Robust and adaptive chatter free formation control of wheeled mobile robots with uncertainties

This paper addresses the robust formation control of non-holonomic mobile robots with homogeneous system architecture and decentralized control structure. Therefore, it was necessary the mathematical modeling of mobile robots, from which, the Separation-Bearing variant of Leader-Following control strategy was implemented. The stability proof were based on the Lyapunov theory. The sliding mode control (SMC) strategy was used in the controller design to make the control robust to the incidence of uncertainties and disturbances. The Fuzzy Adaptive Formation Control is designed to eliminate the  previous bounding knowledge of these uncertainties and disturbances. The proposed control effectiveness is demonstrated by results obtained with simulations in Matlab/Simulink. The pure kinematic and kinematic with disturbances is also analyzed. The results shows the controllers effectiveness to formation of multi-robots systems to the eight-shaped trajectory

Application of Sliding Mode Trajectory Tracking Control Design for Two-Wheeled Mobile Robots

A trajectory tracking controller is proposed to drive the wheeled mobile robot (WMR) to follow a predefined trajectory robustly within a finite time under the presence of uncertainties. The two-wheeled mobile robot and tracking error system are modelled by kinematic equations, the stability and reachability of the sliding mode controller are analysed based on the system models. A two-wheeled mobile robot is built by using the STM32F407 (ARM Cortex-M4 microcontroller) board and a MATLAB GUI, and a cooperative real-time operating system are implemented by using C programming language in order to provide convenient system configuration and improve the overall tracking performance. It is demonstrated that the line and circular trajectories are well tracked in simulation and experiment

Enhanced vision-based localization and control for navigation of non-holonomic omnidirectional mobile robots in GPS-denied environments

New Zealand’s economy relies on primary production to a great extent, where use of the technological advances can have a significant impact on the productivity. Robotics and automation can play a key role in increasing productivity in primary sector, leading to a boost in national economy. This thesis investigates novel methodologies for design, control, and navigation of a mobile robotic platform, aimed for field service applications, specifically in agricultural environments such as orchards to automate the agricultural tasks. The design process of this robotic platform as a non-holonomic omnidirectional mobile robot, includes an innovative integrated application of CAD, CAM, CAE, and RP for development and manufacturing of the platform. Robot Operating System (ROS) is employed for the optimum embedded software system design and development to enable control, sensing, and navigation of the platform. 3D modelling and simulation of the robotic system is performed through interfacing ROS and Gazebo simulator, aiming for off-line programming, optimal control system design, and system performance analysis. Gazebo simulator provides 3D simulation of the robotic system, sensors, and control interfaces. It also enables simulation of the world environment, allowing the simulated robot to operate in a modelled environment. The model based controller for kinematic control of the non-holonomic omnidirectional platform is tested and validated through experimental results obtained from the simulated and the physical robot. The challenges of the kinematic model based controller including the mathematical and kinematic singularities are discussed and the solution to enable an optimal kinematic model based controller is presented. The kinematic singularity associated with the non-holonomic omnidirectional robots is solved using a novel fuzzy logic based approach. The proposed approach is successfully validated and tested through the simulation and experimental results. Development of a reliable localization system is aimed to enable navigation of the platform in GPS-denied environments such as orchards. For this aim, stereo visual odometry (SVO) is considered as the core of the non-GPS localization system. Challenges of SVO are introduced and the SVO accumulative drift is considered as the main challenge to overcome. SVO drift is identified in form of rotational and translational drift. Sensor fusion is employed to improve the SVO rotational drift through the integration of IMU and SVO. A novel machine learning approach is proposed to improve the SVO translational drift using Neural-Fuzzy system and RBF neural network. The machine learning system is formulated as a drift estimator for each image frame, then correction is applied at that frame to avoid the accumulation of the drift over time. The experimental results and analyses are presented to validate the effectiveness of the methodology in improving the SVO accuracy. An enhanced SVO is aimed through combination of sensor fusion and machine learning methods to improve the SVO rotational and translational drifts. Furthermore, to achieve a robust non-GPS localization system for the platform, sensor fusion of the wheel odometry and the enhanced SVO is performed to increase the accuracy of the overall system, as well as the robustness of the non-GPS localization system. The experimental results and analyses are conducted to support the methodology

適応制御戦略に基づく車輪型二足歩行ロボットのバランス安定性の向上

Tohoku University林部充宏課

Adaptive multiple-surface sliding mode control of nonholonomic systems with matched and unmatched uncertainties

The problem of stabilizing a class of nonholonomic systems in chained form affected by both matched and unmatched uncertainties is addressed in this paper. The proposed design methodology is based on a discontinuous transformation of the perturbed nonholonomic system to which an adaptive multiple-surface sliding mode technique is applied. The generation of a sliding mode allows to eliminate the effect of matched uncertainties, while a suitable function approximation technique enables to deal with the residual uncertainties, which are unmatched. The control problem is solved by choosing a particular sliding manifold upon which a second order sliding mode is enforced via a continuous control with discontinuous derivative. A positive feature of the present proposal, apart from the fact of being capable of dealing with the presence of both matched and unmatched uncertainties, is that no knowledge of the bounds of the unmatched uncertainty terms is required. Moreover, the fact of producing a continuous control makes the proposed approach particularly appropriate in nonholonomic applications, such as those of mechanical nature

Adaptive neural network tracking control for underactuated systems with matched and mismatched disturbances

This paper studies neural network-based tracking control of underactuated systems with unknown parameters and with matched and mismatched disturbances. Novel adaptive control schemes are proposed with the utilization of multi-layer neural networks, adaptive control and variable structure strategies to cope with the uncertainties containing approximation errors, unknown base parameters and time-varying matched and mismatched external disturbances. Novel auxiliary control variables are designed to establish the controllability of the non-collocated subset of the underactuated systems. The approximation errors and the matched and mismatched external disturbances are efficiently counteracted by appropriate design of robust compensators. Stability and convergence of the time-varying reference trajectory are shown in the sense of Lyapunov. The parameter updating laws for the designed control schemes are derived using the projection approach to reduce the tracking error as small as desired. Unknown dynamics of the non-collocated subset is approximated through neural networks within a local region. Finally, simulation studies on an underactuated manipulator and an underactuated vibro-driven system are conducted to verify the effectiveness of the proposed control schemes

Stable and Adaptive Control for Wheeled Mobile Platform

ABSTRACT: Most differential drive platforms are equipped with two independent actuators and casters. The positions of the gravity center and the rotation center often do not coincide. This position difference, combined with the effect of unbalanced actuator dynamics on the motion, makes it difficult to properly control the platform. We propose an adaptive nonlinear controller system based on the Lyapunov stability theory that greatly improves the trajectory tracking performance of such platforms. The asymptotically stable kinematic controller takes into account the position difference and the effect of the unbalanced actuator dynamics. The dynamic controller has the desirable property that it requires minimal knowledge of the platform physical parameters. Validation was performed through simulation and several experiments conducted on a rear driven powered wheelchair. Comparative experimental studies suggested that the proposed adaptive control system performs better than a similar method presented in the literature for linear as well as curvilinear trajectory tracking. Furthermore, the control system exhibits good tracking performance on inclined plans and non smooth surfaces

Robust Model Predictive Control for Linear Parameter Varying Systems along with Exploration of its Application in Medical Mobile Robots

This thesis seeks to develop a robust model predictive controller (MPC) for Linear Parameter Varying (LPV) systems. LPV models based on input-output display are employed. We aim to improve robust MPC methods for LPV systems with an input-output display. This improvement will be examined from two perspectives. First, the system must be stable in conditions of uncertainty (in signal scheduling or due to disturbance) and perform well in both tracking and regulation problems. Secondly, the proposed method should be practical, i.e., it should have a reasonable computational load and not be conservative. Firstly, an interpolation approach is utilized to minimize the conservativeness of the MPC. The controller is calculated as a linear combination of a set of offline predefined control laws. The coefficients of these offline controllers are derived from a real-time optimization problem. The control gains are determined to ensure stability and increase the terminal set. Secondly, in order to test the system's robustness to external disturbances, a free control move was added to the control law. Also, a Recurrent Neural Network (RNN) algorithm is applied for online optimization, showing that this optimization method has better speed and accuracy than traditional algorithms. The proposed controller was compared with two methods (robust MPC and MPC with LPV model based on input-output) in reference tracking and disturbance rejection scenarios. It was shown that the proposed method works well in both parts. However, two other methods could not deal with the disturbance. Thirdly, a support vector machine was introduced to identify the input-output LPV model to estimate the output. The estimated model was compared with the actual nonlinear system outputs, and the identification was shown to be effective. As a consequence, the controller can accurately follow the reference. Finally, an interpolation-based MPC with free control moves is implemented for a wheeled mobile robot in a hospital setting, where an RNN solves the online optimization problem. The controller was compared with a robust MPC and MPC-LPV in reference tracking, disturbance rejection, online computational load, and region of attraction. The results indicate that our proposed method surpasses and can navigate quickly and reliably while avoiding obstacles