Model-reference adaptive control based on neurofuzzy networks

Model reference adaptive control (MRAC) is a popular approach to control linear systems, as it is relatively simple to implement. However, the performance of the linear MRAC deteriorates rapidly when the system becomes nonlinear. In this paper, a nonlinear MRAC based on neurofuzzy networks is derived. Neurofuzzy networks are chosen not only because they can approximate nonlinear functions with arbitrary accuracy, but also they are compact in their supports, and the weights of the network can be readily updated on-line. The implementation of the neurofuzzy network-based MRAC is discussed, and the local stability of the system controlled by the proposed controller is established. The performance of the neurofuzzy network-based MRAC is illustrated by examples involving both linear and nonlinear systems. © 2004 IEEE.published_or_final_versio

Fully Evolvable Optimal Neurofuzzy Controller Using Adaptive Critic Designs

A near-optimal neurofuzzy external controller is designed in this paper for a static compensator (STATCOM) in a multimachine power system. The controller provides an auxiliary reference signal for the STATCOM in such a way that it improves the damping of the rotor speed deviations of its neighboring generators. A zero-order Takagi-Sugeno fuzzy rule base constitutes the core of the controller. A heuristic dynamic programming (HDP) based approach is used to further train the controller and enable it to provide nonlinear near-optimal control at different operating conditions of the power system. Based on the connectionist systems theory, the parameters of the neurofuzzy controller, including the membership functions, undergo training. Simulation results are provided that compare the performance of the neurofuzzy controller with and without updating the fuzzy set parameters. Simulation results indicate that updating the membership functions can noticeably improve the performance of the controller and reduce the size of the STATCOM, which leads to lower capital investment

Self-tuning neurofuzzy control for nonlinear systems with offset

A self-tuning neurofuzzy controller with an ability to remove offsets is derived in this paper based on the self-tuning integrating controller derived for the local linear model. The training target for the proposed controllers is derived, and they can be trained by the simplified recursive least squares (RLS) method with a computing time that is linear instead of geometric in the number of weights in the network. Further, the simplified RLS method not only has the same convergence property as the RLS method, it also has a better ability in tracking varying parameters. The performance of the self-tuning neurofuzzy controller is illustrated by examples involving both linear and nonlinear systems.published_or_final_versio

Neurofuzzy controller based full vehicle nonlinear active suspension systems

To design a robust controller for active suspension systems is very important for guaranteeing the riding comfort for passengers and road handling quality for a vehicle. In this thesis, the mathematical model of full vehicle nonlinear active suspension systems with hydraulic actuators is derived to take into account all the motions of the vehicle and the nonlinearity behaviours of the active suspension system and hydraulic actuators. Four robust control types are designed and the comparisons among the robustness of those controllers against different disturbance types are investigated to select the best controller among them. The MATLAB SIMULINK toolboxes are used to simulate the proposed controllers with the controlled model and to display the responses of the controlled model under different types of disturbance. The results show that the neurofuzzy controller is more effective and robust than the other controller types. The implementation of the neurofuzzy controller using FPGA boards has been investigated in this work. The Xilinx ISE program is employed to synthesis the VHDL codes that describe the operation of the neurofuzzy controller and to generate the configuration file used to program the FPGA. The ModelSim program is used to simulate the operation of the VHDL codes and to obtain the expected output data of the FPGA boards. To confirm that FPGA the board used as the neurofuzzy controller system operated as expected, a MATLAB script file is used to compare the set of data obtained from the ModelSim program and the set of data obtained from the MATLAB SIMULINK model. The results show that the FPGA board is effective to be used as a neurofuzzy controller for full vehicle nonlinear active suspension systems. The active suspension system has a great performance for vibration isolation. However the main drawback of the active suspension is that it is high energy consumptive. Therefore, to use this suspension system in the proposed model, this drawback should be solved. Electromagnetic actuators are used to convert the vibration energy that arises from the rough road to useful electrical energy to reduce the energy consumption by the active suspension systems. The results show that the electromagnetic devices act as a power generator, i.e. the vibration energy excited by the rough road surface has been converted to a useful electrical energy supply for the actuators. Furthermore, when the nonlinear damper models are replaced by the electromagnetic actuators, riding comfort and the road handling quality are improved. As a result, two targets have been achieved by using hydraulic actuators with electromagnetic suspension systems: increasing fuel economy and improving the vehicle performance

Design an intelligent controller for full vehicle nonlinear active suspension systems

The main objective of designed the controller for a vehicle suspension system is to reduce the discomfort sensed by passengers which arises from road roughness and to increase the ride handling associated with the pitching and rolling movements. This necessitates a very fast and accurate controller to meet as much control objectives, as possible. Therefore, this paper deals with an artificial intelligence Neuro-Fuzzy (NF) technique to design a robust controller to meet the control objectives. The advantage of this controller is that it can handle the nonlinearities faster than other conventional controllers. The approach of the proposed controller is to minimize the vibrations on each corner of vehicle by supplying control forces to suspension system when travelling on rough road. The other purpose for using the NF controller for vehicle model is to reduce the body inclinations that are made during intensive manoeuvres including braking and cornering. A full vehicle nonlinear active suspension system is introduced and tested. The robustness of the proposed controller is being assessed by comparing with an optimal Fractional Order (FOPID) controller. The results show that the intelligent NF controller has improved the dynamic response measured by decreasing the cost function

Online Intelligent Controllers for an Enzyme Recovery Plant: Design Methodology and Performance

This paper focuses on the development of intelligent controllers for use in a process of enzyme recovery from pineapple rind. The proteolytic enzyme bromelain (EC 3.4.22.4) is precipitated with alcohol at low temperature in a fed-batch jacketed tank. Temperature control is crucial to avoid irreversible protein denaturation. Fuzzy or neural controllers offer a way of implementing solutions that cover dynamic and nonlinear processes. The design methodology and a comparative study on the performance of fuzzy-PI, neurofuzzy, and neural network intelligent controllers are presented. To tune the fuzzy PI Mamdani controller, various universes of discourse, rule bases, and membership function support sets were tested. A neurofuzzy inference system (ANFIS), based on Takagi-Sugeno rules, and a model predictive controller, based on neural modeling, were developed and tested as well. Using a Fieldbus network architecture, a coolant variable speed pump was driven by the controllers. The experimental results show the effectiveness of fuzzy controllers in comparison to the neural predictive control. The fuzzy PI controller exhibited a reduced error parameter (ITAE), lower power consumption, and better recovery of enzyme activity

Analysis and Application of Advanced Control Strategies to a Heating Element Nonlinear Model

open4siSustainable control has begun to stimulate research and development in a wide range of industrial communities particularly for systems that demand a high degree of reliability and availability (sustainability) and at the same time characterised by expensive and/or safety critical maintenance work. For heating systems such as HVAC plants, clear conflict exists between ensuring a high degree of availability and reducing costly maintenance times. HVAC systems have highly non-linear dynamics and a stochastic and uncontrollable driving force as input in the form of intake air speed, presenting an interesting challenge for modern control methods. Suitable control methods can provide sustainable maximisation of energy conversion efficiency over wider than normally expected air speeds and temperatures, whilst also giving a degree of “tolerance” to certain faults, providing an important impact on maintenance scheduling, e.g. by capturing the effects of some system faults before they become serious.This paper presents the design of different control strategies applied to a heating element nonlinear model. The description of this heating element was obtained exploiting a data driven and physically meaningful nonlinear continuous time model, which represents a test bed used in passive air conditioning for sustainable housing applications. This model has low complexity while achieving high simulation performance. The physical meaningfulness of the model provides an enhanced insight into the performance and functionality of the system. In return, this information can be used during the system simulation and improved model based and data driven control designs for tight temperature regulation. The main purpose of this study is thus to give several examples of viable and practical designs of control schemes with application to this heating element model. Moreover, extensive simulations and Monte Carlo analysis are the tools for assessing experimentally the main features of the proposed control schemes, in the presence of modelling and measurement errors. These developed control methods are also compared in order to evaluate advantages and drawbacks of the considered solutions. Finally, the exploited simulation tools can serve to highlight the potential application of the proposed control strategies to real air conditioning systems.openTurhan, T.; Simani, S.; Zajic, I.; Gokcen Akkurt, G.Turhan, T.; Simani, Silvio; Zajic, I.; Gokcen Akkurt, G

Development of an adaptive neurofuzzy controller

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This paper focuses on the development of intelligent controllers for use in a process of enzyme recovery from pineapple rind. The proteolytic enzyme bromelain (EC 3.4.22.4) is precipitated with alcohol at low temperature in a fed-batch jacketed tank. Temperature control is crucial to avoid irreversible protein denaturation. Fuzzy or neural controllers offer a way of implementing solutions that cover dynamic and nonlinear processes. The design methodology and a comparative study on the performance of fuzzy-PI, neurofuzzy, and neural network intelligent controllers are presented. To tune the fuzzy PI Mamdani controller, various universes of discourse, rule bases, and membership function support sets were tested. A neurofuzzy inference system (ANFIS), based on Takagi-Sugeno rules, and a model predictive controller, based on neural modeling, were developed and tested as well. Using a Fieldbus network architecture, a coolant variable speed pump was driven by the controllers. The experimental results show the effectiveness of fuzzy controllers in comparison to the neural predictive control. The fuzzy PI controller exhibited a reduced error parameter (ITAE), lower power consumption, and better recovery of enzyme activity