8 research outputs found
Intelligent modelling and active vibration control of flexible manipulator system
Unwanted vibration of flexible manipulator results in unsatisfactory performance of any dynamic system using the flexile manipulator. This paper presents a robust control strategy in order to suppress undesirable vibration due to flexible manipulator maneuver. First, the appropriate model of the flexible manipulator is extracted by applying the control-model identification technique for linear and nonlinear model, namely, autoregressive with exogenous input (ARX) model and nonlinear ARX (NARX) respectively. The linear model is estimated by recursive least square method (RLS) and nonlinear model identified by artificial neural network (NN). Finally, the PID controller is designed for each proposed model to cancel the vibration of the flexible manipulator. The robustness of the controller is evaluated by imposing new disturbances into the linear and nonlinear systems. System identification and controller design is conducted by numerical and simulation approaches. The results from simulation indicate that performance of PID controller using linear model is satisfactory compared to nonlinear model
Online Deflection Compensation of a Flexible Hydraulic Loader Crane Using Neural Networks and Pressure Feedback
publishedVersio
Nonlinear control for Two-Link flexible manipulator
Recently the use of robot manipulators has been increasing in many applications such as medical applications, automobile, construction, manufacturing, military, space, etc. However, current rigid manipulators have high inertia and use actuators with large energy consumption. Moreover, rigid manipulators are slow and have low payload-to arm-mass ratios because link deformation is not allowed. The main advantages of flexible manipulators over rigid manipulators are light in weight, higher speed of operation, larger workspace, smaller actuator, lower energy consumption and lower cost. However, there is no adequate closed-form solutions exist for flexible manipulators. This is mainly because flexible dynamics are modeled with partial differential equations, which give rise to infinite dimensional dynamical systems that are, in general, not possible to represent exactly or efficiently on a computer which makes modeling a challenging task. In addition, if flexibility nature wasn\u27t considered, there will be calculation errors in the calculated torque requirement for the motors and in the calculated position of the end-effecter. As for the control task, it is considered as a complex task since flexible manipulators are non-minimum phase system, under-actuated system and Multi-Input/Multi-Output (MIMO) nonlinear system. This thesis focuses on the development of dynamic formulation model and three control techniques aiming to achieve accurate position control and improving dynamic stability for Two-Link Flexible Manipulators (TLFMs). LQR controller is designed based on the linearized model of the TLFM; however, it is applied on both linearized and nonlinear models. In addition to LQR, Backstepping and Sliding mode controllers are designed as nonlinear control approaches and applied on both the nonlinear model of the TLFM and the physical system. The three developed control techniques are tested through simulation based on the developed dynamic formulation model using MATLAB/SIMULINK. Stability and performance analysis were conducted and tuned to obtain the best results. Then, the performance and stability results obtained through simulation are compared. Finally, the developed control techniques were implemented and analyzed on the 2-DOF Serial Flexible Link Robot experimental system from Quanser and the results are illustrated and compared with that obtained through simulation
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
Development of New Adaptive Control Strategies for a Two-Link Flexible Manipulator
Manipulators with thin and light weight arms or links are called as Flexible-Link Manipulators (FLMs). FLMs offer several advantages over rigid-link manipulators such as achieving highspeed operation, lower energy consumption, and increase in payload carrying capacity and find applications where manipulators are to be operated in large workspace like assembly of freeflying space structures, hazardous material management from safer distance, detection of flaws
in large structure like airplane and submarines. However, designing a feedback control system for a flexible-link manipulator is challenging due the system being non-minimum phase, underactuated and non-collocated. Further difficulties are encountered when such manipulators handle
unknown payloads. Overall deflection of the flexible manipulator are governed by the different vibrating modes (excited at different frequencies) present along the length of the link. Due to change in payload, the flexible modes (at higher frequencies) are excited giving rise to
uncertainties in the dynamics of the FLM. To achieve effective tip trajectory tracking whilst quickly suppressing tip deflections when the FLM carries varying payloads adaptive control is necessary instead of fixed gain controller to cope up with the changing dynamics of the
manipulator. Considerable research has been directed in the past to design adaptive controllers based on either linear identified model of a FLM or error signal driven intelligent supervised learning e.g. neural network, fuzzy logic and hybrid neuro-fuzzy. However, the dynamics of the FLM being nonlinear there is a scope of exploiting nonlinear modeling approach to design adaptive controllers. The objective of the thesis is to design advanced adaptive control strategies
for a two-link flexible manipulator (TLFM) to control the tip trajectory tracking and its deflections while handling unknown payloads. To achieve tip trajectory control and simultaneously suppressing the tip deflection quickly
when subjected to unknown payloads, first a direct adaptive control (DAC) is proposed. The
proposed DAC uses a Lyapunov based nonlinear adaptive control scheme ensuring overall
system stability for the control of TLFM. For the developed control laws, the stability proof of
the closed-loop system is also presented. The design of this DAC involves choosing a control
law with tunable TLFM parameters, and then an adaptation law is developed using the closed
loop error dynamics. The performance of the developed controller is then compared with that of
a fuzzy learning based adaptive controller (FLAC). The FLAC consists of three major
components namely a fuzzy logic controller, a reference model and a learning mechanism. It
utilizes a learning mechanism, which automatically adjusts the rule base of the fuzzy controller
so that the closed loop performs according to the user defined reference model containing
information of the desired behavior of the controlled system.
Although the proposed DAC shows better performance compared to FLAC but it suffers from
the complexity of formulating a multivariable regressor vector for the TLFM. Also, the adaptive
mechanism for parameter updates of both the DAC and FLAC depend upon feedback error based
supervised learning. Hence, a reinforcement learning (RL) technique is employed to derive an
adaptive controller for the TLFM. The new reinforcement learning based adaptive control
(RLAC) has an advantage that it attains optimal control adaptively in on-line. Also, the
performance of the RLAC is compared with that of the DAC and FLAC.
In the past, most of the indirect adaptive controls for a FLM are based on linear identified
model. However, the considered TLFM dynamics is highly nonlinear. Hence, a nonlinear
autoregressive moving average with exogenous input (NARMAX) model based new Self-Tuning
Control (NMSTC) is proposed. The proposed adaptive controller uses a multivariable Proportional Integral Derivative (PID) self-tuning control strategy. The parameters of the PID
are adapted online using a nonlinear autoregressive moving average with exogenous-input
(NARMAX) model of the TLFM. Performance of the proposed NMSTC is compared with that
of RLAC.
The proposed NMSTC law suffers from over-parameterization of the controller. To overcome
this a new nonlinear adaptive model predictive control using the NARMAX model of the TLFM
(NMPC) developed next. For the proposed NMPC, the current control action is obtained by
solving a finite horizon open loop optimal control problem on-line, at each sampling instant,
using the future predicted model of the TLFM. NMPC is based on minimization of a set of
predicted system errors based on available input-output data, with some constraints placed on the
projected control signals resulting in an optimal control sequence. The performance of the
proposed NMPC is also compared with that of the NMSTC.
Performances of all the developed algorithms are assessed by numerical simulation in
MATLAB/SIMULINK environment and also validated through experimental studies using a
physical TLFM set-up available in Advanced Control and Robotics Research Laboratory,
National Institute of Technology Rourkela. It is observed from the comparative assessment of the
performances of the developed adaptive controllers that proposed NMPC exhibits superior
7performance in terms of accurate tip position tracking (steady state error ≈ 0.01°) while
suppressing the tip deflections (maximum amplitude of the tip deflection ≈ 0.1 mm) when the
manipulator handles variation in payload (increased payload of 0.3 kg).
The adaptive control strategies proposed in this thesis can be applied to control of complex
flexible space shuttle systems, long reach manipulators for hazardous waste management from
safer distance and for damping of oscillations for similar vibration systems