Control strategies for robotic manipulators

This survey is aimed at presenting the major robust control strategies for rigid robot manipulators. The techniques discussed are feedback linearization/Computed torque control, Variable structure compensator, Passivity based approach and Disturbance observer based control. The first one is based on complete dynamic model of a robot. It results in simple linear control which offers guaranteed stability. Variable structure compensator uses a switching/relay action to overcome dynamic uncertainties and disturbances. Passivity based controller make use of passive structure of a robot. If passivity of a feedback system is proved, nonlinearities and uncertainties will not affect the stability. Disturbance observer based controllers estimate disturbances, which can be cancelled out to achieve a nominal model, for which a simple controller can then be designed. This paper, after explaining each control strategy in detail, finally compares these strategies for their pros and cons. Possible solutions to cope with the drawbacks have also been presented in tabular form. © 2012 IEEE

Adaptive Neural Network Robust Control for Space Robot with Uncertainty

The trajectory tracking problems of a class of space robot manipulators with parameters and non-parameters uncertainty are considered. An adaptive robust control algorithm based on neural network is proposed by the paper. Neutral network is used to adaptive learn and compensate the unknown system for parameters uncertainties, the weight adaptive laws are designed by the paper, System stability base on Lyapunov theory is analysised to ensure the convergence of the algorithm. Non-parameters uncertainties are estimated and compensated by robust controller. It is proven that the designed controller can guarantee the asymptotic convergence of tracking error. The controller could guarantee good robust and the stability of closed-loop system. The simulation results show that the presented method is effective

Adaptive Control for Robotic Manipulators base on RBF Neural Network

An adaptive neural network controller is brought forward by the paper to solve trajectory tracking problems of robotic manipulators with uncertainties.  The  first  scheme consists of  a PD feedback  and  a  dynamic  compensator  which is  composed by  neural  network controller and  variable  structure controller.  Neutral network controller is designed to adaptive learn and compensate the unknown uncertainties, variable   structure controller is designed to eliminate approach errors of neutral network. The adaptive weight learning algorithm of neural network is designed to ensure online real-time adjustment, offline learning phase is not need; Global asymptotic stability (GAS) of system base on Lyapunov theory is analysised to ensure the convergence of the algorithm. The simulation results show that the kind of the control scheme is effective and has good robustness

Robotic contour tracking with adaptive feedforward control by fuzzy online tuning

Industrial robots have great importance in manufacturing. Typical uses of the robots are welding, painting, deburring, grinding, polishing and shape recovery. Most of these tasks such as grinding, deburring need force control to achieve high performance. These tasks involve contour following. Contour following is a challenging task because in many of applications the geometry physical of the targeted contour are unknown. In addition to that, achieving tasks as polishing, grinding and deburring requires small force and velocity tracking errors. In order to accomplish these tasks, disturbances have to be taken account. In this thesis the aim is to achieve contour tracking with using fuzzy online tuning. The fuzzy method is proposed in this thesis to adjust a feedforward force control parameter. In this technique, the varying feedforward control parameter compensates for disturbance effects. The method employs the chattering of control signal and the normal force and tangential velocity errors to adjust the control term. Simulations with the model of a direct drive planar elbow manipulator are used to last proposed technique

Review and Analysis on Main Technology of Exoskeletal Robot System for Upper Limbs Rehabilitation

Major function of exoskeletal robot system for upper limbs rehabilitation is to assist patient to carry out upper limbs’ rehabilitation training. Main technology of exoskeletal robot system for upper limbs rehabilitation includes design of mechanical structure of exoskeletal robot, design of control system of exoskeletal robot and implemention of data and information transmission between exoskeletal robot and upper limbs of human body. Currently implemention of data and information transmission rely mainly on methods of acquiring sEMG signal and force feedback. Reviewing and analyzing the specific technical development and deficiency in field of exoskeletal robot system for upper limbs rehabilitation will be important way in improving and upgrading the technology in future

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

Development of Motion Control Systems for Hydraulically Actuated Cranes with Hanging Loads

Automation has been used in industrial processes for several decades to increase efficiency and safety. Tasks that are either dull, dangerous, or dirty can often be performed by machines in a reliable manner. This may provide a reduced risk to human life, and will typically give a lower economic cost. Industrial robots are a prime example of this, and have seen extensive use in the automotive industry and manufacturing plants. While these machines have been employed in a wide variety of industries, heavy duty lifting and handling equipment such as hydraulic cranes have typically been manually operated. This provides an opportunity to investigate and develop control systems to push lifting equipment towards the same level of automation found in the aforementioned industries. The use of winches and hanging loads on cranes give a set of challenges not typically found on robots, which requires careful consideration of both the safety aspect and precision of the pendulum-like motion. Another difference from industrial robots is the type of actuation systems used. While robots use electric motors, the cranes discussed in this thesis use hydraulic cylinders. As such, the dynamics of the machines and the control system designmay differ significantly. In addition, hydraulic cranes may experience significant deflection when lifting heavy loads, arising from both structural flexibility and the compressibility of the hydraulic fluid. The work presented in this thesis focuses on motion control of hydraulically actuated cranes. Motion control is an important topic when developing automation systems, as moving from one position to another is a common requirement for automated lifting operations. A novel path controller operating in actuator space is developed, which takes advantage of the load-independent flow control valves typically found on hydraulically actuated cranes. By operating in actuator space the motion of each cylinder is inherently minimized. To counteract the pendulum-like motion of the hanging payload, a novel anti-swing controller is developed and experimentally verified. The anti-swing controller is able to suppress the motion from the hanging load to increase safety and precision. To tackle the challenges associated with the flexibility of the crane, a deflection compensator is developed and experimentally verified. The deflection compensator is able to counteract both the static deflection due to gravity and dynamic de ection due to motion. Further, the topic of adaptive feedforward control of pressure compensated cylinders has been investigated. A novel adaptive differential controller has been developed and experimentally verified, which adapts to system uncertainties in both directions of motion. Finally, the use of electro-hydrostatic actuators for motion control of cranes has been investigated using numerical time domain simulations. A novel concept is proposed and investigated using simulations.publishedVersio

Performance Comparison of Several Control Algorithms for Tracking Control of Pantograph Mechanism

A sort of parallel manipulator known as a pantograph robot mechanism was created primarily for industrial requests that required high precision and satisfied speed. While tracking a chosen trajectory profile requires a powerful controller. Because it has four active robot links and one robot passive link in place of just two links like the open chain does, it can carry more loads than the open chain robot mechanism while maintaining accuracy and stability. The calculated model for a closed chain pantograph robot mechanism presented in this paper takes into account the boundary conditions. For the purpose of simulating the dynamics of the pantograph robot mechanism, an entire MATLAB Simulink has been created. The related Simscape model had been created to verify the pantograph mathematical model that had been provided. Five alternative tracking controllers were also created and improved using the Flower Pollination (FP) algorithm. The PID controller, which is used in many engineering applications, is the first control. An enriched Fractional Order PID (FOPID) controller is the second control. The third control considers an improved Nonlinear conventional PID (NLPID) controller, and the parameters for this controller were likewise determined using (FP) optimization using the useful objective function. Model Reference Adaptive Control (MRAC) with PID Compensator is the fourth control. The Fuzzy PD+I Control is the last and final controller. A comparison of the different control methods was completed. A rectangular trajectory was chosen as the end effector of the pantograph robot\u27s position reference because it displays performance during sharp edges and provides a more accurate study. The proposed controllers were used for this task to analyse the performance. The outcomes demonstrate that the Fuzzy PD+I control outperforms the PID, FOPID, NLPID, and MRAC with PID Compensator controllers in terms of performance. In the case of the Fuzzy PD+I control, the angles end effector has a lower rise time, a satisfied settling time, and low overshoot with good precision