Direct Inverse Control Research Articles

Adaptive Neural Control for Hysteretic Nonlinear Systems With Hysteresis Neural Direct Inverse Compensator and Its Application.

Aiming at high precision control for a class of hysteretic nonlinear systems, a new hysteresis direct inverse compensator-based adaptive output feedback control scheme is designed in this article. First, a novel long short-term memory neural network (LSTMNN)-based hysteresis inverse compensator is established to compensate the asymmetric hysteresis nonlinearity, where the LSTMNN is used as the prediction mechanism for model operator weights, rather than the overall mapping of hysteresis input and output. Second, by designing the modified high-gain K-Filter states observer and the error transformed function, the unmeasurable states are estimated with arbitrarily small estimation error and the prespecified tracking performance is achieved. Lastly, the biconical dielectric elastomer actuator (DEA) motion platform is constructed. Then, the effectiveness of the proposed LSTMNN-based hysteresis inverse compensator and control scheme are verified on the experimental platform. The experimental results illustrate the effectiveness and advantages of proposed control scheme.

Modeling and controlling heat transfer in chambers: A comparative study of classical and intelligent approaches

This paper introduces nonlinear approaches which include neural networks and ANFIS to identify and control heat transfer within a chamber. Initially, traditional linear models are obtained using transfer functions with delays through Matlab identification tools. However, this traditional linear model failed to faithfully represent the system when the input was changed. This outcome was expected since linear models are reliable only within specific operational ranges. To create a novel model that is applicable across the entire state space, two alternative identification methods, utilizing neural networks and an adaptive neuro-fuzzy inference system were introduced. After testing them with input data not used during the training, the models were compared and all of them showed satisfiying results. In the continuation of the research, control techniques based on these techniques were presented. After assigning an arbitrary temperature as a reference signal, inverse models were made and four controllers in direct inverse control sheme were compared: three feedforward neural networks with different numbers of neurons in the hidden layer and the adaptive neuro-fuzzy inference controller. The results and possible improvements are discussed in the conclusion.

An efficient vibration suppression technology of piezoelectric cantilever beam based on the NARX neural network

Low-frequency vibration is the core problem that hinders high-precision equipment’s positioning accuracy and control accuracy. However, the response of a nonlinear electrical-mechanical coupling system is nonlinear and quite complicated under the ambient random vibrating environment. This paper presents a vibration suppression method through NARX (Nonlinear autoregressive with external input) neural network and its controller. The experiment platform is designed, and its dynamic model is obtained through system identification. The Neural network direct dynamic inverse control system is established, and the controller is designed. Both the simulation and experimental results show that the NARX identification and the controller can effectively achieve vibration suppression, and the experiment’s highest vibration rejection ratio is 90.8%. The vibration suppression technology can be extensively applied to low-frequency vibration systems, such as aerospace equipment and high-precision equipment.

Online trained controller for Electrical Submersible Pumps in liquid–gas flow

The Electrical Submersible Pump (ESP) is an artificial lift method widely used in the oil industry. More than 150,000 wells worldwide are equipped with ESPs to produce cost-effective oil rates. An ESP unit usually includes an electric motor, a protective element, a gas separator (optional), and a multistage centrifugal pump. When free gas produced in the well reaches the pump stages, problems such surging and gas-locking can occur, leading to unexpected shutdowns in the oil production. In this work, a control based on Artificial Neural Networks (ANN) with online training is proposed as a solution to reduce these unplanned shutdowns. The proposed controller was designed using the direct inverse control method and the data obtained from an ESP working with a liquid–gas flow. The results of this work show that the controller can drive the operation of the ESP away from the regions where surging and gas-locking would occur. Since the controller is an online trained ANN, it was able to control the operation of two different pumps, namely the model P100 pump, the model P47 pump, and the model P23 pump.

Neuronskih mreža u upravljačkoj šemi sa unutrašnjim modelom

In this research, control of the Direct Current motor is accomplished using a neuro controller in the Internal Model Control scheme. Two Feed Forward Neural Networks are trained using historical input-output data. The first neural network is trained to identify the object's dynamic behavior, and that model is used as an internal model in the control scheme. The second neural network is trained to obtain an inverse model of the object, which is applied as a neuro controller. Experiment is conducted on the real direct current motor in laboratory conditions. Obtained results are compared to those achieved by implementing the Direct Inverse Control method with the same neuro controller. It was demonstrated that the proposed control method is simple to implement and the system robustness is achieved, which is a great benefit, aside from the fact that no mathematical model of the system is necessary to synthesize the controller of the real object.

Landslide evolution state prediction and down-level control based on multi-task learning

In this paper, multi-task learning is introduced into the study of landslide evolution state prediction and control. Firstly, we define two landslide evolution states and propose a method of landslide evolution state level classification prediction. Specifically, we use Gaussian mixture model (GMM) to reconstruct labeled data sets and establish a landslide evolution state level prediction model based on Multi-Task Learning-Stacked Long-Short Term Memory (MTL-SLSTM), and then use task weight rules to design multi-task losses for network training. The parallel multi-step prediction of the evolution state is achieved by the above works. Secondly, considering the spatial–temporal correlation of different monitoring points on the same landslide, we analyze spatial–temporal data to construct spatial–temporal features, and design a multi-task correlation learning mechanism combined multi-task weight learning and multi-task relationship learning methods to construct spatial–temporal relation. The landslide multi-point prediction model based on Multi-Task Correlation Learning-Stacked Long-Short Term Memory (MTCL-SLSTM) achieves single-step prediction of the evolution state of multiple monitoring points with high accuracy. Finally, according to the idea of neural direct inverse model control, we propose down-level control method based on the prediction of the landslide evolution state. We build an interval prediction network based on bootstrap method and model selection strategies, and then a safe rainfall interval predictor is trained offline. Moreover, we design an online landslide down-level control process combined the landslide evolution state level predictor, which realizes single-step control of single-point of the dangerous landslide. Furthermore, the effectiveness of the proposed method is verified on Baishuihe landslide.

Attitude and Altitude Control of Quadcopter Maneuvers using Neural Network–Based Direct Inverse Control

Attitude and Altitude Control of Quadcopter Maneuvers using Neural Network–Based Direct Inverse Control

Neurocontrol methods in the context of development of technical solutions for transition to unmanned navigation

The paper considers the features and prospects of using neurocontrol methods in the context of development of technical solutions for transition to unmanned merchant vessels. The paper suggests a non-iterative training based artificial neural network (ANN), which is based on the principles of “direct inverse control” to control the speed and motion of unmanned surface vessels. The model is identified, and the structure of an artificial neural network and the diagram of the automatic control system (ACS) of an unmanned vessel (UV) are considered on the example of an electric propulsion vessel. A series of computational experiments is carried out to obtain a sufficiently complete training sample. and the control law is presented. The principle of the control system for an unmanned vessel is considered based on a neural network. At the next stage of the study, focus is on the synthesis of the optimal control system for UV navigation. The problem of the fastest motion of a third-order control object from one point (with any initial speed) to another (at the end point the vessel stops and the speed is zero) is considered. Based on the results of a series of experiments with the UV model, the controller parameters that provide the best indicators of control quality were set in the MATLAB Simulink environment.

DAIC Design for an Electro-Hydraulic Actuator via FASS-NLMS Algorithm

In this paper, it is proposed a new version of Normalized Least Mean Square (NLMS) algorithm, performed through a linguistic description based on the expert’s knowledge and implemented in a fuzzy rule base. In order to obtain a good trade-off between convergence speed and steady-state Mean Square Error (MSE) during the update of the estimate of the weights vector, it is used a Mamdani Fuzzy Inference System (MFIS) to adapt the step size of NLMS algorithm, whose inputs are the squared error and the normalized time instant. The validation of the proposed optimization algorithm was performed in Direct Adaptive Inverse Control (DAIC) design and applied to a non-minimum phase plant referring to an Electro-Hydraulic Actuator (EHA) system, shuch that a disturbance signal was added to the control signal.

Coordinated optimization control of shield machine based on dynamic fuzzy neural network direct inverse control

During shield machine tunneling, the earth pressure in the sealed cabin must be kept balanced to ensure construction safety. As there is a strong nonlinear coupling relationship among the tunneling parameters, it is difficult to control the balance between the amount of soil entered and the amount discharged in the sealed cabin. So, the control effect of excavation face stability is poor. For this purpose, a coordinated optimization control method of shield machine based on dynamic fuzzy neural network (D-FNN) direct inverse control is proposed. The cutter head torque, advance speed, thrust, screw conveyor speed and earth pressure difference in the sealed cabin are selected as inputs, and the D-FNN control model of the control parameters is established, whose output are screw conveyor speed and advance speed at the next moment. The error reduction rate method is introduced to trim and identify the network structure to optimize the control model. On this basis, an optimal control system for earth pressure balance (EPB) of shield machine is established based on the direct inverse control method. The simulation results show that the method can optimize the control parameters coordinately according to the changes of the construction environment, effectively reduce the earth pressure fluctuations during shield tunneling, and can better control the stability of the excavation surface.

A method for vehicle speed tracking by controlling driving robot

Using a driving robot instead of a human driver to carry out a vehicle emission durability test can effectively improve the accuracy of test. In order to accurately control the speed tracking of driving robot under different working conditions, a speed tracking method of driving robot based on dynamic fuzzy neural network (DFNN) direct inverse control is proposed, which considers the self-learning of vehicle longitudinal performance. Firstly, the kinematics and dynamics models of the driving robot’s mechanical legs are established. Moreover, in order to coordinately control the multi-legs of the driving robot to track the vehicle speed, the longitudinal performance model of the controlled vehicle is established by using performance self-learning data and neural network algorithm. Then, to accurately control the movement of the mechanical leg, a direct inverse controller based on DFNN is designed. The output of direct inverse controller is dynamically compensated by closed-loop control of braking force and throttle opening. Finally, the speed tracking experiments and simulations are carried out by human driver, proportional-integral-derivative (PID) control, fuzzy control and the proposed method under different working conditions. The results show that the proposed method does not have the same large prediction error as human driver. At the same time, it can track the speed quickly in different switching conditions. The fluctuation of the tracking speed is small, and the tracking error remains within ±1km/h. The proposed method avoids the design of complex control law based on model and can coordinately control the multi-legs to complete the tracking of the target speed.

High-Precision Tracking Control of a Soft Dielectric Elastomer Actuator With Inverse Viscoelastic Hysteresis Compensation

In this paper, we present a new control approach for high-precision tracking control of a soft dielectric elastomer actuator (DEA) with inverse viscoelastic hysteresis compensation. To this end, we first investigate the viscoelastic response of the DEA and divide it into transition region and stable region. Then, the viscoelastic response is characterized by creep and hysteresis effects according to the different features of the two regions. Finally, a two-level tracking control approach is developed as follows: a direct inverse hysteresis compensation controller with a phenomenological hysteresis model is designed for the viscoelastic hysteresis description and compensation, and a conventional proportional-integral feedback controller is combined to compensate for the model uncertainty and creep effect. To verify the effectiveness of the developed tracking control approach, several experiments are conducted with various reference sinusoidal trajectories. Experimental results show that: when the frequency of the trajectory is within the range of 0.1 to 1 Hz, the maximum tracking error and the root-mean-square error decrease from 40.63% to 3.95% and 28.38% to 1.86%, respectively. This paper is the first attempt to achieve high-precision tracking control of soft DEAs by combining a phenomenological-model feedforward compensator and a feedback control law for the viscoelastic compensation, which may accelerate the practical applications of DEAs to soft robots.

Ship roll damping via direct inverse neural network control system

Undesired roll motions of surface vessels under sea way have negative influence on passengers, crew members, equipment and cargo. Damping roll motions is a complex problem due to unpredictable and irregular nature of marine environmental disturbances that induce roll motions. There are several stabilizing systems regarding roll motions such as gyrostabilizers, anti-roll tanks, active fins and rudder roll stabilizers. Among all types of roll stabilizers or roll damping systems active fins practically represents the most effective roll damping technique and yet the most commonly used roll stabilizer. Assuming that the longitudinal location of fins corresponds to the vessel center of gravity a one degree of freedom nonlinear model can be used to identify ship dynamics. In this paper the performance and robustness of a proposed control system based on neural network approach applied to active fins that takes into account roll response spectral characteristics transitions due to not only varying sea state but also changing sailing conditions is assessed. The suggested control system achieves robust performance with adequate roll damping effect that ranges from 50 to 65% roll reduction according to severity of weather conditions and sailing status.

Neural Network Control System of UAV Altitude Dynamics and Its Comparison with the PID Control System

This article proposes a comparative method to assess the performance of artificial neural network’s direct inverse control (DIC-ANN) with the PID control system. The comparison served as an analysis tool to assess the advantages of DIC-ANN over conventional control method for a UAV attitude controller. The development of ANN method for UAV control purposes arises due to the limitations of the conventional control method, which is the mathematical based model, involving complex expression, and most of them are difficult to be solved directly into analytic solution. Although the linearization simplified the solving process for such mathematical based model, omitting the nonlinear and the coupling terms is unsuitable for the dynamics of the multirotor vehicle. Thus, the DIC-ANN perform learning mechanism to overcome the limitation of PID tuning. Therefore, the proposed comparative method is developed to obtain conclusive results of DIC-ANN advantages over the linear method in UAV attitude control. Better achievement in the altitude dynamics was attained by the DIC-ANN compared to PID control method.

An RBFNN‐Based Direct Inverse Controller for PMSM with Disturbances

Considering the system uncertainties, such as parameter changes, modeling error, and external uncertainties, a radial basis function neural network (RBFNN) controller using the direct inverse method with the satisfactory stability for improving universal function approximation ability, convergence, and disturbance attenuation capability is advanced in this paper. The weight adaptation rule of the RBFNN is obtained online by Lyapunov stability analysis method to guarantee the identification and tracking performances. The simulation example for the position tracking control of PMSM is studied to illustrate the effectiveness and the applicability of the proposed RBFNN‐based direct inverse control method.

Attitude and Altitude Control of a Quadcopter Using Neural Network Based Direct Inverse Control Scheme

Attitude and Altitude Control of a Quadcopter Using Neural Network Based Direct Inverse Control Scheme

An Inverse Kinematics Solution Based on CA-CMAC and ILC for Trajectory Tracking of Redundant DOF Manipulator

The composite control strategy of CA-CMAC and ILC is adopted to solve the inverse kinematic problem of the redundant DOF manipulator during its real-time and high-precision tracking on the three-dimensional space target trajectory. A direct inverse model control strategy is adopted, in which CA-CMAC takes the current joint angles and the desired position increment of the manipulator as the input, and estimates the expected joint angle increments of the manipulator using the system history control experience. Then the estimated joint angles are taken as the initial value of the ILC module by which the control effect is improved iteratively. Based on the MATLAB, the tracking controls of linear and circular space target trajectories were simulated respectively. The results show that CA-CAMC and ILC composite control has better tracking precision and stability than CMAC control, while keeping the joint angles of the manipulator continuous and smooth during trajectory tracking.

Stability and Convergence Analysis of Direct Adaptive Inverse Control

In adaptive inverse control (AIC), adaptive inverse of the plant is used as a feed-forward controller. Majority of AIC schemes estimate controller parameters using the indirect method. Direct adaptive inverse control (DAIC) alleviates the adhocism in adaptive loop. In this paper, we discuss the stability and convergence of DAIC algorithm. The computer simulation results are presented to demonstrate the performance of the DAIC. Laboratory scale experimental results are included in the paper to study the efficiency of DAIC for physical plants.

Neural Network Based Model Predictive Control of Batch Extractive Distillation Process for Improving Purity of Acetone

In a pharmaceutical industry, batch extractive distillation (BED), a combination process between extraction and distillation processes, has been widely implemented to separate waste solvent mixture of acetone-methanol because of minimum-boiling azeotrope properties. Normally, water is used as solvent and semi-continues mode is proposed to improve purity of acetone. The solvent is charged into the BED column until the purity of a desired product is achieved. After the total reflux start-up period is ended, a dynamic optimization strategy is applied to determine an acetone distillate composition profile maximizing the weight of the distillate product (acetone). The acetone distillate composition profile is used as the set point of neural network model-based controllers: the neural network direct inverse model control (NNDIC) and neural network based model predictive control (NNMPC) in order to provide the acetone composition with the purity of 94.0% by mole within 9.5 hours. It has been found that although both NNDIC and proportional integral derivative (PID) control can maintain the distillate purity on its specification for the set point tracking and in presence of plant uncertainties, the NNMPC provides much more satisfactory control performance and gives the smoothest controller action without any fluctuation when compared to the NNDIC and PID.

Particle Swarm Optimization-Based Direct Inverse Control for Controlling the Power Level of the Indonesian Multipurpose Reactor

A neural network-direct inverse control (NN-DIC) has been simulated to automatically control the power level of nuclear reactors. This method has been tested on an Indonesian pool type multipurpose reactor, namely, Reaktor Serba Guna-GA Siwabessy (RSG-GAS). The result confirmed that this method still cannot minimize errors and shorten the learning process time. A new method is therefore needed which will improve the performance of the DIC. The objective of this study is to develop a particle swarm optimization-based direct inverse control (PSO-DIC) to overcome the weaknesses of the NN-DIC. In the proposed PSO-DIC, the PSO algorithm is integrated into the DIC technique to train the weights of the DIC controller. This integration is able to accelerate the learning process. To improve the performance of the system identification, a backpropagation (BP) algorithm is introduced into the PSO algorithm. To show the feasibility and effectiveness of this proposed PSO-DIC technique, a case study on power level control of RSG-GAS is performed. The simulation results confirm that the PSO-DIC has better performance than NN-DIC. The new developed PSO-DIC has smaller steady-state error and less overshoot and oscillation.