Prospects for control methods in engineering systems

This paper presents the implementation of a neural network controller (NNC) for maximum power point tracking (MPPT). A fuzzy logic controller (FLC) with three fuzzy sets is designed and its look-up control table is used to train a 3-layer feed-forward neural network. The MPPT controllers are implemented with an 8-bit microcontroller, PIC16F877A, and tested on a power source which comprises a voltage source and a series resistor. Experimental results show that the implemented NNC can stably extract the maximum power. The performance of the NNC approximates to the performance of the real-time FLC and the lookup table FLC. The RAM used in the NNC is less than the RAM used in the real-time FLC. And, the ROM used in the NNC is less than the ROM used in the lookup table FLC.

This paper presents design of Fuzzy Logic Controller (FLC) and Neural Network Controller (NNC) as a Regulator for effective voltage control over a Simple and a Stabilized regulator in order to maintain stability and enhance the closed-loop performance of a power system using a fast computing user friendly Graphical User Interface(GUI). The gains and tuning parameters are kept almost same in Simple, Stabilized, Fuzzy Logic and Neural Network Regulator. The step responses are interfaced on a common GUI page, the performance of Fuzzy Logic Regulator in comparison to the conventional fixed gain regulators proves better but Neural Network Controller has the best results.KeywordsFuzzy LogicNeural NetworkVoltage RegulatorSteady state errorGUI

Objective: BLDC motor specifically used for high speed applications and needs sophisticated speed control. Conventional controls techniques require more physical involvement for assisting BLDC motor to operate with precise speed value. However, soft tuning based controller may give comparatively better results for that and the same has been elaborated in present paper using neural controller of speed as per requirements.the main objective of this study may be summarized as (i) MATLAB simulink model for BLDC motor incorporated with soft tuned controller and inverter simultaneously, (ii) development of neural controller for BLDC motor control, and (iii) validation of results developed through proposed controller with the available results using PI, PID and fuzzy logic controller. Methods: The speed obtained through simulink model with input supply voltage variations are used as a reference signal for the training and testing purpose of neural controller. With the help of these reference signals, neural control executes and gives optimum values of external parameters such as speed and electromagnetic torque. Result: The study outcome provides dynamic execution of proposed controllerbased on neural network. The proposed controller’s results are validated with the reference results obtained mathematically for the system. The controller efficiency and sensitivity has been checked by MATLAB-Simulink software. Novelty: comparative study for mathematically obtained reference values and neural controller based results are proposed in this paper. The results are validated with the help of Matlab produces tables and figures. Keywords: BLDC Motor; speed control; neural network controller

Intelligent mobile robots move on uncertain grounds, thus requiring good navigation strategies for things like path tracking and obstacle avoidance. This research uses an Omni-drive mobile robot to autonomously approach given objectives in different situations encountered in static and dynamic environments. The paper compares two distinct controllers – fuzzy logic controller and neural network controller- that lead the mobile robot towards its destination without hitting obstacles. These are responsible for adjusting the linear and angular velocities of a mobile robot which makes adaptive navigation possible during real-time. The experimental results have depicted the adaptability of each controller as well as its efficiency especially when dealing with uncertainties involved with the mobile robot navigation system. By systematically evaluating and contrasting them, this study brings out the best performance between Fuzzy Logic and Neural Network Controllers regarding enhancing the autonomy and robustness of Mobile Robots. This research helps to advance knowledge in autonomous systems for practical applications, which will give rise to more efficient navigational techniques for mobile robots; thus, efficient systems that are autonomous become more reliable today. The results show that these controllers are effective in safely steering the robot from its starting point to a specified destination without hitting obstacles.

This paper proposes a new technique for energy managing in hybrid power systems. The proposed management system is designed to manage the power flow between the hybrid power system in order to satisfy the load requirements based on artificial neural network (ANN) and fuzzy logic controllers. The neural network controller is employed to achieve the maximum power point tracking (MPPT) for different types of sources like Photovoltaic(PV), wind energy and Fuel cell at DC-link.The developed fuzzy logic controller (FLC)-based MPPT method and ANN based method was assessed using a hybrid system comprised PV panels, wind turbine (WT) and Fuel Cell with DC–DC converters. The dynamic behaviour of the proposed model is examined under different operating conditions. The analysis of the proposed hybrid system provides higher power as compared to PV, WT and FC systems at different loads. This research is utilized for the stand-alone system as well as a grid. ANN gives a better performs compared to the Fuzzy in the MPPT method for the PV panels, wind turbine (WT) and Fuel Cell with DC–DC converters for the DC loads. The result analysis of the Fuzzy and the ANN are simulated in the MATLAB/Simulink.

This paper presents the implementation of adaptive fuzzy neural network controller (FNNC) for accurate speed control of a permanent magnet synchronous motor (PMSM). FNNC includes neural network controller (NC) and fuzzy logic controller (FC). It combines the capability of fuzzy reasoning in handling uncertain information and the capability of neural network in learning from processes. The initial weights and biases of the artificial neural network (ANN) are obtained by offline training method. Using the output of the fuzzy controller (FC), online training is carried out to update the weights and biases of the ANN. Several results of simulation are provided to demonstrate the effectiveness of the proposed FNNC under the occurrence of parameter variations and external disturbance.KeywordsFuzzy ControllerFuzzy Logic ControllerProportional IntegralReference SpeedFuzzy Logic RuleThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The present paper investigates an advanced control system of the wind energy conversion using an Artificial Neural Network (ANN) controller, which is developed in two modes: the offline mode, required for testing different sets of neural network parameters to find the optimal neural network controller (structure, activation function, and training algorithm), and the online mode where the optimal ANN controller is used in the five-phase PMSG based wind turbine system. The required data to generate the ANN model is obtained from a conventional PI controller. The controller inputs are the error between reference and actual speed (e), and the error change (de = e(k)-e(k-1)). As for the ANN output, it is the decreasing or increasing electromagnetic torque (dTe*). The studied neural network controller has been tested and validated using different wind speed values. Results and analysis are presented, and the contribution has been demonstrated.

This article analyses the dynamic behaviour of a beam structure containing multiple transverse cracks using neural network controller. The first three natural frequencies and mode shapes have been calculated using theoretical, finite-element, and experimental analysis for the cracked and un-cracked beam. Comparisons of the results among theoretical, finite-element, and experimental analysis have also been presented. The calculated vibration signatures were used to train the feed-forward multi-layered neural network controller with back-propagation technique for the prediction of cracks. Relative crack locations and relative crack depths are the output of the neural controller. Results obtained from the various analyses are validated using the developed experimental set-up. Results from neural controller have been presented for comparison with the output from theoretical, finite-element, and experimental analysis. From the evaluation of the performance of the neural network controller it is observed that the developed method can be used as a crack diagnostic tool in the domain of dynamically vibrating structures.

This study aims to evaluate the behavior of neural network control method for digitally controlled dc-dc converters when the circuit parameter condition varies from which is in the training of the neural network. Learning based methods, which include the neural network control, are constructed and tuned using the training data to control given plants. They can provide the effective control since they are tuned as the dedicated one, however, they have difficulties when the parameters of components in the plant are changed from original ones, which are used to obtain the training data. Therefore, they may lose the effectivity in control if the the parameters in plant are changed. In the case of controlling dc-dc converters, the circuit parameter condition such as capacitance, reactor and so forth, are varied in the case that the specification of the circuit design is changed. Moreover they are changed by the environmental condition such as temperature, aging degradation and so forth. In this study, we study the effectivity of neural network control method for dc-dc converters when the circuit parameters are changed from the ones which are used in the training. When the circuit parameters are changed from original one, we evaluate that whether the neural network can control without any tuning, such as re-training of the neural network. From evaluation results, we confirm that the neural network control can work effectively even in such situation and it reveals that the neural network control has the robustness against the change of the circuit parameter condition.

In this Chapter we provide the reader with some background material on neural control strategies and we discuss neural optimal control in more detail. The Chapter is organized as follows. In Section 4.1 the basic principles of existing methods in neural control are presented, including direct and indirect adaptive control, reinforcement learning, neural optimal control, internal model control and model predictive control. In Section 4.2 neural optimal control is discussed with respect to classical theory of nonlinear optimal control. The emphasis in this Section is on the formulation of control problems as parametric optimization problems, where static and dynamic nonlinear controllers are parametrized by multilayer perceptrons. Furthermore an efficient way for including a priori results from linear control theory into the neural controller is highlighted. The latter is illustrated on the examples of swinging up an inverted and double inverted pendulum system. New contributions are stated in Section 4.2.6.

DC-DC converters are often used in electrical systems to keep the output voltage constant. Boost converters are utilized for a variety of purposes, including regenerative braking of direct current motors, portable device applications, and regulated power supplies. Buck converters, on the other hand, are used in sophisticated communications, data communication, and self-regulating power supplies. One of the most crucial elements in the power conversion process is managing the DC-DC converters. This study aims to find out which nonlinear controller, fuzzy or neural network, works best when the output load or boost converter characteristics change. So, in this, we create a boost converter controller using a fuzzy and neural network. The fuzzy controller for this study's converter employs a standard set of rules, whereas the neural network controller employs two hidden layer networks. MATLAB software is then used to reconstruct both controllers. The simulation results show that the fuzzy logic controller has a very long transient and settling period with no steady-state error in both transient and steady-state situations. On the other hand, the neural network controller has a short transient and settling period with a steady-state error.

This paper presents a comparative performance analysis of the control strategies for a two link planar robotic manipulator. In this work PD/PID computed torque control, filtered error approximation based adaptive control, and neural network (NN) control methods are considered for comparative performance analysis. The comparative analysis of the results demonstrates that the neural network controller gives the most effective results and confirms that the dynamical system can track the desired trajectory. The computer simulation is carried out using MATLAB and satisfactory results are presented which demonstrate the tracking capability and the effectiveness of the proposed control scheme.

DC-DC converters can be found in almost every power electronics device. To improve the efficiency and controller response of a DC-DC converter to dynamical system changes, neural network has been chosen as an alternative to classic methods. However, no prior work has been done in the neural network approach for control of a PSFB (phase-shifted full-bridge) converter yet. In this research, a multi-layer feedforward neural network controller is proposed. The neural network based controller has the advantage of adaptive learning ability, and can work under the situation when the input voltage and load current fluctuate. Levenberg-Marquardt back-propagation training algorithm is used in computer simulation. The neural controller is then implemented on hardware using a DSP (digital signal processor). Satisfactory experimental results are obtained.

This paper proposes neural network controllers using a reference model and investigates their characteristics. Since the neural network compensates the incorrectness of the plant such as nonlinearity and uncertainty against the reference model, a conventional controller can be designed based on the known dynamics of the reference model. The neural network controller can be classified into three types in terms of the connection between the neural network controller and the conventional controller. In the D-type controller, the output from the conventional controller inputs to the neural network and the reference model in parallel. In the Y-type controller, the output from the conventional controller inputs to the neural network, the plant and the reference model in parallel. The neural network of both controllers learns the combination of the inverse characteristics of the plant and the forward characteristics of the reference model. In the I-type controller, the output from the conventional controller inputs to the reference model whose output inputs to the neural network. The neural network leams the inverse characteristics of the plant. Simulation results using nonlinear plants show the achievement of the neural controllers and confirm their effectiveness for controlling the nonlinear plants.

The paper presents a neural network controller for trajectory tracking for a two D.O.F. SCARA robot mechanism. Two types of neural network controllers were built: a joint space neural network controller and a task space neural network controller. The two controllers were compared with a computed torque method controller in a joint as well as task space. The four controllers were tested on a real robot mechanism. The Lyapunov theory for deriving the adaptation law, or the learning algorithm of neural networks, was used to prove the robot system stability with a neural network controller. >