Adaptive Motion Control Research Articles

Organization of adaptive automatic control systems of wheeled and tracked vehicles

The article considers the problem and principles of construction of on-board information-management systems of transport vehicles. The authors stated issues of organization of adaptive control systems, diagnostics and motion control of wheeled and tracked vehicles.

模糊神经网络在自适应双轴运动控制系统中的应用

模糊神经网络在自适应双轴运动控制系统中的应用

Adaptive motion control law of a robotic wheelchair

In the present work, a dynamic model of a robotic wheelchair is developed considering a lateral deviation of the center of mass. The Lyapunov and input/output stability theories are used to design a novel tracking and positioning adaptive control for the robotic wheelchair. Properties of the dynamic model with respect to its matrices and parameters are shown. A filter is used to obtain a closed loop equation that allows designing the adaptive control law. Then, a projection algorithm is used to improve the adaptive control in the sense of avoiding parameter drift. Experimental results show good performance of the adaptive control.

Intelligent Adaptive Motion Controller Design for Mecanum Wheeled Omnidirectional Robots with Parameter Variations

Intelligent Adaptive Motion Controller Design for Mecanum Wheeled Omnidirectional Robots with Parameter Variations

A neural framework for adaptive robot control

This paper investigates how dynamics in recurrent neural networks can be used to solve some specific mobile robot problems such as motion control and behavior generation. We have designed an adaptive motion control approach based on a novel recurrent neural network, called Echo state networks. The advantage is that no knowledge about the dynamic model is required, and no synaptic weight changing is needed in presence of time varying parameters in the robot. To generate the robot behavior over time, we adopted a biologically inspired approach called neural fields. Due to its dynamical properties, a neural field produces only one localized peak that indicates the optimum movement direction, which navigates a mobile robot to its goal in an unknown environment without any collisions with static or moving obstacles.

Path Planning and Control for Multiple AGVs Based on Improved Two-Stage Traffic Scheduling

An improved two-stage traffic scheduling algorithm for path planning and conflict avoidance of multiple AGVs (Automated Guided Vehicle) is combined with an adaptive motion control algorithm for path following of a single AGV in this paper, in order to implement an integrated planning and control system. A genetic algorithm (GA) is used for feasible path planning both offline and online. Multiple objectives and constraints are added to the online GA when some digital map routes cannot be used due to unavoidable conflict. The conflict-free policy we propose changes the speed or route of the AGV with a lower priority to make the conflict settled. Adaptive motion control enables individual AGV to follow the planned paths at any given speed. The planned paths and given speed are the information links connecting traffic scheduling and motion control. Numerical simulation confirms the effectiveness of our traffic scheduling algorithm and the adaptability of the motion control algorithm.

Adaptive motion control of sensors of flexible manufacturing systems for nondestructive testing

A method for adaptive motion control of elements of flexible manufacturing systems for nondestructive testing is proposed. Their control principle is based on correction of motion parameters in accordance with current testing results. A modified evaluating-function method is used to interpolate the motion paths.

The Operational Space Formulation with Neural-Network Adaptive Motion Control

An operational space controller that employs three-layer neural network (NN) adaptive motion control is presented in this paper. It is shown that the trajectory tracking errors and the NN weight errors are bounded and consequently the controller is shown to be stable. Comparative study is made between the performance of the proposed NN adaptive motion control strategy and the conventional inverse-dynamics control and PD plus gravity compensation controller. The simulation results show that the NN adaptive motion control can be as effective as the inverse-dynamics without the need for a priori knowledge of the system dynamics. Real-time implementation of the strategy was also conducted on a real PUMA 560 robot.

Adaptive Visual Servoing of Robot Manipulators without Measuring the Image Velocity

The direct adaptive control of planar robot manipulators through visual servoing is considered. A solution is developed for image-based visual systems to allow tracking of a desired trajectory, when both camera calibration and robot dynamics are uncertain. In order to solve the MIMO parameter adaptive problem without using image velocity information, the adaptive camera calibration is formulated as a relative degree two MIMO adaptive control problem. A recently proposed Lyapunov/passivity-based adaptive control for relative degree two MIMO system based on SDU factorization is applied. The resulting adaptive calibration is then combined with an adaptive motion controller for the manipulator, which takes into account its uncertain nonlinear dynamics. The overall stability of the adaptive visual servoing system can be proved thanks to the explicit Lyapunov-like functions of both adaptation schemes.

Motion Control of Passive Intelligent Walker Using Servo Brakes

We propose a new intelligent walker based on passive robotics that assists the elderly, handicapped people, and the blind who have difficulty in walking. We developed a prototype of the robot technology walker (RT walker), a passive intelligent walker that uses servo brakes. The RT walker consists of a support frame, two casters, two wheels equipped with servo brakes, and it has passive dynamics that change with respect to applied force/moment. This system is intrinsically safe for humans, as it cannot move unintentionally, i.e., it has no driving actuators. In addition, the RT walker provides a number of navigational features, including good maneuverability, by appropriately controlling the torque of servo brakes based on RT. We propose a human adaptive motion control algorithm that changes the apparent dynamics to adapt to user difficulties, and an environmentally adaptive motion control algorithm, which incorporates environmental information to provide obstacle/step avoidance and gravity compensation functions. The proposed control algorithms are experimentally applied to the RT walker to test their validity.

Fuzzy adaptive EKF motion control for non-holonomic and underactuated cars with parametric and non-parametric uncertainties

A new fuzzy adaptive motion control system including on-line extended Kalman's filter (EKF) for wheeled underactuated cars with non-holonomic constraints on the motion is presented. The presence of parametric uncertainties in the kinematics and in the dynamics is treated using suitable differential adaptation laws. We merge adaptive control with fuzzy inference system. By using fuzzy system, the parameters of the kinematical controller are functions of the lateral, longitudinal and orientation errors of the motion. In this way we have a robust control system where the dynamics of the motion errors is with lower time response than the adaptive control without fuzzy. Also Lyapunov's stability of the motion errors is proved based on the properties of the fuzzy maps. If data from incremental encoders are employed for the feedback directly, sensor noises can damage the performance of the motion control in terms of the motion errors and of the parametric adaptation. These noises are aleatory and denote a kind of non-parametric uncertainties which perturb the nominal model of the car. Therefore an EKF is inserted in the adaptive control system to compensate for the above non-parametric uncertainties. The control algorithm efficiency is confirmed through simulation tests in Matlab environment.

Robust adaptive motion control of permanent magnet linear motors based on disturbance compensation

Unlike rotational motors, permanent magnetic linear motors (PMLMs) are more sensitive to various force disturbances because of the reduction of gears. The parameters of PMLMs are difficult to accurately measure and vary in experiments and applications. To efficiently control a PMLM and alleviate the influence of parameter variation, robust adaptive control method is proposed to be used in the motion control of PMLMs. The force ripple is the main force disturbance when the velocity is close to zero, but it is complex and difficult to be described by an accurate model. So, in the designed robust adaptive controller, an off-line backpropagation neural network is proposed to approximate the function of the force ripple. Simulation and experimental results show that the designed robust adaptive controller can obtain higher control precision and be robust to the parameter variation.

Smart Rehabilitation Devices: Part II - Adaptive Motion Control.

This article presents a study of adaptive motion control of smart versatile rehabilitation devices using MR fluids. The device provides both isometric and isokinetic strength training and is reconfigurable for several human joints. Adaptive controls are developed to regulate resistance force based on the prescription of the therapist. Special consideration has been given to the human-machine interaction in the adaptive control that can modify the behavior of the device to account for strength gains or muscle fatigue of the human subject.

지면의 미끄러운 정도에 따른 캐릭터의 걷기 동작 생성

3차원 가상현실 내에서 캐릭터 움직임 동작의 기술은 기존 방식인 키프레임 기법에 의존하던 것이 점차 동작 제어 기법을 활용하고, 보다 사실적이고 자연스러운 움직임을 생성해 내고자 하는 방향으로 발전해 가고 있다. 그러나 이러한 동작 제어 기법을 통해 가상현실의 지형 성질에 따라 적응적인 캐릭터의 동작을 표현하는데 한계가 있다. 즉, 대부분의 가상환경에서 캐릭터의 걷는 움직임은 일정하고 단조로운 동작만을 반복하여 표현하고 있어 관찰자로 하여금 지루함을 느끼게 하고, 지형의 조건이나 형태에 맞지않게 캐릭터의 발끝이 지면에 스며들거나 떠있는 등의 부자연스러운 동작으로 인해 사실감을 저하시킨다. 본 논문에서는 적은 매개변수들과 역운동학 방법을 적용하여 기본 걷기 동작을 표현하고, 지면의 성질을 마찰계수로 대표시켜 지면에 적응적인 걷기 동작의 생성 방법을 제안한다. 제안된 방법에서는 구심력과 마찰계수를 결합한 후, 이를 근거로하여 한 주기 동안의 걷는 동작을 분석하고 이를 기반으로 동작에 필요한 매개변수를 조정하여 지질에 따른 적응적인 캐릭터의 걷기 동작을 생성한다. In 3D virtual environment the description of character' s movement that has utilized the conventional key-frame technique is gradually being developed toward the application of motion control method to generate more realistic and natural motion. Even the motion control method, however, has the limitation for expression of character's motion adapted to the ground properties of virtual world. That is, the walking motions of character are not only, for the most part, so uniform simple and repeated often as to feel to be tedious, but also the unnatural motion in which the tips of the toes soak through a plane or float in the air discording with the conditions of terrain lowers the semblance of reality. This paper proposes an adaptive motion control method for human figure locomotion in virtual environments or games, in which the walking motion is dynamiccally adapted to the ground's sliding properties. We compute an optimal parameters for one cycle of walking motion adapted to the ground properties by combining the coefficient of friction and centripetal force, and therefrom we induce a set of nonskid speed corresponding to various sliding properties of the ground.

A shape adaptive motion control system with application to robotic polishing

This paper presents a new shape adaptive motion control system that integrates part measurement with motion control. The proposed system consists of four blocks: surface measurement; surface reconstruction; tool trajectory planning; and axis motion control. The key technology used in surface measurement and surface reconstruction is the spatial spectral analysis. In the surface measurement block, a new spectral spectrum comparison method is proposed to determine an optimal digitizing frequency. In the surface reconstruction block, different interpolation methods are compared in the spatial spectral domain. A spatial spectral B-spline method is presented. In the tool trajectory planning block, a method is developed to select a motion profile first and then determine tool locations according to the reconstructed surface in order to improve the accuracy of the planned toolpath. Based on the proposed methods, a software package is developed and implemented on the polishing robot constructed at Ryerson University. The effectiveness of the proposed system has been demonstrated by the experiment on edge polishing. In this experiment, the shape of the part edges is measured first, and then constructed as a wire-frame CAD model, based on which the tool trajectory is planned to control the tool to polish the edges.

Virtual Remote Center of Motion control for needle placement robots

Objective: We present an algorithm that enables percutaneous needle-placement procedures to be performed with unencoded, unregistered, minimally calibrated robots while removing the constraint of placing the needle tip on a mechanically enforced Remote Center of Motion (RCM).Materials and Methods: The algorithm requires only online tracking of the surgical tool and a five-degree-of-freedom (5-DOF) robot comprising three prismatic DOF and two rotational DOF. An incremental adaptive motion control cycle guides the needle to the insertion point and also orients it to align with the target-entry-point line. The robot executes RCM motion without having a physically constrained fulcrum point.Results: The proof-of-concept prototype system achieved 0.78 mm translation accuracy and 1.4° rotational accuracy (this is within the tracker accuracy) within 17 iterative steps (0.5–1 s).Conclusion: This research enables robotic assistant systems for image-guided percutaneous procedures to be prototyped/constructed more quickly and less expensively than has been previously possible. Since the clinical utility of such systems is clear and has been demonstrated in the literature, our work may help promote widespread clinical adoption of this technology by lowering system cost and complexity.

Explicit force control for underwater vehicle-manipulator systems

In this paper two explicit force control schemes for underwater vehicle-manipulator systems are presented. The schemes take into account several factors such as uncertainty in the model knowledge, presence of hydrodynamic effects, kinematic redundancy of the system, and poor performance of vehicle's actuation system. The possible occurrence of loss of contact due to vehicle's movement during the task is also considered, and the adoption of an adaptive motion control scheme is investigated to take advantage of dynamic compensation. The proposed control schemes have extensively been tested in numerical simulation runs; the results obtained in a case study are reported to illustrate their performance.

Neural networks for advanced control of robot manipulators

Presents an approach and a systematic design methodology to adaptive motion control based on neural networks (NNs) for high-performance robot manipulators, for which stability conditions and performance evaluation are given. The neurocontroller includes a linear combination of a set of off-line trained NNs, and an update law of the linear combination coefficients to adjust robot dynamics and payload uncertain parameters. A procedure is presented to select the learning conditions for each NN in the bank. The proposed scheme, based on fixed NNs, is computationally more efficient than the case of using the learning capabilities of the neural network to be adapted, as that used in feedback architectures that need to propagate back control errors through the model to adjust the neurocontroller. A practical stability result for the neurocontrol system is given. That is, we prove that the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the NN bank and the design parameters of the controller. In addition, a robust adaptive controller to NN learning errors is proposed, using a sign or saturation switching function in the control law, which leads to global asymptotic stability and zero convergence of control errors. Simulation results showing the practical feasibility and performance of the proposed approach to robotics are given.

Adaptive motion control using neural network approximations

In this paper, we present a new adaptive technique for tracking control of mechanical systems in the presence of friction and periodic disturbances. Radial Basis Functions (RBFs) are used to compensate for the effects of nonlinearly occurring parameters in the friction and periodic disturbance model. Theoretical analysis, such as stability and transient performance, is provided. Furthermore, the performance of the adaptive RBF controller and its non-adaptive counterpart are compared.

Adaptive control of dc motor drives with converter non-linearities

Power electronic converters used in motor drives exhibit non-linear behaviour due to dead time and other circuit imperfections. Motors and their mechanical loads are often characterized by significant non-linearities as well. Since the parameters describing the system are not always accurately known, improved motion control performance may be attempted by using non-linear adaptive control methods. In this paper, thefocus is ona dc motor fed by a full-bridge dcdc converter. A linearizing adaptive motion controller is derived for the overall system, under the assumption that the parameters describing the converter, the motor and its load are all unknown. The controller guarantees global stability and asymptotic tracking.