator. The communication between the devices was carried out through a data communication network so that the information processed by the controllers was sent directly to the control surfaces of the aircraft represented in the flight simulator. A case study was developed where the proposed autopilot was submitted to several flight conditions. The results obtained indicate that models and hardware used to design the autopilot system are feasible and kept the aircraft stabilized.

Trajectory Tracking of a Nonholonomic Mobile Robot Considering the Actuator Dynamics: Design of a Neural Dynamic Controller Based on Sliding Mode Theory

In this paper, a trajectory tracking control for a nonholonomic mobile robot by the integration of a kinematic controller and neural dynamic controller is investigated, where the wheel actuator dynamics is integrated with mobile robot dynamics and kinematics so that the actuator input voltages are the control inputs. The proposed neural dynamic controller (PNDC), based on the sliding mode theory, is constituted by a neural voltage controller (NVC) and a neural robust compensator (NRC), which has as objective compensates the uncertainties and disturbances in the dynamics. Stability analysis and numerical simulation are provided to show the effectiveness of the PNDC.

Image-based camera-robot target-tracking satisfying multicriteria constraints

Abstract This paper presents an image-based camera control, mounted on a nonholonomic mobile robot platform, traking a mobile target as reference, via task function approach. The system stability is guaranteed by the Lyapunov theory. Due to parametric uncertainties (target depth), actuator (acceleration and velocity) and visual constraints, the gain is generated via LMIs (Linear Matrix Inequalities), in order to maximize the stability region associated with the closed loop. A convex optimization package was used to obtain the feedback gain, and simulations are presented to visualize the system behavior.