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Novel fuzzy logic controllers with self-tuning capability
Two controllers which extend the PD+I fuzzy logic controller to deal with the plant having time varying nonlinear dynamics are proposed. The adaptation ability of the first self tuning PD+I fuzzy logic controller (STPD+I_31) is achieved by adjusting the output scaling factor automatically thereby contributing to significant improvement in performance. Second controller (STPD+I_9) is the simplified version of STPD+I_31 which is designed under the imposed constraint that allows only minimum number of rules in the rule bases. The proposed controllers are compared with two classical nonlinear controllers: the pole placement self tuning PID controller and sliding mode controller. All the controllers are applied to the two-links revolute robot for the tracking control. The tracking performance of STPD+I_31 and STPD+I_9 are much better than the pole placement self tuning PID controller during high speed motions while the performance are comparable at low and medium speed. In addition, STPD+I_31 and STPD+I_9 outperform sliding mode controller using same method of comparison study
Control strategies for robotic manipulators
This survey is aimed at presenting the major robust control strategies for rigid robot manipulators. The techniques discussed are feedback linearization/Computed torque control, Variable structure compensator, Passivity based approach and Disturbance observer based control. The first one is based on complete dynamic model of a robot. It results in simple linear control which offers guaranteed stability. Variable structure compensator uses a switching/relay action to overcome dynamic uncertainties and disturbances. Passivity based controller make use of passive structure of a robot. If passivity of a feedback system is proved, nonlinearities and uncertainties will not affect the stability. Disturbance observer based controllers estimate disturbances, which can be cancelled out to achieve a nominal model, for which a simple controller can then be designed. This paper, after explaining each control strategy in detail, finally compares these strategies for their pros and cons. Possible solutions to cope with the drawbacks have also been presented in tabular form. © 2012 IEEE
INTELLIGENT FAULT TOLERANT CONTROL SCHEMES FOR AUTONOMOUS UNDERWATER VEHICLES
The area of autonomous underwater vehicles (AUVs) is an increasingly important area of
research, with AUVs being capable of handling a far wider range of missions than either
an inhabited underwater vehicle or a remotely operated vehicle (ROV). One of the major
drawbacks of such vehicles is the inability of their control systems to handle faults
occurring within the vehicle during a mission. This study aims to develop enhancements
to an existing control system in order to increase its fault tolerance to both sensor and
actuator faults.
Faults occurring within the sensors for both the yaw and roll channels of the AUV are
considered. Novel fuzzy inference systems (FISs) are developed and tuned using both the
adaptive neuro-fuzzy inference system (ANFIS) and simulated annealing tuning methods.
These FISs allow the AUV to continue operating after a fault has occurred within the
sensors.
Faults occurring within the actuators which control the canards of the AUV and hence the
yaw channel are also examined. Actuator recovery FISs capable of handling faults
occurring within the actuators are developed using both the simulated annealing and tabu
search methods of tuning FISs. The fault tolerance of the AUV is then further enhanced
by the development of an error estimation FIS that is used to replace an error sensor.
It concludes that the novel FISs designed and developed within the thesis provide an
improved performance to both sensor and actuator faults in comparison to benchmark
control systems. Therefore having these FISs embedded within the overall control
scheme ensure the AUV is fault tolerant to a range of selected failures
Fuzzy PD control of an optically guided long reach robot
This thesis describes the investigation and development of a fuzzy controller for a manipulator with a single flexible link. The novelty of this research is due to the fact that the controller devised is suitable for flexible link manipulators with a round cross section. Previous research has concentrated on control of flexible slender structures that are relatively easier to model as the vibration effects of torsion can be ignored. Further novelty arises due to the fact that this is the
first instance of the application of fuzzy control in the optical Tip Feedback Sensor (TFS) based configuration.
A design methodology has been investigated to develop a fuzzy controller suitable for application in a safety critical environment such as the nuclear industry. This methodology provides justification for all the parameters of the fuzzy controller including membership fUllctions, inference and defuzzification techniques and the operators used in the algorithm. Using the novel modified phase plane method investigated in this thesis, it is shown that the derivation of complete, consistent and non-interactive rules can be achieved. This methodology was successfully applied
to the derivation of fuzzy rules even when the arm was subjected to different payloads. The design approach, that targeted real-time embedded control applicat.ions from the outset, results in a controller implementation that is suitable for cheaper CPU constrained and memory challenged
embedded processors.
The controller comprises of a fuzzy supervisor that is used to alter the derivative term of a linear classical Proportional + Derivative (PD) controller. The derivative term is updated in relation to the measured tip error and its derivative obtained through the TFS based configuration. It is shown that by adding 'intelligence' to the control loop in this way, the performance envelope of the classical controller can be enhanced. A 128% increase in payload, 73.5% faster settling time and a reduction of steady state of over 50% is achieved using fuzzy control over its classical counterpart
Intelligent control of nonlinear systems with actuator saturation using neural networks
Common actuator nonlinearities such as saturation, deadzone, backlash, and hysteresis are unavoidable in practical industrial control systems, such as computer numerical control (CNC) machines, xy-positioning tables, robot manipulators, overhead crane mechanisms, and more. When the actuator nonlinearities exist in control systems, they may exhibit relatively large steady-state tracking error or even oscillations, cause the closed-loop system instability, and degrade the overall system performance. Proportional-derivative (PD) controller has observed limit cycles if the actuator nonlinearity is not compensated well. The problems are particularly exacerbated when the required accuracy is high, as in micropositioning devices. Due to the non-analytic nature of the actuator nonlinear dynamics and the fact that the exact actuator nonlinear functions, namely operation uncertainty, are unknown, the saturation compensation research is a challenging and important topic with both theoretical and practical significance.
Adaptive control can accommodate the system modeling, parametric, and environmental structural uncertainties. With the universal approximating property and learning capability of neural network (NN), it is appealing to develop adaptive NN-based saturation compensation scheme without explicit knowledge of actuator saturation nonlinearity. In this dissertation, intelligent anti-windup saturation compensation schemes in several scenarios of nonlinear systems are investigated. The nonlinear systems studied within this dissertation include the general nonlinear system in Brunovsky canonical form, a second order multi-input multi-output (MIMO) nonlinear system such as a robot manipulator, and an underactuated system-flexible robot system. The abovementioned methods assume the full states information is measurable and completely known.
During the NN-based control law development, the imposed actuator saturation is assumed to be unknown and treated as the system input disturbance. The schemes that lead to stability, command following and disturbance rejection is rigorously proved, and verified using the nonlinear system models. On-line NN weights tuning law, the overall closed-loop performance, and the boundedness of the NN weights are rigorously derived and guaranteed based on Lyapunov approach. The NN saturation compensator is inserted into a feedforward path. The simulation conducted indicates that the proposed schemes can effectively compensate for the saturation nonlinearity in the presence of system uncertainty
Performance and Configuration Analysis of Tracking Time Anti-Windup PID Controllers
As popular as the application of Proportional Integral Derivative (PID) controller is, issues relating to saturation effects are still being addressed using different techniques. Amongst such techniques are clamping anti-windup technique and back-calculation anti-windup techniques which primary prevent the integral term of the PID control action from reaching saturation. Separate tracking time technique was applied to both cases of anti-windup techniques investigated in this research unlike the conventional tracking time. These anti-windup controllers were used to control the operation of a motorized globe valve. The results obtained after simulation in MATLAB Simulink environment showed that both techniques gave similar outputs with a stable response of magnitude 0.95 at 1.5 seconds settling time when a unit step reference input signal was applied as compared to conventional PID controller that had an overshoot of 1.04 before settling to a magnitude of 1.0 at 1.5 seconds. Vibration, instability, and operational distortion were experienced when the anti-windup techniques were cascaded. The same responses were obtained when their outputs were combined to control the motorized globe valve. Other interesting mathematical models of important components are contained in the full paper
PAC: A Novel Self-Adaptive Neuro-Fuzzy Controller for Micro Aerial Vehicles
There exists an increasing demand for a flexible and computationally
efficient controller for micro aerial vehicles (MAVs) due to a high degree of
environmental perturbations. In this work, an evolving neuro-fuzzy controller,
namely Parsimonious Controller (PAC) is proposed. It features fewer network
parameters than conventional approaches due to the absence of rule premise
parameters. PAC is built upon a recently developed evolving neuro-fuzzy system
known as parsimonious learning machine (PALM) and adopts new rule growing and
pruning modules derived from the approximation of bias and variance. These rule
adaptation methods have no reliance on user-defined thresholds, thereby
increasing the PAC's autonomy for real-time deployment. PAC adapts the
consequent parameters with the sliding mode control (SMC) theory in the
single-pass fashion. The boundedness and convergence of the closed-loop control
system's tracking error and the controller's consequent parameters are
confirmed by utilizing the LaSalle-Yoshizawa theorem. Lastly, the controller's
efficacy is evaluated by observing various trajectory tracking performance from
a bio-inspired flapping-wing micro aerial vehicle (BI-FWMAV) and a rotary wing
micro aerial vehicle called hexacopter. Furthermore, it is compared to three
distinctive controllers. Our PAC outperforms the linear PID controller and
feed-forward neural network (FFNN) based nonlinear adaptive controller.
Compared to its predecessor, G-controller, the tracking accuracy is comparable,
but the PAC incurs significantly fewer parameters to attain similar or better
performance than the G-controller.Comment: This paper has been accepted for publication in Information Science
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