Modeling and controller design of a single-linked inverted pendulum using optimized fuzzy logic controller approach

Inverted pendulum (IP) is an underactuated systems, since the input of the system is the force applied to the cart and the outputs are the cart position and pendulum angle (SIMO) system, which makes this system is highly nonlinear and unstable. Inverted pendulum considered as the one the most famous classical systems in the field of control and mechatronics. This project focuses on the design of a fuzzy controller to stabilize an inverted pendulum in a vertical position. A continuous correction mechanism is required to move the cart in a certain way in order to balance the pendulum to prevent it from falling down. This project started by a derivation of the mathematical model of the single linked inverted pendulum system by using Euler-Lagrange method. After that, a fuzzy logic controller (FLC) based Sugeno inference system was designed and genetic algorithm was used to tune the parameters of the controller using MATLAB software. Both controllers were tested using real time inverted pendulum. Experimental results showed that optimized FLC was much better than Sugeno FLC in terms of settling time, overshoot and steady state error

Robust online adaptive neural network control for the regulation of treadmill exercises

The paper proposes a robust online adaptive neural network control scheme for an automated treadmill system. The proposed control scheme is based on Feedback-Error Learning Approach (FELA), by using which the plant Jacobian calculation problem is avoided. Modification of the learning algorithm is proposed to solve the overtraining issue, guaranteeing to system stability and system convergence. As an adaptive neural network controller can adapt itself to deal with system uncertainties and external disturbances, this scheme is very suitable for treadmill exercise regulation when the model of the exerciser is unknown or inaccurate. In this study, exercise intensity (measured by heart rate) is regulated by simultaneously manipulating both treadmill speed and gradient in order to achieve fast tracking for which a single input multi output (SIMO) adaptive neural network controller has been designed. Real-time experiment result confirms that robust performance for nonlinear multivariable system under model uncertainties and unknown external disturbances can indeed be achieved. © 2011 IEEE

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

Computational intelligence techniques for HVAC systems: a review

Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air conditioning (HVAC) systems are the major source of energy consumption in buildings and an ideal candidate for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems.The analysis of trends reveals the minimization of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences. Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE–2, HVACSim+ and ESP–r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on MAS, as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions

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 intelligent control of double-link flexible robotic manipulator

The use of robotic manipulator with multi-link structure has a great influence in most of the current industries. However, controlling the motion of multi-link manipulator has become a challenging task especially when the flexible structure is used. Currently, the system utilizes the complex mathematics to solve desired hub angle with the coupling effect and vibration in the system. Thus, this research aims to develop a dynamic system and controller for double-link flexible robotics manipulator (DLFRM) with the improvement on hub angle position and vibration suppression. A laboratory sized DLFRM moving in horizontal direction is developed and fabricated to represent the actual dynamics of the system. The research utilized neural network as the model estimation. Results indicated that the identification of the DLFRM system using multi-layer perceptron (MLP) outperformed the Elman neural network (ENN). In the controllers’ development, this research focuses on two main parts namely fixed controller and adaptive controller. In fixed controller, the metaheuristic algorithms known as Particle Swarm Optimization (PSO) and Artificial Bees Colony (ABC) were utilized to find optimum value of PID controller parameter to track the desired hub angle and supress the vibration based on the identified models obtained earlier. For the adaptive controller, self-tuning using iterative learning algorithm (ILA) was implemented to adapt the controller parameters to meet the desired performances when there were changes to the system. It was observed that self-tuning using ILA can track the desired hub angle and supress the vibration even when payload was added to the end effector of the system. In contrast, the fixed controller degraded when added payload exceeds 20 g. The performance of these control schemes was analysed separately via real-time PC-based control. The behaviour of the system response was observed in terms of trajectory tracking and vibration suppression. As a conclusion, it was found that the percentage of improvement achieved experimentally by the self-tuning controller over the fixed controller (PID-PSO) for settling time are 3.3 % and 3.28 % of each link respectively. The steady state errors of links 1 and 2 are improved by 91.9 % and 66.7 % respectively. Meanwhile, the vibration suppression for links 1 and 2 are improved by 76.7 % and 67.8 % respectively

Desain Autotuning Kontroler PID Berbasis Algoritma Neural-Network Untuk Sistem Pengaturan Cascade Level dan Flow Liquid Pada Plant Coupled Tanks

Pada industri proses yang melibatkan fluida, suatu fluida akan dipompa dan dialirkan dari satu tangki ke tangki yang lain untuk diolah. Pemindahan cairan dari satu tangki ke tangki yang lain meyebabkan berubahnya level fluida dalam tangki. Dalam pengaturan level, pemindahan cairan biasa disebut sebagai pembebanan pada level. Perubahan beban ini dapat mempengaruhi dari kinerja kontroler.. Kontroler yang banyak digunakan di industri proses adalah kontroler PID karena kesederhanaan struktur dan kehandalannya. Pada penerapan kontroler PID, tuning parameter kontroler sering dilakukan dengan prosedur trial and error. Untuk tetap memenuhi spesifikasi kontrol yang diharapkan, maka perlu dilakukan tuning ulang parameter kontroler PID. Kontroler PID Neural Network ini didesain untuk dapat melakukan autotuning pada parameter kontroler PID sehingga dapat mengatasi perubahan parameter pada plant dan menjaga performa dari plant. Berdasarkan hasil simulasi, sistem pengaturan level air pada plant coupled tank dengan PID Neural Network lebih baik dengan nilai RMSE 0,044 % daripada kontroler PID dengan nilai RMSE 0.35 %. Pada pengujian beban kontroler PI dengan konfigurasi kontrol cascade mampu memberikan hasil yang lebih baik dengan nilai RMSE 1.13 %. ================================================================================= In the industrial processes involving fluid, a fluid to be pumped and drained from one tank to another tank for processing. The transfer of liquid from one tank to another led to changes in the liquid level in the tank. In the level control, commonly referred to as the displacement fluid loading level. This load changes may affect the performance of the controller. The main control strategy used is based on the PID controller design because is simple and robustness. In many times the controller tuning is done by trial and error. Plant in the industry can change the parameters that result from changes in the load on the plant. To keep control of who is expected to meet the specifications it is necessary to retuning PID controller parameters. Neural Network PID controller is designed to perform autotuning in PID controller parameters so that it can cope with changes in the parameters of the plant and maintain the performance of the plant. Based on simulation results, level control system on plant coupled tank with PID Neural Network is better with RMSE value 0.044% then controller PID with RMSE value 0.35%. For load disturbance testing controller PID with cascade configuration control able to profit to better result with RMSE value 1.13 %

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

A Predictive Fuzzy-Neural Autopilot for the Guidance of Small Motorised Marine Craft

This thesis investigates the design and evaluation of a control system, that is able to adapt quickly to changes in environment and steering characteristics. This type of controller is particularly suited for applications with wide-ranging working conditions such as those experienced by small motorised craft. A small motorised craft is assumed to be highly agile and prone to disturbances, being thrown off-course very easily when travelling at high speed 'but rather heavy and sluggish at low speeds. Unlike large vessels, the steering characteristics of the craft will change tremendously with a change in forward speed. Any new design of autopilot needs to be to compensate for these changes in dynamic characteristics to maintain near optimal levels of performance. This study identities the problems that need to be overcome and the variables involved. A self-organising fuzzy logic controller is developed and tested in simulation. This type of controller learns on-line but has certain performance limitations. The major original contribution of this research investigation is the development of an improved self-adaptive and predictive control concept, the Predictive Self-organising Fuzzy Logic Controller (PSoFLC). The novel feature of the control algorithm is that is uses a neural network as a predictive simulator of the boat's future response and this network is then incorporated into the control loop to improve the course changing, as well as course keeping capabilities of the autopilot investigated. The autopilot is tested in simulation to validate the working principle of the concept and to demonstrate the self-tuning of the control parameters. Further work is required to establish the suitability of the proposed novel concept to other control