417 research outputs found
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Design of an adaptive neural predictive nonlinear controller for nonholonomic mobile robot system based on posture identifier in the presence of disturbance
This paper proposes an adaptive neural predictive nonlinear controller to guide a nonholonomic wheeled mobile robot during continuous and non-continuous gradients trajectory tracking. The structure of the controller consists of two models that describe the kinematics and dynamics of the mobile robot system and a feedforward neural controller. The models are modified Elman neural network and feedforward multi-layer perceptron respectively. The modified Elman neural network model is trained off-line and on-line stages to guarantee the outputs of the model accurately represent the actual outputs of the mobile robot system. The trained neural model acts as the position and orientation identifier. The feedforward neural controller is trained off-line and adaptive weights are adapted on-line to find the reference torques, which controls the steady-state outputs of the mobile robot system. The feedback neural controller is based on the posture neural identifier and quadratic performance index optimization algorithm to find the optimal torque action in the transient state for N-step-ahead prediction. General back propagation algorithm is used to learn the feedforward neural controller and the posture neural identifier. Simulation results show the effectiveness of the proposed adaptive neural predictive control algorithm; this is demonstrated by the minimised tracking error and the smoothness of the torque control signal obtained with bounded external disturbances
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Design of a cognitive neural predictive controller for mobile robot
This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel UniversityIn this thesis, a cognitive neural predictive controller system has been designed to guide a nonholonomic wheeled mobile robot during continuous and non-continuous trajectory tracking and to navigate through static obstacles with collision-free and minimum tracking error. The structure of the controller consists of two layers; the first layer is a neural network system that controls the mobile robot actuators in order to track a desired path. The second layer of the controller is cognitive layer that collects information from the environment and plans the optimal path. In addition to this, it detects if there is any obstacle in the path so it can be avoided by re-planning the trajectory using particle swarm optimisation (PSO) technique.
Two neural networks models are used: the first model is modified Elman recurrent neural network model that describes the kinematic and dynamic model of the mobile robot and it is trained off-line and on-line stages to guarantee that the outputs of the model will accurately represent the actual outputs of the mobile robot system. The trained neural model acts as the position and orientation identifier. The second model is feedforward multi-layer perceptron neural network that describes a feedforward neural controller and it is trained off-line and its weights are adapted on-line to find the reference torques, which controls the steady-state outputs of the mobile robot system. The feedback neural controller is based on the posture neural identifier and quadratic performance index predictive optimisation algorithm for N step-ahead prediction in order to find the optimal torque action in the transient to stabilise the tracking error of the mobile robot system when the trajectory of the robot is drifted from the desired path during transient state.
Three controller methodologies were developed: the first is the feedback neural controller; the second is the nonlinear PID neural feedback controller and the third is nonlinear inverse dynamic neural feedback controller, based on the back-stepping method and Lyapunov criterion. The main advantages of the presented approaches are to plan an optimal path for itself avoiding obstructions by using intelligent (PSO) technique as well as the analytically derived control law, which has significantly high computational accuracy with predictive optimisation technique to obtain the optimal torques control action and lead to minimum tracking error of the mobile robot for different types of trajectories.
The proposed control algorithm has been applied to monitor a nonholonomic wheeled mobile robot, has demonstrated the capability of tracking different trajectories with continuous gradients (lemniscates and circular) or non-continuous gradients (square) with bounded external disturbances and static obstacles. Simulations results and experimental work showed the effectiveness of the proposed cognitive neural predictive control algorithm; this is demonstrated by the minimised tracking error to less than (1 cm) and obtained smoothness of the torque control signal less than maximum torque (0.236 N.m), especially when external disturbances are applied and navigating through static obstacles.
Results show that the five steps-ahead prediction algorithm has better performance compared to one step-ahead for all the control methodologies because of a more complex control structure and taking into account future values of the desired one, not only the current value, as with one step-ahead method. The mean-square error method is used for each component of the state error vector to compare between each of the performance control methodologies in order to give better control results
Feedback Linearization Techniques for Collaborative Nonholonomic Robots
Collaborative robots performing tasks together have significant advantages over a single
robot. Applications can be found in the fields of underwater robotics, air traffic control,
intelligent highways, mines and ores detection and tele-surgery. Collaborative wheeled
mobile robots can be modeled by a nonlinear system having nonholonomic constraints.
Due to these constraints, the collaborative robots arc not stabilizable at a point by
continuous time-invariant feedback control laws. Therefore, linear control is ineffective,
even locally, and innovative design techniques are needed. One possible design technique
is feedback control and the principal interest of this thesis is to evaluate the best feedback
control technique.
Feedback linearization is one of the possible feedback control techniques. Feedback
linearization is a method of transforming a nonlinear system into a linear system using
feedback transformation. It differs from conventional Taylor series linearization since it
is achieved using exact coordinates transformation rather than by linear approximations
of the system. Linearization of the collaborative robots system using Taylor series results
in a linear system which is uncontrollable and is thus unsuitable. On the other hand, the
feedback linearized control strategies result in a stable system. Feedback linearized
control strategies can he designed based on state or input, while both state and input
linearization can be achieved using static or dynamic feedback.
In this thesis, a kinematic model of the collaborative nonholonomic robots is derived,
based on the leader-follower formation. The objective of the kinematic model is to
facilitate the design of feedback control strategies that can stabilize the system and
Minimize the error between the desired and actual trajectory. The leader-follower
formation is used in this research since the collaborative robots are assumed to have
communication capabilities only.
The kinematic model for the leader-follower formation is simulated using
MATLAB/Simulink. A comparative assessment of various feedback control strategies is
evaluated. The leader robot model is tested using five feedback control strategies for
different trajectories. These feedback control strategies are derived using cascaded
system theory, stable tracking method based on linearization of corresponding error
model, approximation linearization, nonlinear control design and full state linearization
via dynamic feedback. For posture stabilization of the leader robot, time-varying and full
state dynamic feedback linearized control strategies are used. For the follower robots
using separation bearing and separation-separation formation, the feedback linearized
control strategies are derived using input-output via static feedback.
Based on the simulation results for the leader robot, it is found that the full state dynamic
feedback linearized control strategy improves system performance and minimizes the
mean of error more rapidly than the other four feedback control strategies. In addition to
stabilizing the system, the full state dynamic feedback linearized control strategy
achieves posture stabilization. For the follower robots, the input-output via static
feedback linearization control strategies minimize the error between the desired and
actual formation. Furthermore, the input-output linearized control strategies allow
dynamical change of the formation at run-time and minimize the disturbance of formation
change. Thus, for a given feasible trajectory, the full state feedback linearized strategy for
the leader robot and input-output feedback linearized strategies for the follower robots are
found to be more efficient in stabilizing the system
Research on a semiautonomous mobile robot for loosely structured environments focused on transporting mail trolleys
In this thesis is presented a novel approach to model, control, and planning the motion of
a nonholonomic wheeled mobile robot that applies stable pushes and pulls to a
nonholonomic cart (York mail trolley) in a loosely structured environment. The method is
based on grasping and ungrasping the nonholonomic cart, as a result, the robot changes its
kinematics properties. In consequence, two robot configurations are produced by the task
of grasping and ungrasping the load, they are: the single-robot configuration and the
robot-trolley configuration. Furthermore, in order to comply with the general planar
motion law of rigid bodies and the kinematic constraints imposed by the robot wheels for
each configuration, the robot has been provided with two motorized steerable wheels in
order to have a flexible platform able to adapt to these restrictions. [Continues.
Motion Planning and Posture Control of Multiple n-link Doubly Nonholonomic Manipulators
The paper considers the problem of motion planning and posture control of multiple n-link doubly
nonholonomic mobile manipulators in an obstacle-cluttered and bounded workspace. The workspace
is constrained with the existence of an arbitrary number of fixed obstacles (disks, rods and curves),
artificial obstacles and moving obstacles. The coordination of multiple n-link doubly nonholonomic
mobile manipulators subjected to such constraints becomes therefore a challenging navigational
and steering problem that few papers have considered in the past. Our approach to developing
the controllers, which are novel decentralized nonlinear acceleration controllers, is based on a
Lyapunov control scheme that is not only intuitively understandable but also allows simple but
rigorous development of the controllers. Via the scheme, we showed that the avoidance of all types
of obstacles was possible, that the manipulators could reach a neighborhood of their goal and that
their final orientation approximated the desired orientation. Computer simulations illustrate these
results.
KEYWORDS: Lyapunov-based control scheme; Doubly nonholonomic manipulators; Ghost parking
bays; Minimum distance technique; Stability; Kinodynamic constraints
Keep Rollin' - Whole-Body Motion Control and Planning for Wheeled Quadrupedal Robots
We show dynamic locomotion strategies for wheeled quadrupedal robots, which
combine the advantages of both walking and driving. The developed optimization
framework tightly integrates the additional degrees of freedom introduced by
the wheels. Our approach relies on a zero-moment point based motion
optimization which continuously updates reference trajectories. The reference
motions are tracked by a hierarchical whole-body controller which computes
optimal generalized accelerations and contact forces by solving a sequence of
prioritized tasks including the nonholonomic rolling constraints. Our approach
has been tested on ANYmal, a quadrupedal robot that is fully torque-controlled
including the non-steerable wheels attached to its legs. We conducted
experiments on flat and inclined terrains as well as over steps, whereby we
show that integrating the wheels into the motion control and planning framework
results in intuitive motion trajectories, which enable more robust and dynamic
locomotion compared to other wheeled-legged robots. Moreover, with a speed of 4
m/s and a reduction of the cost of transport by 83 % we prove the superiority
of wheeled-legged robots compared to their legged counterparts.Comment: IEEE Robotics and Automation Letter
Adaptive consensus based formation control of unmanned vehicles
Over the past decade, the control research community has given significant attention to formation control of multiple unmanned vehicles due to a variety of commercial and defense applications. Consensus-based formation control is considered to be more robust and reliable when compared to other formation control methods due to scalability and inherent properties that enable the formation to continue even if one of the vehicles experiences a failure. In contrast to existing methods on formation control where the dynamics of the vehicles are neglected, this dissertation in the form of four papers presents consensus-based formation control of unmanned vehicles-both ground and aerial, by incorporating the vehicle dynamics.
First, neural networks (NN)-based optimal adaptive consensus-based formation control over finite horizon is presented for networked mobile robots or agents in the presence of uncertain robot/agent dynamics and communication. In the second paper, a hybrid automaton is proposed to control the nonholonomic mobile robots in two discrete modes: a regulation mode and a formation keeping mode in order to overcome well-known stabilization problem. The third paper presents the design of a distributed consensus-based event-triggered formation control of networked mobile robots using NN in the presence of uncertain robot dynamics to minimize communication. All these papers assume state availability.
Finally, the fourth paper extends the consensus effort by introducing the development of a novel nonlinear output feedback NN-based controller for a group of quadrotor UAVs --Abstract, page iv
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