A brief review of neural networks based learning and control and their applications for robots

As an imitation of the biological nervous systems, neural networks (NN), which are characterized with powerful learning ability, have been employed in a wide range of applications, such as control of complex nonlinear systems, optimization, system identification and patterns recognition etc. This article aims to bring a brief review of the state-of-art NN for the complex nonlinear systems. Recent progresses of NNs in both theoretical developments and practical applications are investigated and surveyed. Specifically, NN based robot learning and control applications were further reviewed, including NN based robot manipulator control, NN based human robot interaction and NN based behavior recognition and generation

Underwater dual manipulators-Part I: Hydrodynamics analysis and computation

1098-1103This paper introduces two 4-DOF underwater manipulators mounted on autonomous underwater vehicle (AUV) with grasping claws, such that the AUV can accomplish the underwater task by using dual manipulators. Mechanical design of the manipulator is briefly presented and the feature of the simple structure of dual manipulators is simulated by using Solid Works. In addition, the hydrodynamics of the manipulator is analyzed, considering drag force, added mass and buoyancy. Then, hydrodynamic simulations of the manipulator are conducted by using 3-D model with Adams software, from which the torque of each joint is calculated. This paper presents an integrated result of computed torques by combining the theoretical calculation and simulation results, which is instrumental in determining the driving torque of the manipulators

Intelligent control of nonlinear systems with actuator saturation using neural networks

Common actuator nonlinearities such as saturation, deadzone, backlash, and hysteresis are unavoidable in practical industrial control systems, such as computer numerical control (CNC) machines, xy-positioning tables, robot manipulators, overhead crane mechanisms, and more. When the actuator nonlinearities exist in control systems, they may exhibit relatively large steady-state tracking error or even oscillations, cause the closed-loop system instability, and degrade the overall system performance. Proportional-derivative (PD) controller has observed limit cycles if the actuator nonlinearity is not compensated well. The problems are particularly exacerbated when the required accuracy is high, as in micropositioning devices. Due to the non-analytic nature of the actuator nonlinear dynamics and the fact that the exact actuator nonlinear functions, namely operation uncertainty, are unknown, the saturation compensation research is a challenging and important topic with both theoretical and practical significance. Adaptive control can accommodate the system modeling, parametric, and environmental structural uncertainties. With the universal approximating property and learning capability of neural network (NN), it is appealing to develop adaptive NN-based saturation compensation scheme without explicit knowledge of actuator saturation nonlinearity. In this dissertation, intelligent anti-windup saturation compensation schemes in several scenarios of nonlinear systems are investigated. The nonlinear systems studied within this dissertation include the general nonlinear system in Brunovsky canonical form, a second order multi-input multi-output (MIMO) nonlinear system such as a robot manipulator, and an underactuated system-flexible robot system. The abovementioned methods assume the full states information is measurable and completely known. During the NN-based control law development, the imposed actuator saturation is assumed to be unknown and treated as the system input disturbance. The schemes that lead to stability, command following and disturbance rejection is rigorously proved, and verified using the nonlinear system models. On-line NN weights tuning law, the overall closed-loop performance, and the boundedness of the NN weights are rigorously derived and guaranteed based on Lyapunov approach. The NN saturation compensator is inserted into a feedforward path. The simulation conducted indicates that the proposed schemes can effectively compensate for the saturation nonlinearity in the presence of system uncertainty

Modeling and Control of Flexible Link Manipulators

Autonomous maritime navigation and offshore operations have gained wide attention with the aim of reducing operational costs and increasing reliability and safety. Offshore operations, such as wind farm inspection, sea farm cleaning, and ship mooring, could be carried out autonomously or semi-autonomously by mounting one or more long-reach robots on the ship/vessel. In addition to offshore applications, long-reach manipulators can be used in many other engineering applications such as construction automation, aerospace industry, and space research. Some applications require the design of long and slender mechanical structures, which possess some degrees of flexibility and deflections because of the material used and the length of the links. The link elasticity causes deflection leading to problems in precise position control of the end-effector. So, it is necessary to compensate for the deflection of the long-reach arm to fully utilize the long-reach lightweight flexible manipulators. This thesis aims at presenting a unified understanding of modeling, control, and application of long-reach flexible manipulators. State-of-the-art dynamic modeling techniques and control schemes of the flexible link manipulators (FLMs) are discussed along with their merits, limitations, and challenges. The kinematics and dynamics of a planar multi-link flexible manipulator are presented. The effects of robot configuration and payload on the mode shapes and eigenfrequencies of the flexible links are discussed. A method to estimate and compensate for the static deflection of the multi-link flexible manipulators under gravity is proposed and experimentally validated. The redundant degree of freedom of the planar multi-link flexible manipulator is exploited to minimize vibrations. The application of a long-reach arm in autonomous mooring operation based on sensor fusion using camera and light detection and ranging (LiDAR) data is proposed.publishedVersio

Iterative Learning Control and its Applications

Robotic manipulators and Unmanned Aerial Vehicles (UAVs) have been used to execute some repeatable assignments, due to the advantage of safety, convenience, and flexibility. Iterative learning control (ILC) is an approach to eliminate some repeatable disturbance which may come from unknown parameters, dynamic uncertainties, or the surroundings. Therefore, this research aims to present two types of iterative learning controller, PD-type and adaptive-type, to implement on robotic manipulator and UAVs, which would complete the given repetitive missions and achieve the expected specifications. Meanwhile, a dead zone inverse model is proposed to solve the actuator dead zone problem. The traditional hierarchical control method for UAVs is adopted. The inner loop control performance is verified using Gimbal. The free flight experimental test is completed with the purpose of certifying the proposed out loop control stratagem. In addition, theoretical proof and simulation results are also presented to demonstrate the effectiveness of the proposed controllers

Reinforcement learning control of a flexible two-link manipulator: an experimental investigation

This article discusses the control design and experiment validation of a flexible two-link manipulator (FTLM) system represented by ordinary differential equations (ODEs). A reinforcement learning (RL) control strategy is developed that is based on actor-critic structure to enable vibration suppression while retaining trajectory tracking. Subsequently, the closed-loop system with the proposed RL control algorithm is proved to be semi-global uniform ultimate bounded (SGUUB) by Lyapunov's direct method. In the simulations, the control approach presented has been tested on the discretized ODE dynamic model and the analytical claims have been justified under the existence of uncertainty. Eventually, a series of experiments in a Quanser laboratory platform are investigated to demonstrate the effectiveness of the presented control and its application effect is compared with PD control

Towards a more robust non-rigid robotic joint

The following paper presents an improved, low cost, non-rigid joint that can be used in both robotic manipulators and leg-based traction robotic systems. This joint is an improvement over the previous one presented by the same authors because it is more robust. The design iterations are presented and the final system has been modeled including some nonlinear blocks. A control architecture is proposed that allows compliant control to be used under adverse conditions or in uncontrolled environments. The presented joint is a cost-effective solution that can be used when normal rigid joints are not suitable.info:eu-repo/semantics/publishedVersio

Fuzzy tuned PID controller for envisioned agricultural manipulator

The implementation of image-based phenotyping systems has become an important aspect of crop and plant science research which has shown tremendous growth over the years. Accurate determination of features using images requires stable imaging and very precise processing. By installing a camera on a mechanical arm driven by motor, the maintenance of accuracy and stability becomes non-trivial. As per the state-of-the-art, the issue of external camera shake incurred due to vibration is a great concern in capturing accurate images, which may be induced by the driving motor of the manipulator. So, there is a requirement for a stable active controller for sufficient vibration attenuation of the manipulator. However, there are very few reports in agricultural practices which use control algorithms. Although, many control strategies have been utilized to control the vibration in manipulators associated to various applications, no control strategy with validated stability has been provided to control the vibration in such envisioned agricultural manipulator with simple low-cost hardware devices with the compensation of non-linearities. So, in this work, the combination of proportional-integral-differential (PID) control with type-2 fuzzy logic (T2-F-PID) is implemented for vibration control. The validation of the controller stability using Lyapunov analysis is established. A torsional actuator (TA) is applied for mitigating torsional vibration, which is a new contribution in the area of agricultural manipulators. Also, to prove the effectiveness of the controller, the vibration attenuation results with T2-F-PID is compared with conventional PD/PID controllers, and a type-1 fuzzy PID (T1-F-PID) controller

Underwater dual manipulators-Part II: Kinematics analysis and numerical simulation

1104-1112This paper introduces dual-arm underwater manipulators mounted on an autonomous underwater vehicle (AUV), which can accomplish the underwater handling task. Firstly, the mechanical structure of the dual-arm system is briefly introduced, wherein each 4-DOF manipulator has an additional grasping function. In addition, the kinematics model of the manipulator is derived by using the improved D-H method. Secondly, the working space of the underwater dual-arm system is analyzed, which is obtained by using Monte Carlo method. The cubic polynomial interpolation and the five polynomial interpolation trajectory planning methods are compared in the joint space. Finally, with the help of the Robotics Toolbox software, the numerical test is conducted to verify the functions of the underwater dual-arm manipulator system