Adaptive coupling control for overhead crane systems

Due to the requirements of high positioning accuracy, small swing angle, short transportation time, and high safety, both motion and stabilization control for an overhead crane system becomes an interesting issue in the field of control technology development. Since the overhead crane system is subject to underactuation with respect to the load sway dynamics, it is very hard to manipulate the crane system in a desired manner, namely, gantry position tracking and sway angle stabilization. Hence, in this paper, a nonlinear control scheme incorporating parameter adaptive mechanism is devised to ensure the overall closed-loop system stability. By applying the designed controller, the position error will be driven to zero while the sway angle is rapidly damped to achieve swing stabilization. Stability proof of the overall system is given in terms of Lyapunov concept. To demonstrate the effectiveness of the proposed controller, results for both computer simulation and experiments are also shown.     

Enhancing Design of Visual-Servo Delayed System

A robust adaptive predictor is proposed to solve the time-varying and delay control problem of an overhead crane system with a stereo-vision servo. The predictor is based on the use of a recurrent neural network (RNN) with tapped delays, and is used to supply the real-time signal of the swing angle. There are two types of discrete-time controllers under investigation, i.e., the proportional-integral-derivative (PID) controller and the sliding controller. Firstly, a design principle of the neural predictor is developed to guarantee the convergence of its swing angle estimation. Then, an improved version of the particle swarm optimization algorithm, the parallel particle swarm optimization (PPSO) method is used to optimize the control parameters of these two types of controllers. Finally, a homemade overhead crane system equipped with the Kinect sensor for the visual servo is used to verify the proposed scheme. Experimental results demonstrate the effectiveness of the approach, which also show the parameter convergence in the predictor.     

Anti-swing control of a hydraulic loader crane with a hanging load

In this paper, anti-swing control for a hydraulic loader crane is presented. The difference between hydraulic and electric cranes are discussed to show the challenges associated with hydraulic actuation. The hanging load dynamics and relevant kinematics of the crane are derived to create the 2-DOF anti-swing controller. The anti-swing controller is added to the electro-hydraulic motion controller via feedforward. A dynamic simulation model of the crane is made, and the control system is evaluated in simulations with a path controller in actuator space. Simulation results show significant reduction in the load swing angle during motion. Experiments are carried out to verify the performance of the anti-swing controller, showing good suppression of the payload angle in practice.     

Intelligent feedforward control and payload estimation for a two-link robotic manipulator

Conventional model-based computed torque control fails to produce a good trajectory tracking performance in the presence of payload uncertainty and modeling error. The challenge is to provide accurate dynamics information to the controller. A new control architecture that incorporates a neural-network, fuzzy logic and a simple proportional-derivative (PD) controller is proposed to control an articulated robot carrying a variable payload. An off-line trained feedforward (multilayer) neural network takes payload mass estimates from a fuzzy-logic mass estimator as one of the inputs to represent the inverse dynamics of the articulated robot. The effectiveness of the proposed architecture is demonstrated by experiment on a two-link planar manipulator with changing payload mass. Experimental results show that this control architecture achieves excellent tracking performance in the presence of payload uncertainty.     

Adaptive Fuzzy Controller of the Overhead Cranes With Nonlinear Disturbance

Overhead cranes are common industrial structures that are used in many factories and harbors. They are usually operated manually or by some conventional control methods, such as the optimal and PLC-based methods. The theme of this paper is to provide an effective all-purpose adaptive fuzzy controller for the crane. This proposed method does not need the complex dynamic model of the crane system, but it uses trolley position and swing angle information instead to design the fuzzy controller. An adaptive algorithm is provided to tune the free parameters in the crane control system. The ways to speed the transportation and reduce the computational efforts are also given. Therefore, the designing procedure of the proposed controller will be very easy. External disturbance, such as the wind and the hit, which always deteriorates the control performance, is also discussed in this paper to verify the robustness of the proposed adaptive fuzzy algorithm. At last, several experimental results with different wire length and payload weight compare the feasibility and effectiveness of the proposed scheme with conventional methods     

A two-layered fuzzy logic controller for systems with deadzones

Existing fuzzy control methods do not perform well when applied to systems containing nonlinearities arising from unknown deadzones. In particular, we show that a usual "fuzzy PD" controller applied to a system with a deadzone suffers from poor transient performance and a large steady-state error. In this paper, we propose a novel two-layered fuzzy logic controller for controlling systems with deadzones. The two-layered control structure consists of a fuzzy logic-based precompensator followed by a usual fuzzy PD controller. Our proposed controller exhibits superior transient and steady-state performance compared to usual fuzzy PD controllers. In addition, the controller is robust to variations in deadzone nonlinearities. We illustrate the effectiveness of our scheme using computer simulation examples.<>     

Manipulating rigid payloads with multiple robots using compliantgrippers

This paper describes an approach to nonmodel-based decentralized controls of multirobot systems utilizing structural flexibility in gripper design to avoid large unwanted internal forces acting on multirobot systems. It is proven in theory that a simple proportional and derivative (PD) position feedback plus gravity compensation controller can regulate the desired position/orientation of a payload manipulated by multiple robots with compliant grippers and simultaneously damp vibrations of compliant grippers. By adding a force feedforward control to the PD scheme, a hybrid position/force control scheme is further developed to control internal forces between robots and the payload in the particular directions, in the event that the compliance of grippers is low or negligible in these directions. Experiments conducted with two CRS A460 industrial robots manipulating a beam, using a rigid and a compliant gripper, confirm these theoretical predictions     

A learning scheme for low-speed precision tracking control of hybrid stepping motors

Servo control of the hybrid stepping motor is complicated due to its highly nonlinear torque-current-position characteristics, especially under low operating speeds. This paper presents a simple and efficient control algorithm for the high-precision tracking control of hybrid stepping motors. The principles of learning control have been exploited to minimize the motor's torque ripple, which is periodic and nonlinear in the system states, with specific emphasis on low-speed situations. The proposed algorithm utilizes a fixed proportional-derivative (PD) feedback controller to stabilize the transient dynamics of the servomotor and the feedforward learning controller to compensate for the effect of the torque ripple and other disturbances for improved tracking accuracy. The stability and convergence performance of the learning control scheme is presented. It has been found that all error signals in the learning control system are bounded and the motion trajectory converges to the desired value asymptotically. The experimental results demonstrated the effectiveness and performance of the proposed algorithm.     

Nichtlineare Trajektorienfolgeregelung für einen Laborhelikopter

This contribution is devoted to the nonlinear tracking control problem of the laboratory experiment helicopter 3DOF distributed by Quanser. The laboratory experiment belongs to the class of mechanical systems with three degrees-of-freedom and two control inputs. It is well known that the systematic design of nonlinear controllers for underactuated mechanical systems is a challenge compared to fully actuated systems. On certain simplifying assumptions, which very well apply to the operating range of practical interest, we can show that the mathematical model is configuration flat. Thereby, a mechanical system is said to be configuration flat if it is differential flat and the flat outputs solely depend on the generalized coordinates of the mechanical system. The controller design is based on a formulation of the mechanical system on a Riemannian manifold where the kinetic energy serves as a natural Riemannian metric. In a first step a nonlinear tracking controller including an integral part in the linear error system is designed by means of a quasi-static state feedback. In a second step the design of the tracking controller is based on the theory of exact linearization utilizing the so-called dynamic extension algorithm. The experimental results of both controllers are compared and discussed in detail. In particular, the quasi-static state feedback controller shows an excellent tracking behavior. The performance as being obtained by the nonlinear controlled cannot be achieved by conventional linear control strategies.     

Nonlinear feedback control of a class of nonlinear systems

A nonlinear feedback controller is designed on the basis of a truncated Volterra series representation of the process model, the necessary parameters of which can be obtained via suitable experiments. A simple example demonstrates the increased range and improved performance of the nonlinear controller compared to two linear controllers.     

Design and Real-Time Implementation of a nonlinear regulation controller for the RMP-100 Segway TWIP

The RMP-100 is an underactuated robot known in the literature as two wheeled inverted pendulum (TWIP). This mechanism has two independent wheels that allow to perform two tasks simultaneously: keep the inverted pendulum in its upright position (balancing) and move the robot to a specific location on the workspace terrain. This paper proposes (as an extension of the work of Gutiérrez Frías, 2013) a nonlinear Lyapunov-based controller with the purpose to stabilize the pose (position and orientation) of the robot while balancing the pendulum. Moreover, asymptotic stability is proven using LaSalle’s invariance principle. Furthermore, the development of a new software for implementing real-time controllers on the RMP-100 is briefly explained; and finally, in order to validate the performance of the proposed controller, experimental results are included.     

An energy exchanging and dropping-based model-free output feedback crane control method

For the overhead crane control problem, velocity-related terms (corresponding to full-state feedback) are generally required in the designed control systems for damping injection to achieve (asymptotic) stability. However, it is known that velocity signals may be noisy or even unmeasurable. Also, most existing controllers require full or partial plant physical parameters like rope length or load mass. To resolve these issues, a model-free energy exchanging and dropping-based control law is proposed to achieve output (only position/swing-angle) feedback control for overhead cranes. We synthesize a total energy function, consisting of the (generalized) crane energy and the controller energy, to render it to achieve its (local) minimum at the desired equilibrium point. The proposed control law is dynamically generated by an artificial block-spring system, which exchanges energy with the crane dynamics and then drops the energy via an elegant dropping mechanism to gradually attenuate the total energy. The corresponding stability and convergence analysis is implemented using some Lyapunov-like analysis. Simulation and experimental results suggest the effectiveness and feasibility of the proposed method for crane control, in terms of rapid swing suppression, efficient trolley positioning, as well as increased robustness.     

How to reduce the impedance for pHRI: Admittance control or underactuation?

Many applications of physical human–robot interaction (pHRI) seek to minimize the impedance felt by the operator. For large-scale operations, such as industrial material handling, two architectures have been proposed. The first uses a rigid robotic manipulator with force sensing and an admittance controller, such as the class of Intelligent Assist Devices (IADs). The second utilizes an underactuated macro-mini manipulator system, such as the uMan Assist Device. Given an application with a large payload, which of these two systems can offer the lowest inertia interaction? This work analyzes the effective inertia experienced by the operator in each of the systems. It compares the conditions under which each provides the lightest manipulation, conditions that depend on the payload as well as the frequency of manipulation. The results are validated on a large overhead robotic system configured alternatively in each of the configurations.     

Data-based error compensation for georeferenced payload path tracking of automated tower cranes

In order to increase the productivity on construction sites, a current topic of research is the automation of the payload transport by tower cranes. Thereby, a key requirement is to ensure that the tower crane precisely tracks the planned paths and positions the payload at the specified target location. Most of the state-of-the-art tower crane controllers damp load sway while moving each driving system to its desired position. However, the path error also consists of bending displacements of the tower crane’s mechanical structure, observer errors, or sensor offsets once the crane hook position is considered in a fixed georeferenced construction site system. These errors have not been addressed in literature on tower cranes so far. This paper introduces an approach to reduce the path error of automated tower cranes without permanently integrating additional sensors. A regression model is derived for predicting the path error and a least absolute shrinkage and selection operator (LASSO) is used to select the most important features. The predicted error is then used to compute a compensating hook path such that the measured hook path matches the desired hook path. The effectiveness of the approach is experimentally validated utilizing a real large-scale tower crane showing a reduction of the path error of more than 50% and a position accuracy of less than 16 cm.     

An output-based controller with two operational modes for a robotwith uncertain parameters in set-point regulation tasks

This paper presents a control approach for the set-point regulation task of a rigid robot with uncertain parameters. The controller strategy is based on two operational modes. During the first mode, the controller drives the robot toward a small neighborhood of the equilibrium point, while in the second mode, the robot converges exponentially to the final target. The proposed control scheme is associated with simple linear controllers, it applies position measurements only, and accounts for the system uncertainties and the unknown payload. Friction is included in the model. Simulation and experimental results are demonstrated     

Adaptive control techniques forfrictioncompensation

In this paper, we design two adaptive controllers for a second-ordermechanicalsystem which incorporates frictional effects such as Coulomb, static, Stribeck, andviscousfriction. First, we design a modular position tracking controller that can accommodate avarietyof adaptive update laws. The proposed controller is shown to compensate foruncertaintyassociated with the friction parameters which appear linearly in the model. In thesecond controlscheme, we show how a Lyapunov-based adaptive position setpoint controller canbe designed tocompensate for parametric uncertainty throughout the mechanical systemincluding the Stribeckeffect related constant which does not appear linearly in the model.Experimental results areprovided to illustrate the performance of the proposed controllers.     

锂电池组储能的混合动力RTG系统设计

锂电池组与柴油机构成的混合动力起重机系统是港口节能减排的一项重要技术。针对起重机再生制动能量的回收,设计了双向DC-DC变换器来实现锂电池组储能系统的两种工作模式(再生制动模式和锂电池组放电模式);对双向DC-DC变流器升压工作方式设计了双闭环控制器,降压工作方式设计了电流环和电压环两种控制器,并进行了对比,从而实现了对锂电池组储能系统充、放电过程和不同运行模式间切换过程的控制。运用PLECS搭建了系统仿真模型,仿真结果表明,在制动能量回收过程中,采用电压环控制器可以实现较高效率的制动能量回收。     

A new actuation mechanism for an Interfacial Force Microscope: Design,implementation, and feedback control

In this paper we present development of a sensing and actuation mechanism along with its controller for an Interfacial Force Microscope. The device can be used as a micro-scale force sensor or actuator using a feedback control scheme that regulates the interaction force between the probe tip and the sample. The mechanism is essentially a force balancing system consisting of a torsion bar and two variable gap parallel plate capacitors with a sharp probe attached to the moving plate of one of the capacitors. The capacitors are utilized to measure the air gap distance and act as electrostatic actuators to compensate the force applied to the sensor tip. Given that the system is nonlinear with respect to the control input, a nonlinear feedback linearization scheme coupled with a dynamic output feedback controller is developed and integrated with available measurement and computing hardware. The dynamic controller requires measurement of position information only and provides improved performance when compared to conventional controllers. Experimental studies are conducted for nanoindentation and imaging applications and performance of the controller is evaluated.     

Implementation of a neural network tracking controller for a singleflexible link: comparison with PD and PID controllers

The objective of this paper is to show the results of the practical implementation of a neural network (NN) tracking controller on a single flexible link and compare its performance to that of proportional derivative (PD) and proportional integral derivative (PID) standard controllers. The NN controller is composed of an outer PD tracking loop, a singular perturbation inner loop for stabilization of the fast flexible-mode dynamics, and an NN inner loop used to feedback linearize the slow pointing dynamics. No off-line training or learning is needed for the NN. It is shown that the tracking performance of the NN controller is far better than that of the PD or PID standard controllers. An extra friction term was added in the tests to demonstrate the ability of the NN to learn unmodeled nonlinear dynamics