1,361 research outputs found
Advanced Discrete-Time Control Methods for Industrial Applications
This thesis focuses on developing advanced control methods for two industrial
systems in discrete-time aiming to enhance their performance in delivering the
control objectives as well as considering the practical aspects. The first part
addresses wind power dispatch into the electricity network using a battery
energy storage system (BESS). To manage the amount of energy sold to the
electricity market, a novel control scheme is developed based on discrete-time
model predictive control (MPC) to ensure the optimal operation of the BESS in
the presence of practical constraints. The control scheme follows a decision
policy to sell more energy at peak demand times and store it at off-peaks in
compliance with the Australian National Electricity Market rules. The
performance of the control system is assessed under different scenarios using
actual wind farm and electricity price data in simulation environment. The
second part considers the control of overhead crane systems for automatic
operation. To achieve high-speed load transportation with high-precision and
minimum load swings, a new modeling approach is developed based on independent
joint control strategy which considers actuators as the main plant. The
nonlinearities of overhead crane dynamics are treated as disturbances acting on
each actuator. The resulting model enables us to estimate the unknown
parameters of the system including coulomb friction constants. A novel load
swing control is also designed based on passivity-based control to suppress
load swings. Two discrete-time controllers are then developed based on MPC and
state feedback control to track reference trajectories along with a feedforward
control to compensate for disturbances using computed torque control and a
novel disturbance observer. The practical results on an experimental overhead
crane setup demonstrate the high performance of the designed control systems.Comment: PhD Thesis, 230 page
Comparison of Linear and Nonlinear MPC on Operator-In-the-Loop Overhead Cranes
Model Predictive Control has been proved to enhance the control performance of overhead cranes. However, in Operator-In-the-Loop (OIL) overhead cranes the trajectory of the payload strongly depends on the runtime decisions of the user and can not be predicted beforehand. Simple assumptions on the future references evolution have therefore to be made. In this paper we investigate the applicability of linear and nonlinear MPC strategies to the case of OIL overhead cranes, based on different assumptions on the future evolution of the length of the hoisting cable
Operator vision aids for space teleoperation assembly and servicing
This paper investigates concepts for visual operator aids required for effective telerobotic control. Operator visual aids, as defined here, mean any operational enhancement that improves man-machine control through the visual system. These concepts were derived as part of a study of vision issues for space teleoperation. Extensive literature on teleoperation, robotics, and human factors was surveyed to definitively specify appropriate requirements. This paper presents these visual aids in three general categories of camera/lighting functions, display enhancements, and operator cues. In the area of camera/lighting functions concepts are discussed for: (1) automatic end effector or task tracking; (2) novel camera designs; (3) computer-generated virtual camera views; (4) computer assisted camera/lighting placement; and (5) voice control. In the technology area of display aids, concepts are presented for: (1) zone displays, such as imminent collision or indexing limits; (2) predictive displays for temporal and spatial location; (3) stimulus-response reconciliation displays; (4) graphical display of depth cues such as 2-D symbolic depth, virtual views, and perspective depth; and (5) view enhancements through image processing and symbolic representations. Finally, operator visual cues (e.g., targets) that help identify size, distance, shape, orientation and location are discussed
Vision-based control of a knuckle boom crane with online cable length estimation
A vision-based controller for a knuckle boom crane is presented. The
controller is used to control the motion of the crane tip and at the same time
compensate for payload oscillations. The oscillations of the payload are
measured with three cameras that are fixed to the crane king and are used to
track two spherical markers fixed to the payload cable. Based on color and size
information, each camera identifies the image points corresponding to the
markers. The payload angles are then determined using linear triangulation of
the image points. An extended Kalman filter is used for estimation of payload
angles and angular velocity. The length of the payload cable is also estimated
using a least squares technique with projection. The crane is controlled by a
linear cascade controller where the inner control loop is designed to damp out
the pendulum oscillation, and the crane tip is controlled by the outer loop.
The control variable of the controller is the commanded crane tip acceleration,
which is converted to a velocity command using a velocity loop. The performance
of the control system is studied experimentally using a scaled laboratory
version of a knuckle boom crane
Feedback Synthesis for Controllable Underactuated Systems using Sequential Second Order Actions
This paper derives nonlinear feedback control synthesis for general control
affine systems using second-order actions---the needle variations of optimal
control---as the basis for choosing each control response to the current state.
A second result of the paper is that the method provably exploits the nonlinear
controllability of a system by virtue of an explicit dependence of the
second-order needle variation on the Lie bracket between vector fields. As a
result, each control decision necessarily decreases the objective when the
system is nonlinearly controllable using first-order Lie brackets. Simulation
results using a differential drive cart, an underactuated kinematic vehicle in
three dimensions, and an underactuated dynamic model of an underwater vehicle
demonstrate that the method finds control solutions when the first-order
analysis is singular. Moreover, the simulated examples demonstrate superior
convergence when compared to synthesis based on first-order needle variations.
Lastly, the underactuated dynamic underwater vehicle model demonstrates the
convergence even in the presence of a velocity field.Comment: 9 page
Model Predictive Control for operator-in-the-loop overhead cranes
In this paper, a Model Predictive Control approach for the velocity control of operator-in-the loop overhead cranes is proposed. The operator can select the maximum position overshoot as a tuning parameter for the method. Simulations provide a comparison between the proposed method and the well known Zero Vibration input shaping technique, showing its effectiveness in controlling the payload oscillations
MPC-PID control of operator-in-the-loop overhead cranes: A practical approach
In this paper, a velocity control system for industrial overhead cranes based on a Model Predictive Control approach is proposed. The problem of the control of the operator-in-the-loop system is addressed, as the operator drives the system pushing a button while the control algorithm drives the cart reducing the oscillations of the load. An inner velocity control loop is used in order to overcome some of the problems of controlling the system by using directly the torque of the motor as a control variable. Simulations show the effectiveness of the approach, in particular in the presence of friction
Nonlinear optimal control for the 4-DOF underactuated robotic tower crane
Tower cranes find wide use in construction works, in ports and in several loading and unloading procedures met in industry. A nonlinear optimal control approach is proposed for the dynamic model of the 4-DOF underactuated tower crane. The dynamic model of the robotic crane undergoes approximate linearization around a temporary operating point that is recomputed at each time-step of the control method. The linearization relies on Taylor series expansion and on the associated Jacobian matrices. For the linearized state-space model of the system a stabilizing optimal (H-infinity) feedback controller is designed. To compute the controller’s feedback gains an algebraic Riccati equation is repetitively solved at each iteration of the control algorithm. The stability properties of the control method are proven through Lyapunov analysis. The proposed control approach is advantageous because: (i) unlike the popular computed torque method for robotic manipulators, the new control approach is characterized by optimality and is also applicable when the number of control inputs is not equal to the robot’s number of DOFs, (ii) it achieves fast and accurate tracking of reference setpoints under minimal energy consumption by the robot’s actuators, (iii) unlike the popular Nonlinear Model Predictive Control method, the article’s nonlinear optimal control scheme is of proven global stability and convergence to the optimum.This research work has been partially supported by Grant Ref. “CSP contract 040322”—“Nonlinear control, estimation and fault diagnosis for electric power generation and electric traction/propulsion systems” of the Unit of Industrial Automation of the Industrial Systems Institute
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