Neural network-based control of balanced robotic manipulators with joint flexibility

This paper includes an application of layered artificial neural network controller to study the load-frequency control problem in a power system. The control scheme guarantees that steady state error of frequencies and inadvertent interchange of tie-lines are maintained in a given tolerance limitation. The proposed control has been designed for a three-area interconnected power system such that two areas include steam turbines and the other area includes a hydro turbine. Only one artificial neural network (ANN) controller, which controls the inputs of each area in the power system together, is considered. In the study, a back propagation-through-time algorithm is used as a neural network learning rule. The performance of the power system is simulated by using a conventional integral controller and ANN controller, separately. By comparing the results for both cases, the performance of an ANN controller is better than conventional controllers.

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 paper highlights, neural network (NN) based adaptive control using backstepping control technique is proposed for robot manipulator trajectory tracking. Firstly, the vector of current is considered as the control variable for robot manipulator mechanical subsystem by using the adaptive update algorithm of NN and an enclosed control input for the desired vector of current is constructed. So that, the goal of trajectory tracking of robot manipulator is achieved. Secondly, the voltage commands are constructed in order to control the joint currents to follow the anticipated value by using the NN controller in order to manipulate the dynamics of DC motor. Simplicity of control law is achieved by using proposed control technique along with low computational cost. In addition, robot manipulator and its actuator dynamics does not require the mathematical representation of model. The weight values of NN's and robotic manipulator parameters are adaptively updated. The efficiency and usefulness of proposed scheme on 2-DOF robot manipulator is analyzed by using the running mean error. The results depicts that the proposed model out perform than the conventional PD controller in terms of enhanced robotic manipulator trajectory tracking.

This paper presents investigations into the development of neural network (NN) control of a two-link flexible robot manipulator with load 0.5 kg. A dynamic model of the system is derived using a combined Euler-Lagrange and assumed mode methods (AMM). The controller reduces nonlinearities problem that can be efficiently solved using NN control. The system responses namely hub angular position and deflection responses and end-point acceleration at both links are obtained and analysed. The performances of the controllers are assessed in terms of the input tracking controller capability of the system. Moreover, the robustness of the NN control schemes is discussed. Finally, a comparative assessment of the control strategies is presented. The results show that NN controller performs give increasing profiles that compared with PID control.

Photovoltaic systems play a pivotal role in renewable energy initiatives. To enhance the efficiency of solar panels amid changing environmental conditions, effective Maximum Power Point Tracking (MPPT) is essential. This study introduces an innovative control approach based on an Artificial Neural Network (ANN) controller tailored for photovoltaic systems. The aim is to elevate the precision and adaptability of MPPT, thereby improving solar energy harvesting. This research integrated an ANN controller into a photovoltaic system in order dynamically optimize the operating point of solar panels in response to environmental changes. The performance of the ANN controller was compared with traditional MPPT approaches using simulation in Simulink/Matlab. The results of the simulation showed that the ANN controller performed better than the traditional MPPT techniques, highlighting the effectiveness of this method for dynamically changing solar panel performance. The ANN particularly demonstrates higher precision and adaptability when environmental conditions vary. The strategy consistently achieves and maintains the maximum power point, enhancing overall energy harvesting efficiency. The integration of an ANN controller marks a significant advance in solar energy control. The study highlights the superiority of the ANN controller through rigorous simulations, demonstrating increased accuracy and adaptability. This approach not only proves effective, but also has the potential to outperform other MPPT strategies in terms of stability and responsiveness

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.

Inverter's controller directly determines the grid-integration quality of power conversion systems. To address complex operating conditions, e.g. grid voltage distortion, parameter variations and weak grid scenarios, this paper proposes a lightweight artificial neural network (ANN) controller with strong adaptability. In implementation, the ANN controller generates control signals while simultaneously optimizing its weights in real time using the gradient descent algorithm. Distinctively, the weight gradients are directly calculated using the loss function and weights, which compared to the error backpropagation method, significantly reduces computational complexity, and therefore achieves the computational lightweight feature. In addition, the ANN phase-locked loop (ANN-PLL) is constructed to provide phase alignment for current reference while enabling fully ANNbased inverter control architecture. In principle, the proposed ANN controller relies neither on an offline training dataset nor on the system model, and achieves adaptive weights adjustment in real time with minimal computational effort. Through physical experiments, the proposed lightweight ANN controller was compared with the sliding mode controller and the model-based ANN controller, verifying its superior performance under complex operating conditions, such as grid voltage distortion, input voltage variation, current reference variation, filter parameter variation, and extremely weak grid (short circuit ratio=1.09).

The paper deals with the applications of artificial neural networks in the control of the DC drive. In the paper three control structures are discussed. The first control structure uses a conventional PI controller. The second structure uses a neural network predictive control. The last structure is a sensorless control of the DC drive using feedforward neural network. The DC drives were simulated in program Matlab with Simulink toolbox. The main goal was to find the simplest neural network structures with minimum number of neurons, but simultaneously good control characteristics are required. Despite used neural networks, which are very simple, it was achieved satisfactory results.KeywordsArtificial Neural NetworkArtificial Neural Network ModelModel Predictive ControlDirect Torque ControlInduction Motor DriveThese 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.

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.

Real-time motion planning and control of multi-joint robot manipulators are studied using neural networks. The proposed neural network approach consists of two modules: one for real-time collision-free motion planning, the other for real-time fine control of the robot manipulators. The motion planning module is a biologically inspired, parallel connected, topologically organised neural network, where each neuron is characterised by a shunting equation, whose state space is the robot configuration joint space. This module is capable of planning a real-time optimal path for robot manipulators through the dynamic activity landscape of the neural network. The motion control module involves of a feedforward neural network together with a traditional PD feedback loop. It is capable of achieving real-time fine motion control of robot manipulators under significant uncertainties and without any prior knowledge of the robot dynamics. This module can quickly compensate sudden changes in the robot dynamics. The real-time fine control of robot manipulators is achieved through the online learning of the neural network. Both the motion planning and motion control modules are computationally efficient, and their global stabilities are proved using Lyapunov stability theory.

Learning from adaptive neural output feedback control of robot manipulators

Robotic manipulators are highly coupled multi-input multi-output (MIMO) nonlinear systems with uncertainties and highly time-varying dynamic capabilities. These characteristics make the trajectory control of a robotic manipulator very challenging. This paper presents the modeling and trajectory tracking control of a 3-DOF robotic manipulator using self-tuning fuzzy sliding mode controller (ST-FSMC). The stability of the system was investigated using the Lyapunov direct method. The controller was implemented using MATLAB/Simulink and its performance was evaluated. The simulation results show that the proposed controller removed chattering phenomena from the control effort and reduced the tracking error (average steady-state error) to 0.0036 rad. However, in the case of the conventional controllers, the average steady-state e rror increased to 0.0413 rad, 0.0044 rad, and 0.0053 rad for the Proportional-integral-derivative (PID) controller, Sliding mode controller (SMC), and Fuzzy sliding mode controller (FSMC), respectively. The conventional controllers (PID, SMC, and FSMC) were designed for the purpose of comparison with the proposed ST-FSMC. The simulation results show that the designed controller (ST-FSMC) has a superior tracking performance, is robust and is not sensitive to applied model parameter variations as compared to other conventional controllers.

This paper aims at improving the excitation control system for the doubly fed induction generator-based wind farm (WF). The proposed decentralized coordinated neural control is consisted of two controllers, i.e., a multiple model predictive controller (MPC) and an artificial neural network (ANN) controller. The MPC is based on the interaction measurement model and designed to guarantee the performance of system-wide optimization around the chosen operating points, while the ANN controller is used to calculate the weights of MPC controllers and trained to guarantee the control performance over the full operating region. The proposed control strategy allows to reduce the computational cost, and at the same time to take advantage of the distributed nature of the power system. Moreover, a simple, generic hybrid power system is used to demonstrate transient stability contributions. The problems of large-scale WF integration and its impact on transient stabilities of power systems are discussed through the eigenvalue analysis and time domain simulation. The results show that system damping and transient stability of hybrid power system are considerably improved.

In this paper, an artificial neural network (ANN) controller based position sensorless control of permanent magnet synchronous motor (PMSM) using Space vector modulation-Direct torque control (SVM-DTC) for variable speed drive has been presented. The SVM-DTC require the initial position of the rotor during the starting of the PMSM drive. The installation of the shaft-mounted position sensor requires additional space, assembly, wiring circuit, and is fragile component. The speed sensor-less control of PMSM enhances the performance of drives in harsh environments and reduces the overall cost of the drive and improve mechanical reliability. The speed estimation requires the knowledge of drive parameters, the model-based speed control technique is suitable for low and medium-speed motor drive applications without knowing the exact parameter of the PMSM drive. The Rotor Flux based Model Reference adaptive system (RF-MRAS) is used for a wide speed operation and estimates rotor angle in dynamic conditions. The presence of an integrator in the voltage model of RF-MRAS affects the low speed performance of the drive, therefore to improve the speed response at low speed, the ANN controller is used to replace the Proportional-Integral (PI) controller, which is employed in the adaptive model of the speed observer. The performance of the control scheme is simulated at variable speed and load conditions with the help of the OPAL-RT 4500 simulation platform.

This paper presents the five-level direct power control (DPC) using artificial neural networks (ANNs) controller of a doubly fed induction generator (DFIG) based wind power generation systems (WPGSs). The validity of the proposed strategies is verified by simulation tests of an induction generator. The harmonic distortion of stator current, reactive power, and stator active power are determined and compared to the above strategies. The five-level DPC strategy with ANN controller is shown to be able to minimize the reactive power, harmonic distortion of stator current, and stator active power ripples and to improve performance DPC control scheme. Â