Walter Fetter Lages

Point stabilization of mobile robots with nonlinear model predictive control

This paper presents an optimal control scheme based on model-based predictive control (MPC) for a wheeled mobile robot (WMR) with nonholonomic constraints. It is shown that, by using MPC, some advantages can be obtained, such as the ability to handle constraints due to state or input limitations and performance improvement. To solve some problems with other MPCs for WMRs, this paper proposes to formulate a cost function in polar coordinates. Considerations regarding the computational effort of the MPC are developed with the purpose of analysing the viability of the proposed technique in real-time.

REAL-TIME CONTROL OF A MOBILE ROBOT USING LINEARIZED MODEL PREDICTIVE CONTROL

Abstract This paper proposes an optimal control strategy for a differential-drive mobile robot. It is well known that such a system can not be feedback stabilized by a smooth time-invariant control law. By using model predictive control (MPC), an appropriate control law is implicitly obtained. Furthermore, the system physical constraints on state and inputs are dealt with in a straightforward way. The optimization problem is solved by quadratic programming (QP) and experimental results show that the control law computation can be performed under the system real-time requirements.

Robotized inspection of power lines with infrared vision

This paper presents a method to automatically detect faults in transmission lines by using thermographie images. It involves the acquisition of infrared images over a network and its processing. Since the method is intented to be used embedded in a robot, low demands on processing power are required, in order to enable inspection on large extensions of transmission lines. The automatic inspection of power lines, allows for inspections over a longer period of time and with a decreased risk for the professionals involved. Experimental results show that the proposed method is able to detect cables with problems.

PREDICTIVE CONTROL OF AN UNDERACTUATED BRACHIATION ROBOT

Abstract This work is focused on the position control of an underactuated brachiation mobile robot with 3 links. We present the modeling of the dynamics of the robot and introduce the application of the nonlinear model-based predictive control (NMPC) to such a system. The robot has 3 revolute joints but only one of them is actuated, i. e., the robot has less control inputs than degrees of freedom. The investigation of the NMPC to control such a system is due to the fact that NMPC is able do deal with constraints (state or input limitations) in a direct way, obtaining an optimal control law. Simulation results are shown, as well as some directions for future developments.

Event-triggered PI control design.

Abstract In networked control, event-triggered controllers can provide significant reduction in message traffic as well as device energy consumption, which is potentially important in wireless networks. Motivated by this fact, this work presents a systematic design method for event-triggered PI controllers. The event generation is based on the evaluation of the degradation of a linear quadratic (LQ) performance criterion. Based on the Lyapunov theory, a formal proof of asymptotic stability of the closed-loop under the asynchronous sampling strategy is provided. A simulation example illustrates the main characteristics of the proposed approach.

MPC Applied to Motion Control of an Underactuated Brachiation Robot

In this paper we present the application of the nonlinear model based predictive control to the motion control of an underactuated brachiation robot. The robot has 3 links and only one joint, the second one, is actuated. The aim of this work is to develop a control input sequence that moves continuously forward the robot over an horizontal line. We investigate the use of MPC to control the underactuated brachiation robot due to its advantages, mainly the construction of an optimal stabilizing control law and due to its ability to consider in a direct way the constraints into the optimization problem. We present computational simulations with their respective results and we highlight some important considerations.

Feedforward control of a mobile robot using a neural network

Many papers about mobile robot control deal only with the kinematic model of the robot. Since it is the kinematics and not the dynamics that introduces the nonholonomicity into the system, this approach provides a simple model while preserving the most interesting portion of the system. However, for large robots, or robots moving at high speeds, the dynamics of the body can not be neglected. This paper proposes a controller for a nonholonomic mobile robot developed by combining a kinematic controller based on nonsmooth discontinuous transformation and a dynamic controller based on a neural network (computed torque like). The overall system stability is proved by Lyapunov theory.

Adaptive Linearizing Control of Mobile Robots

Abstract Controllers for mobile robots are often designed by taking into account only the kinematics of the robot. The difficulties associated with the dynamic effects such as mass and inertia moments arise from the nonlinearities of the robot model and from the uncertain about these parameters. This paper proposes a controller based on the certainty equivalence principle that considers the dynamic effects on the robot. The proposed controller deals with the nonlinearities by means of feedback linearization, while the unknown parameters are estimated by a modified least squares algorithm. Simulated and experimental results are presented.

Control of a brachiating robot for inspection of aerial power lines

The present work addresses the problem of realtime predictive control of a brachiating robot. The robot is constrained (underactuated, limited torque) and a multivariable system, which implies a very difficult problem due to the large amount of on-line computation that is required. Previous works demonstrated that it is not possible to consider the nonlinear model-based MPC under real-time constraints. To overcome this problem we present a linearized model-based MPC, which is able to be handled under real-time constraints.

An embedded module for robotized inspection of power lines by using thermographic and visual images

This paper deals with an embedded module for automation of thermographic inspection. The module captures video streams from an infrared camera and from a visible image camera and performs an image processing to detect faults. The protocols used to capture the image streams and the image processing are discussed. The results, with the faults highlighted, are sent through an synthetic image stream to a supervision station.