31 research outputs found
Inverse model based control for a twin rotor system
The use of active control technique has intensified in
various control applications, particularly in the field of aircraft
systems. A laboratory set-up system which resembles the
behaviour of a helicopter, namely twin rotor multi-input multioutput system (TRMS) is used as an experimental rig in this
research. This paper presents an investigation using inverse
model control for the TRMS. The control techniques embraced in
this work are direct inverse-model control, augmented PID with
feedforward inverse-model control and augmented PID with
feedback inverse-model control. Particle swarm optimization
(PSO) method is used to tune the parameter of PID controller. To
demonstrate the applicability of the methods, a simulated
hovering motion of the TRMS, derived from experimental data is
considered. The proposed inverse model based controller is
shown to be capable of handling both systems dynamic as well as
rigid body motion of the system, providing good overall system
performance
A single-step identification strategy for the coupled TITO process using fractional calculus
The reliable performance of a complete control system depends on accurate model information being used to represent each subsystem. The identification
and modelling of multivariable systems are complex and challenging due to cross-coupling. Such a system may require multiple steps and decentralized
testing to obtain full system models effectively. In this paper, a direct identification strategy is proposed for the coupled two-input two-output
(TITO) system with measurable input–output signals. A well-known closed-loop relay test is utilized to generate a set of inputs–outputs data from a single
run. Based on the collected data, four individual fractional-order transfer functions, two for main paths and two for cross-paths, are estimated from
single-run test signals. The orthogonal series-based algebraic approach is adopted, namely the Haar wavelet operational matrix, to handle the fractional
derivatives of the signal in a simple manner. A single-step strategy yields faster identification with accurate estimation. The simulation and experimental
studies depict the efficiency and applicability of the proposed identification technique. The demonstrated results on the twin rotor multiple-input multiple-
output (MIMO) system (TRMS) clearly reveal that the presented idea works well with the highly coupled system even in the presence of measurement
noise
Dynamic modelling and control of a flexible manoeuvring system.
In this research a twin rotor multi-input multi-output system (TRMS), which is a
laboratory platform with 2 degrees of freedom (DOF) is considered. Although, the
TRMS does not fly, it has a striking similarity with a helicopter, such as system
nonlinearities and cross-coupled modes. Therefore, the TRMS can be perceived as
an unconventional and complex "air vehicle" that poses formidable challenges in
modelling, control design and analysis, and implementation. These issues constitute
the scope of this research.
Linear and nonlinear models for the vertical movement of the TRMS are
obtained via system identification techniques using black-box modelling. The
approach yields input-output models without a priori defined model structure or
specific parameter settings reflecting any physical attributes of the system. Firstly,
linear parametric models, characterising the TRMS in its hovering operation mode,
are obtained using the potential of recursive least squares (RLS) estimation and
genetic algorithms (GAs). Further, a nonlinear model using multi-layer perceptron
(MLP) neural networks (NNs) is obtained. Such a high fidelity nonlinear model is
often required for nonlinear system simulation studies and is commonly employed in
the aerospace industry. Both time and frequency domain analyses are utilised to
investigate and develop confidence in the models obtained. The frequency domain
verification method is a useful tool in the validation of extracted parametric models.
It allows high-fidelity verification of dynamic characteristics over a frequency range
of interest. The resulting models are utilized in designing controllers for low
frequency vibration suppression, development of suitable feedback control laws for
set-point tracking, and design of augmented feedforward and feedback control
schemes for both vibration suppression and set-point tracking performance. The
modelling approaches presented here are shown to be suitable for modelling
complex new generation air vehicles, whose flight mechanics are not well
understood.
Modelling of the TRMS revealed the presence of resonance modes, which are
responsible for inducing unwanted vibrations in the system. Command shaping
11
control strategies are developed to reduce motion and uneven mass induced
vibrations, produced by the main rotor during the vertical movement around the
lateral axis of the TRMS rig. 2-impulse, 3-impulse and 4-impulse sequence input
shapers and Iow-pass and band-stop digital filters are developed to shape the
command signals such that the resonance modes are not overly excited. The
effectiveness of this concept is then demonstrated in both simulation and real-time
experimental environments in terms of level of vibration reduction using power
spectral density profiles of the system response.
Combinations of intelligent and conventional techniques are commonly used
the control of complex dynamic systems. Such hybrid schemes have proved to be
efficient and can overcome the deficiencies of conventional and intelligent
controllers alone. The current study is confined to the development of two forms of
hybrid control schemes that combine fuzzy control and conventional PID
compensator for input tracking performance. The two hybrid control strategies
comprising conventional PO control plus PlO compensator and PO-type fuzzy
control plus PlO compensator are developed and implemented for set-point tracking
control of the vertical movement of the TRMS rig. It is observed that the hybrid
control schemes are superior to other feedback control strategies namely, PlO
compensator, pure PO-type and PI-type fuzzy controllers in terms of time domain
system behaviour.
This research also witnesses investigations into the development of an
augmented feedforward and feedback control scheme (AFFCS) for the control of
rigid body motion and vibration suppression of the TRMS. The main goal of this
framework is to satisfy performance objectives in terms of robust command tracking,
fast system response and minimum residual vibration. The developed control
strategies have been designed and implemented within both simulation and real-time
environments of the TRMS rig. The employed control strategies are shown to
demonstrate acceptable performances. The obtained results show that much
improved tracking is achieved on positive and negative cycles of the reference
signal, as compared to that without any control action. The system performance with
the feedback controller is significantly improved when the feedforward control
component is added. This leads to the conclusion that augmenting feedback control
with feedforward method can lead to more practical and accurate control of flexible
systems such as the TRMS
Controller tuning by means of multi-objective optimization algorithms: a global tuning framework
© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.A holistic multi-objective optimization design technique for controller tuning is presented. This approach gives control engineers greater flexibility to select a controller that matches their specifications. Furthermore, for a given controller it is simple to analyze the tradeoff achieved between conflicting objectives. By using the multi-objective design technique it is also possible to perform a global comparison between different control strategies in a simple and robust way. This approach thereby enables an analysis to be made of whether a preference for a certain control technique is justified. This proposal is evaluated and validated in a nonlinear multiple-input multiple-output system using two control strategies: a classical proportional- integral-derivative control scheme and a feedback state controller.This work was supported in part by the FPI-2010/19 Grant and the Project PAID-06-11 from the Universitat Politecnica de Valencia and in part by the Projects DPI2008-02133, TIN2011-28082, and ENE2011-25900 from the Spanish Ministry of Science and Innovation.Reynoso Meza, G.; García-Nieto Rodríguez, S.; Sanchís Saez, J.; Blasco, X. (2013). Controller tuning by means of multi-objective optimization algorithms: a global tuning framework. IEEE Transactions on Control Systems Technology. 21(2):445-458. https://doi.org/10.1109/TCST.2012.2185698S44545821
Model Identification and Robust Nonlinear Model Predictive Control of a Twin Rotor MIMO System
PhDThis thesis presents an investigation into a number of model predictive control
(MPC) paradigms for a nonlinear aerodynamics test rig, a twin rotor multi-input
multi-output system (TRMS). To this end, the nonlinear dynamic model of the
system is developed using various modelling techniques. A comprehensive study is
made to compare these models and to select the best one to be used for control
design purpose. On the basis of the selected model, a state-feedback multistep
Newton-type MPC is developed and its stability is addressed using a terminal
equality constraint approach. Moreover, the state-feedback control approach is
combined with a nonlinear state observer to form an output-feedback MPC. Finally,
a robust MPC technique is employed to address the uncertainties of the system.
In the modelling stage, analytical models are developed by extracting the physical
equations of the system using the Newtonian and Lagrangian approaches. In the case
of the black-box modelling, artificial neural networks (ANNs) are utilised to model
the TRMS. Finally, the grey-box model is used to enhance the performance of the
white-box model developed earlier through the optimisation of parameters using a
genetic algorithm (GA) based approach. Stability analysis of the autonomous TRMS
is carried out before designing any control paradigms for the system.
In the control design stage, an MPC method is proposed for constrained nonlinear
systems, which is the improvement of the multistep Newton-type control strategy.
The stability of the proposed state-feedback MPC is guaranteed using terminal
equality constraints. Moreover, the formerly proposed MPC algorithm is combined
with an unscented Kalman filter (UKF) to formulate an output-feedback MPC. An
extended Kalman filter (EKF) based on a state-dependent model is also introduced,
whose performance is found to be better compared to that of the UKF. Finally, a
robust MPC is introduced and implemented on the TRMS based on a polytopic
uncertainty that is cast into linear matrix inequalities (LMI)
Evolutionary algorithms for active vibration control of flexible manipulator
Flexible manipulator systems offer numerous advantages over their rigid counterparts including light weight, faster system response, among others. However, unwanted vibration will occur when flexible manipulator is subjected to disturbances. If the advantages of flexible manipulator are not to be sacrificed, an accurate model and efficient control system must be developed. This thesis presents the development of a Proportional-Integral-Derivative (PID) controller tuning method using evolutionary algorithms (EA) for a single-link flexible manipulator system. Initially, a single link flexible manipulator rig, constrained to move in horizontal direction, was designed and fabricated. The input and output experimental data of the hub angle and endpoint acceleration of the flexible manipulator were acquired. The dynamics of the system was later modeled using a system identification (SI) method utilizing EA with linear auto regressive with exogenous (ARX) model structure. Two novel EAs, Genetic Algorithm with Parameter Exchanger (GAPE) and Particle Swarm Optimization with Explorer (PSOE) have been developed in this study by modifying the original Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms. These novel algorithms were introduced for the identification of the flexible manipulator system. Their effectiveness was then evaluated in comparison to the original GA and PSO. Results indicated that the identification of the flexible manipulator system using PSOE is better compared to other methods. Next, PID controllers were tuned using EA for the input tracking and the endpoint vibration suppression of the flexible manipulator structure. For rigid motion control of hub angle, an auto-tuned PID controller was implemented. While for vibration suppression of the endpoint, several PID controllers were tuned using GA, GAPE, PSO and PSOE. The results have shown that the conventional auto-tuned PID was effective enough for the input tracking of the rigid motion. However, for end-point vibration suppression, the result showed the superiority of PID-PSOE in comparison to PID-GA, PID-GAPE and PID-PSO. The performance of the best simulated controller was validated experimentally later. Through experimental validation, it was found that the PID-PSOE was capable to suppress the vibration of the single-link flexible manipulator with highest attenuation of 31.3 dB at the first mode of the vibration. The outcomes of this research revealed the effectiveness of the PID controller tuned using PSOE for the endpoint vibration suppression of the flexible manipulator amongst other evolutionary methods
Modelling and control of a twin rotor MIMO system.
In this research, a laboratory platform which has 2 degrees of freedom (DOF), the Twin
Rotor MIMO System (TRMS), is investigated. Although, the TRMS does not fly, it has
a striking similarity with a helicopter, such as system nonlinearities and cross-coupled
modes. Therefore, the TRMS can be perceived as an unconventional and complex "air
vehicle" that poses formidable challenges in modelling, control design and analysis and
implementation. These issues are the subject of this work.
The linear models for 1 and 2 DOFs are obtained via system identification techniques.
Such a black-box modelling approach yields input-output models with neither a priori
defined model structure nor specific parameter settings reflecting any physical
attributes. Further, a nonlinear model using Radial Basis Function networks is obtained.
Such a high fidelity nonlinear model is often required for nonlinear system simulation
studies and is commonly employed in the aerospace industry. Modelling exercises were
conducted that included rigid as well as flexible modes of the system. The approach
presented here is shown to be suitable for modelling complex new generation air
vehicles.
Modelling of the TRMS revealed the presence of resonant system modes which are
responsible for inducing unwanted vibrations. In this research, open-loop, closed-loop
and combined open and closed-loop control strategies are investigated to address this
problem. Initially, open-loop control techniques based on "input shaping control" are
employed. Digital filters are then developed to shape the command signals such that the
resonance modes are not overly excited. The effectiveness of this concept is then
demonstrated on the TRMS rig for both 1 and 2 DOF motion, with a significant
reduction in vibration.
The linear model for the 1 DOF (SISO) TRMS was found to have the non-minimum
phase characteristics and have 4 states with only pitch angle output. This behaviour
imposes certain limitations on the type of control topologies one can ado·pt. The LQG
approach, which has an elegant structure with an embedded Kalman filter to estimate
the unmeasured states, is adopted in this study.
The identified linear model is employed in the design of a feedback LQG compensator
for the TRMS with 1 DOF. This is shown to have good tracking capability but requires.
high control effort and has inadequate authority over residual vibration of the system.
These problems are resolved by further augmenting the system with a command path
prefilter. The combined feedforward and feedback compensator satisfies the
performance objectives and obeys the constraint on the actuator. Finally, 1 DOF
controller is implemented on the laboratory platform
Data-Driven Model-Free Sliding Mode and Fuzzy Control with Experimental Validation
The paper presents the combination of the model-free control technique with two popular nonlinear control techniques, sliding mode control and fuzzy control. Two data-driven model-free sliding mode control structures and one data-driven model-free fuzzy control structure are given. The data-driven model-free sliding mode control structures are built upon a model-free intelligent Proportional-Integral (iPI) control system structure, where an augmented control signal is inserted in the iPI control law to deal with the error dynamics in terms of sliding mode control. The data-driven model-free fuzzy control structure is developed by fuzzifying the PI component of the continuous-time iPI control law. The design approaches of the data-driven model-free control algorithms are offered. The data-driven model-free control algorithms are validated as controllers by real-time experiments conducted on 3D crane system laboratory equipment
Active vibration control of flexible beam incorporating recursive least square and neural network algorithms
In recent years, active vibration control (AVC) has emerged as an important area of scient ific study especially for vibrat ion suppression of flexible structures. Flexible structures offer great advantages in contrast to the conventional structures, but necessary action must be taken for cancelling the unwanted vibration. In this research, a simulation algorithm represent ing flexible beam with specific condit ions was derived from Euler Bernoulli beam theory. The proposed finite difference (FD) algorithm was developed in such way that it allows the disturbance excitat ion at various points. The predicted resonance frequencies were recorded and validated with theoretical and experimental values. Subsequent ly, flexible beam test rig was developed for collecting data to be used in system ident ificat ion (SI) and controller development. The experimental rig was also utilised for implementation and validat ion of controllers. In this research, parametric and nonparametric SI approaches were used for characterising the dynamic behaviour of a lightweight flexible beam using input - output data collected experimentally. Tradit ional recursive least square (RLS) method and several artificial neural network (ANN) architectures were utilised in emulat ing this highly nonlinear dynamic system here. Once the model of the system was obtained, it was validated through a number of validation tests and compared in terms of their performance in represent ing a real beam. Next, the development of several convent ional and intelligent control schemes with collocated and non-collocated actuator sensor configurat ion for flexible beam vibrat ion attenuation was carried out. The invest igat ion involves design of convent ional proportional-integral-derivat ive (PID) based, Inverse recursive least square active vibrat ion control (RLS-AVC), Inverse neuro active vibration control (Neuro-AVC), Inverse RLS-AVC with gain and Inverse Neuro-AVC with gain controllers. All the developed controllers were tested, verified and validated experimentally. A comprehensive comparat ive performance to highlight the advantages and drawbacks of each technique was invest igated analyt ically and experimentally. Experimental results obtained revealed the superiorit y of Inverse RLS-AVC with gain controller over convent ional method in reducing the crucial modes of vibration of flexible beam structure. Vibration attenuation achieved using proportional (P), proportional-integral (PI), Inverse RLS-AVC, Inverse Neuro- AVC, Inverse RLS-AVC with gain and Inverse Neuro-AVC with gain control strategies are 9.840 dB, 6.840 dB, 9.380 dB, 8.590 dB, 17.240 dB and 5.770 dB, respectively
Evolutionary optimisation and real-time self-tuning active vibration control of a flexible beam system
Active vibration control has long been recognised as a solution for flexible beam structure to achieve sufficient vibration suppression. The flexible beam dynamic model is derived according to the Euler Bernoulli beam theory. The resonance frequencies of the beam are investigated analytically and the validity was experimentally verified. This thesis focuses on two main parts: proportional-integralderivative (PID) controller tuning methods based on evolutionary algorithms (EA) and real-time self-tuning control using iterative learning algorithm and poleplacement methods. Optimisation methods for determining the optimal values of proportional-integral-derivative (PID) controller parameters for active vibration control of a flexible beam system are presented. The main objective of tuning the PID controller is to obtain a fast and stable system using EA such as genetic algorithm (GA) and differential evolution (DE) algorithms. The PID controller is tuned offline based on the identified model obtained using experimental input-output data. Experimental results have shown that PID parameters tuned by EA outperformed conventional tuning method in term of better transient response. However, in term of vibration attenuation, the performance between DE, GA and Ziegler-Nichols (ZN) method produced about the same value. For real-time selftuning control, successful design and implementation has been accomplished. Two techniques, self-tuning using iterative learning algorithm and self-tuning poleplacement control were implemented to adapt the controller parameters to meet the desired performances. In self-tuning using iterative learning algorithm, its learning mechanism will automatically find new control parameters. Whereas the self tuning pole-placement control uses system identification in real time and then the control parameters are calculated online. It is observed that self-tuning using iterative learning algorithm does not require accurate model of the plant and control the vibration based on the reference error, but it is unable to maintain its transient performance due to the change of physical parameters. Meanwhile, self-tuning poleplacement controller has shown its ability to maintain its transient performance as it was designed based on the desired closed loop poles where the control system can track changes in the plant and disturbance characteristics at every sampling time. Overall results revealed the effectiveness of both control schemes in suppressing the unwanted vibration over conventional fixed gain controllers