Integrated Direct/Indirect Adaptive Robust Control of Multi-DOF Hydraulic Robotic Arms

2007 ◽  
Author(s):  
Amit Mohanty ◽  
Bin Yao
Keyword(s):  
Parameter Estimation ◽  
Robust Control ◽  
Adaptive Robust Control ◽  
Least Square ◽  
Parametric Uncertainties ◽  
Robot Arm ◽  
Transient Performance ◽  
Seamless Integration ◽  
Output Tracking ◽  
Indirect Adaptive Robust Control

In a general DIARC framework [13], the emphasis is always on the guaranteed transient performance and accurate trajectory tracking in the presence of uncertain nonlinearity and parametric uncertainties along with accurate parameter estimation for secondary purpose such as system health monitoring and prognosis. Need for accurate parameter estimation calls for the use of Least Square Estimation (LSE) type of algorithms for such a seamless integration of good tracking performance and accurate parameter estimation. This paper presents a physical model based integrated direct/indirect adaptive robust control (DIARC) strategy for a hydraulically actuated 3-DOF robotic arm. To avoid the need of acceleration feedback for DIARC back-stepping design, the property, that the adjoint matrix and the determinant of the inertial matrix could be linearly parameterized by certain suitably selected parameters is utilized. Unlike gradient-type parameter estimation law, which used overparamterization, there is no multiple estimation of the single parameter. Theoretically, the resulting controller is able to take into account not only the effect of parametric uncertainties coming from the payload and various hydraulic parameters but also the effect of uncertain nonlinearities. Furthermore, the proposed DIARC controller guarantees a prescribed output tracking transient performance and final tracking accuracy while achieving asymptotic output tracking in the presence of parametric uncertainties only. Simulation results based on a three degree-of-freedom (DOF) hydraulic robot arm (a scaled down version of an industrial back-hoe/excavator arm) are presented to illustrate the proposed control algorithm.

Integrated Direct/Indirect Adaptive Robust Control (DIARC) of Hydraulic Robotics Arm With Accurate Parameter Estimates

2006 ◽  
Author(s):  
Amit Mohanty ◽  
Bin Yao
Keyword(s):  
Parameter Estimation ◽  
Robust Control ◽  
Trajectory Tracking ◽  
Adaptive Robust Control ◽  
Tracking Performance ◽  
Robotic Arm ◽  
Parameter Estimates ◽  
Robust Controller ◽  
Seamless Integration ◽  
Indirect Adaptive Robust Control

In a general Adaptive Robust Control (ARC) framework, the emphasis is always on the guaranteed transient performance and accurate trajectory tracking in the presence of uncertain nonlinearity and parametric uncertainties. However, when secondary purposes such as system health monitoring and prognosis are of equal importance, intelligent integration of output tracking performance oriented direct adaptive robust control (DARC) and the recently proposed accurate parameter estimation-based indirect adaptive robust control (IARC) is required. In this paper, we will consider such a seamless integration for a hydraulic robotic arm. The newly developed IARC design is first applied to the trajectory tracking for the robotic arm but with an improved estimation model, in which accurate parameter estimates are obtained through a parameter estimation algorithm that is based on physical dynamics rather than the tracking error dynamics. An integrated direct/indirect adaptive robust controller (DIARC) is then presented that preserves the excellent transient tracking performance of the direct ARC designs as well as the better parameter estimation process of the IARC design. The proposed Integrated Direct/Indirect Adaptive Robust Controller (DIARC) achieves the controller-identifier separation, thus enabling certain modularity in the controller design.

Nonlinear Model Based Coordinated Adaptive Robust Control of Electro-Hydraulic Robotic Manipulators: Methods and Comparative Studies

2001 ◽  
Author(s):  
Fanping Bu ◽  
Bin Yao
Keyword(s):  
Robust Control ◽  
Comparative Studies ◽  
Adaptive Robust Control ◽  
Robot Arm ◽  
Tracking Accuracy ◽  
Output Tracking ◽  
Model Based ◽  
Acceleration Feedback ◽  
Hydraulic Cylinders ◽  
Electrical Motors

Abstract Compared to conventional robot manipulators driven by electrical motors, hydraulic robot arms have richer nonlinear dynamics and stronger couplings among various joints (or hydraulic cylinders). This paper focuses on the physical model based coordinated adaptive robust control (ARC) strategies that explicitly take into account the strong coupling among various hydraulic cylinders (or joints). In our recent studies, two such methods were proposed to avoid the need of acceleration feedback in doing ARC backstepping designs. The first method uses an observer to recover the state needed for the ARC backstepping design. The second method utilizes the property that the adjoint matrix and the determinant of the inertial matrix can be linearly parametrized by certain suitably selected parameters and employ certain over-parametrizing techniques. Theoretically, both the resulting ARC controllers guarantee a prescribed output tracking transient performance and final tracking accuracy while achieving asymptotic output tracking in the presence of parametric uncertainties only. This paper focuses on the comparative studies of these two methods under various practical constraints. Extensive simulation results which are based on a three degree-of-freedom (DOF) hydraulic robot arm are presented to illustrate the advantages and drawbacks of each method.

Indirect Neural Network Adaptive Robust Control of Linear Motor Drive System

2002 ◽  
Author(s):  
J. Q. Gong ◽  
Bin Yao
Keyword(s):  
Neural Network ◽  
Robust Control ◽  
Tracking Error ◽  
Linear Motor ◽  
Adaptive Robust Control ◽  
Operating Conditions ◽  
Tracking Performance ◽  
Motor Drive ◽  
Output Tracking ◽  
On Line

In this paper, an indirect neural network adaptive robust control (INNARC) scheme is developed for the precision motion control of linear motor drive systems. The proposed INNARC achieves not only good output tracking performance but also excellent identifications of unknown nonlinear forces in system for secondary purposes such as prognostics and machine health monitoring. Such dual objectives are accomplished through the complete separation of unknown nonlinearity estimation via neural networks and the design of baseline adaptive robust control (ARC) law for output tracking performance. Specifically, recurrent neural network (NN) structure with NN weights tuned on-line is employed to approximate various unknown nonlinear forces of the system having unknown forms to adapt to various operating conditions. The design is actual system dynamics based, which makes the resulting on-line weight tuning law much more robust and accurate than those in the tracking error dynamics based direct NNARC designs in implementation. With a controlled learning process achieved through projection type weights adaptation laws, certain robust control terms are constructed to attenuate the effect of possibly large transient modelling error for a theoretically guaranteed robust output tracking performance in general. Experimental results are obtained to verify the effectiveness of the proposed INNARC strategy. For example, for a typical point-to-point movement, with a measurement resolution level of ±1μm, the output tracking error during the entire execution period is within ±5μm and mainly stays within ±2μm showing excellent output tracking performance. At the same time, the outputs of NNs approximate the unknown forces very well allowing the estimates to be used for secondary purposes such as prognostics.

scholarly journals Nonlinear Adaptive Robust Control of the Electro-Hydraulic Servo System

2020 ◽  
Vol 10 (13) ◽  
pp. 4494 ◽  
Author(s):  
Lijun Feng ◽  
Hao Yan
Keyword(s):  
Robust Control ◽  
Position Control ◽  
External Disturbance ◽  
Servo System ◽  
Adaptive Robust Control ◽  
Superior Performance ◽  
Parametric Uncertainties ◽  
State Equations ◽  
Hydraulic Servo ◽  
Hydraulic Servo System

This paper focuses on high performance adaptive robust position control of electro-hydraulic servo system. The main feature of the paper is the combination of adaptive robust algorithm with discrete disturbance estimation to cope with the parametric uncertainties, uncertain nonlinearities, and external disturbance in the hydraulic servo system. First of all, a mathematical model of the single-rod position control system is developed and a nonlinear adaptive robust controller is proposed using the backstepping design technique. Adaptive robust control is used to encompass the parametric uncertainties and uncertain nonlinearities. Subsequently, a discrete disturbance estimator is employed to compensate for the effect of strong external disturbance. Furthermore, a special Lyapunov function is formulated to handle unknown nonlinear parameters in the system state equations. Simulations are carried out, and the results validate the superior performance and robustness of the proposed method.

Parameter estimation and upper bounding adaptation in adaptive-robust control approaches for trajectory control of robots

Robotica ◽  
2002 ◽  
Vol 20 (6) ◽  
pp. 653-660 ◽  
Author(s):  
Ibrahim Uzmay ◽  
Recep Burkan
Keyword(s):  
Parameter Estimation ◽  
Adaptive Control ◽  
Robust Control ◽  
Uncertain System ◽  
Adaptive Controller ◽  
Robust Adaptive Control ◽  
Adaptive Robust Control ◽  
Lyapunov Theory ◽  
Control Law ◽  
Control Input

In this paper a new robust adaptive control law for n-link robot manipulators with parametic uncertainties is derived using the Lyapunov theory thus guaranteed the stability of an uncertain system. The novelty of the adaptive robust control algorithm is that manipulator parameters and adaptive upper bounding functions are estimated to control the system properly, and the adaptive robust control law is also updated as an exponential function of manipulator kinematics, inertia parameters and tracking errors. The proposed adaptive control input includes a parameter estimation law as an adaptive controller and an additional control input vector as a robust controller. The developed approach has the advantages of both adaptive and robust control laws, without their discolour tags.

Adaptive Robust Control Strategy for Coupled Parallel-Kinematics Piezo-Flexural Micro and Nano-Positioning Stages

2007 ◽  
Author(s):  
Saeid Bashash ◽  
Nader Jalili
Keyword(s):  
Robust Control ◽  
Sliding Mode Control ◽  
Tracking Control ◽  
Sliding Mode ◽  
Variable Structure ◽  
Adaptive Robust Control ◽  
Switching Function ◽  
Parametric Uncertainties ◽  
Parallel Kinematics ◽  
Axis Parallel

Multiple-axis parallel kinematics piezo-flexural stages, used in variety of scanning probe microscopy and manipulation applications, suffer not only from the hysteresis nonlinearity and the effects of system dynamics, but also from the nonlinear coupling of one axis action on the motion of the other axes. Motivated by these shortfalls, a Lyapunov-based adaptive robust control methodology is proposed for the simultaneous tracking control of multiple-axis parallel kinematics piezo-flexural micro/nano-positioning systems in the presence of parametric uncertainties and unknown or partially known hysteresis and coupling nonlinearities. For this, a double-axis parallel-kinematics piezo-flexural stage with sub-nanometer resolution capacitive position sensors is considered for the system analysis and controller verification. The hysteresis response and the coupling effect of the stage are studied through a number of experiments, followed by a mathematical formulation for the system nonlinear equation of motion. Adopting the variable structure (sliding mode) control method with a parameter adaptation strategy, an adaptive robust controller is then derived with its asymptotic stability guaranteed through the Lyapunov stability criterion. To avoid the chatter phenomenon, which is a common problem in the sliding mode control, the hard switching function of the controller is replaced by a soft switching function. Through theoretical analysis, it is demonstrated that a parallelogram-type zone of attraction can be explicitly formed for the proposed soft variable structure controller, to which the error phase trajectory converges. Therefore, a uniformly ultimately bounded closed-loop system response is achieved. The proposed controller is eventually implemented on the piezo-flexural stage, demonstrating the effective double-axis tracking control of the stage, even in the absence of a representative hysteresis model and despite parametric uncertainties. Results indicate good agreement between experiments and theoretical developments.

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