Adaptive Inverse Control Research Articles

Level Control of Quadruple Tank System Based on Adaptive Inverse Evolutionary Neural Controller

This article proposes an adaptive inverse evolutionary neural (AIEN) controller for liquid level control of the quadruple tank system. Firstly, an inverse evolutionary neural model (IEN) that is utilized for offline identifying a dynamics of quadruple tank system, provides a feed-forward control signal from the reference liquid level. In which, the evolutionary neural model is a 3-layers neural network that is optimized by a hybrid method of modified differential evolution and backpropagation algorithm. Then, a hybrid feedforward and PID feedback control is realized to eliminate the steady-state error. Finally, to solve an uncertainty and disturbance characteristic, an adaptive law is proposed to adopt online in its operation. Simulation and real-time control experimental results demonstrated the feasibility and effectiveness of the proposed approach for the quadruple-tank system.

Adaptive inverse backlash boundary vibration control design for an Euler–Bernoulli beam system

In this paper, we address the vibration suppression problem of an Euler–Bernoulli beam structure suffering from the input backlash nonlinearity and the unknown disturbance. Considering the nonlinearity and discontinuity of the backlash with the uncertain parameter, an adaptive inverse backlash dynamics is proposed to handle the nonlinear characteristic and correct the mismatch of the backlash parameter. Further, an adaptive vibration control law is developed to improve the system performance via combining with the proposed inverse backlash dynamics. Meanwhile, a disturbance observer is designed to reject the external boundary disturbance, and we bring a logarithmic barrier function in control design to ensure the end-point displacement of this system be constrained in the desired limitation. At last, we analyze the stability of this flexible system and the effectiveness of the proposed control method with simulation experiments.

Event-triggered Adaptive Dynamic Surface Output Feedback Inverse Control for a Class of Hysteresis Nonlinear Systems

Aiming at the difficult problem of the nonlinear system with hysteresis, the Prandtl-Ishlinskii model is used to describe the system. A neural network-based output feedback adaptive dynamic surface inverse control (DSIC) scheme is proposed. Firstly, in order to overcome the hysteresis compensation error, an auxiliary design parameter is introduced into the observer to construct an improved high-gain k-order filter state observer to accurately estimate the unmeasurable state. Then, the inverse of the asymmetric displacement matrix of the Prandtl-Ishlinskii model is used as feedforward compensation. the unknown density function with different thresholds is not estimated, the analysis compensation error is obtained, the over-parameterization problem is effectively avoided. At the same time, the event trigger mechanism is introduced in the controller design process to discretize the continuous control signal under the condition of event triggering to improve the computational efficiency of the controller. Finally, the simulation is verified, the results shows the control scheme has higher control precision and faster control speed.

Adaptive Inverse Controller Design Based on the Fuzzy C-Regression Model (FCRM) and Back Propagation (BP) Algorithm

Establishing an accurate inverse model is a key problem in the design of adaptive inverse controllers. Most real objects have nonlinear characteristics, so mathematical expression of an inverse model cannot be obtained in most situation. A Takagi–Sugeno(T-S)fuzzy model can approximate real objects with high precision, and is often applied in the modeling of nonlinear systems. Since the consequent parameters of T-S fuzzy models are linear expressions, this paper firstly uses a fuzzy c-regression model (FCRM) clustering algorithm to establish inverse fuzzy model. As the least mean square (LMS) algorithm is only used to adjust consequent parameters of the T-S fuzzy model in the process of parameter adjustment, the premise parameters are fixed and unchanged in the process of adjustment. In this paper, the back propagation (BP) algorithm is applied to adjust the premise and consequent parameters of the T-S fuzzy model, simultaneously online. The simulation results show that the error between the system output controlled by proposed adaptive inverse controller and the desired output is smaller, also the system stability can be maintained when the system output has disturbances.

Adaptive fuzzy optimal control for a class of active suspension systems with full‐state constraints

In this study, an adaptive fuzzy inverse optimal control problem is investigated for a class of vehicle active suspension systems. Since active suspension systems have dynamic characteristics of complexities and spring non-linearities, the fuzzy logic systems are utilised to learn the unknown non-linear dynamics. In addition, there exist the constraints of the displacements of the sprung and unsprung masses, vertical vibration speeds, and current intensity in the considered suspension system, therefore, the Barrier Lyapunov functions are introduced into the control design to ensure that the full-state constraints are not overstepped. The inverse optimal control method is adopted by constructing an auxiliary system, which circumvents the assignment of solving a Hamilton–Jacobi–Bellman equation and brings about an inverse optimal controller associated with a meaningful objective functional. Based on Lyapunov stability theory and backstepping recursive design algorithm, a fuzzy adaptive optimal control scheme is developed. It is proved that the proposed control scheme not only guarantees that the vertical vibration of the vehicle is stabilised by the electromagnetic actuator but also achieves the goal of inverse optimality with regard to the cost functional. Finally, the simulation studies check the validity of the presented control strategy.

Rapid Automatic Slung Load Operations with Unmanned Helicopters

Slung load operations often involve following conservative flight paths to prevent unwanted swing of the load. However, doing so often limits the maneuvering capability of the system and increases the time to complete trajectories. This work investigates methods to rapidly move a slung load with a helicopter with the intent to deliver it quickly and precisely to a fixed point. Inspired by prior work utilizing differential flatness, which is a property of certain systems that allow for advantageous controllers to be developed for nonlinear systems, the controller developed does not require instrumentation of the load or the ability to estimate the state of the load but does not preclude it. A feedforward and feedback controller was developed that modifies the helicopter commands based on the desired load path. The controller is simplified by neglecting selected higher order terms and modeling the load as a point-mass. An adaptive dynamic inversion controller is used to control the helicopter. A fixed downward-facing camera is used to provide estimates of load position for the feedback controller. Simulation and flight-test results are shown using a Yamaha R-MAX helicopter to validate the proposed method.

Adaptive Fuzzy Inverse Optimal Control for Uncertain Strict-Feedback Nonlinear Systems

This article first investigates the adaptive fuzzy inverse optimal control design problem for a class of uncertain strict-feedback nonlinear systems. Fuzzy logic systems are utilized to identify the unknown nonlinear dynamics, and then, an equivalent system and an auxiliary system are established. Based on the auxiliary system and using backstepping recursive design algorithm, an adaptive fuzzy inverse optimal scheme, associating with a meaningful objective functional, is developed. It is proved that the presented adaptive fuzzy inverse optimal control scheme can guarantee that the considered system is input-to-state stabilizable and also achieves the goal of inverse optimality with respect to the cost functional. Finally, the simulation studies and comparisons via two examples are provided to confirm the validity of the developed control strategy.

Adaptive novel MSGA-RBF neurocontrol for piezo-ceramic actuator suffering rate-dependent hysteresis

Piezo-ceramic actuators are widely used in micro-electro-mechanical systems. An adaptive inverse neurocontrol design is proposed for positioning control of the piezo-ceramic actuator (PA). Piezo-ceramic actuators exhibit rate-dependent hysteresis which changes its hysteretic behavior when the rate or the frequency of its driving signal varies. Radial basis function neural network (RBFNN) is utilized to model the input/output relation of the PA with rate-dependent hysteresis to make it work as an accurate hysteretic model for positioning control. And an adaptive inverse nonlinear controller is introduced for the rate-dependent hysteresis compensation of PA. A novel membrane structure genetic algorithm (MSGA) is proposed for the adaptive inverse nonlinear neurocontrol design of PA. Compared with the classical genetic algorithm, the experimental results illustrate the performance of the proposed control system as well as the efficiency of adaptive membrane structure genetic algorithm for positioning control of PA system.

Permanent Magnet Synchronous Motor Speed Control System Based on Adaptive Inverse Control Method

The classical feedback control method for the permanent-magnet synchronous motor cannot fulfill the dynamic requirement and anti-interference requirement at the same time. In this paper, an adaptive inverse control method with disturbance elimination is proposed based on the vector control including speed and current loops. It avoids the system instability and the anti-disturbance performance of the system can be enhanced as well. Firstly, the speed loop adopts the adaptive inverse controller possessing a feed forward control structure, and a normalized least-mean square filtering algorithm accelerates the speed error convergence. The inverse model of the approximately linearized system is obtained by modifying the weighting factor online. Secondly, in order to suppress and eliminate the influence of motor parameter perturbation and external disturbance on the control system, an extended state observer is designed to observe and compensate the disturbance of the system. Thus, the dynamic performance and anti-disturbance performance of the system can be improved simultaneously. Finally, the effectiveness of the proposed method is verified by experiment and simulation.

Adaptive inverse control of chatter vibrations in internal turning operations

Abstract In this article, adaptive inverse control of chatter vibrations in internal turning process is addressed. The active boring bar is composed of a slender steel cutting tool, an electrodynamic shaker as controllable actuator and an IEPE accelerometer as feedback sensor. The SISO control system actuates in the direction normal to the cut surface. A novel adaptive inverse control algorithm is presented in this paper. This algorithm is mainly used in the feed-forward Active Noise Control (ANC) applications and is known as the Filtered-x Normalized Least Mean Square (FxNLMS). In order to extend the application of adaptive inverse control algorithms in the field of Active Vibration Control (AVC), the FxNLMS algorithm with feedback architecture is suggested for the specific problem of chatter suppression in internal turning operations. The performance of developed adaptive feedback controller is experimentally verified during the internal turning of Aluminum alloy 6063-T6. The value of critical limiting depth of cut is anticipated to be nearly 0.2 [mm] for the slender boring bar. However, the stable cutting process is conducted by the active boring bar in the presence of adaptive controller up to depth of cut 2 [mm]. That is, the boundary of stability is enhanced by at least 10 folds. It has been observed that the amplitude of chatter vibrations is efficiently suppressed adjacent to the fundamental resonance peak of the active boring bar. In addition, the feedback FxNLMS controller effectively attenuates the boring bar’s vibration by at least 70 dBs. Moreover, the periodic chatter marks are eliminated from the surface texture of workpiece and the roughness of cut surface is remarkably improved. The obtained results suggest that the practical application of adaptive inverse control algorithms for chatter rejection can be extended to other machining processes as well.

Robustness of adaptive inverse control in solving internal and external disturbance uncertainties for a class of non-linear systems

In this paper, adaptive inverse control is developed to deal with the problem about the control performance of non-linear systems, which are affected by the uncertainty of the parameters and external disturbance. An adaptive inverse controller, which is used as a feed forward controller, is designed to realise real-time tracking of the system output and to enhance the robustness of the system under parameter perturbation. A noise and disturbance canceller is used to eliminate the influence of external disturbance and measurement noise for open loop system output. The results of a simulation conducted on the variable frequency speed control system of an induction motor verify the feasibility and effectiveness of the proposed method.

Robustness of adaptive inverse control in solving internal and external disturbance uncertainties for a class of non-linear systems

In this paper, adaptive inverse control is developed to deal with the problem about the control performance of non-linear systems, which are affected by the uncertainty of the parameters and external disturbance. An adaptive inverse controller, which is used as a feed forward controller, is designed to realise real-time tracking of the system output and to enhance the robustness of the system under parameter perturbation. A noise and disturbance canceller is used to eliminate the influence of external disturbance and measurement noise for open loop system output. The results of a simulation conducted on the variable frequency speed control system of an induction motor verify the feasibility and effectiveness of the proposed method.

Adaptive Inverse Control Based on Kriging Algorithm and Lyapunov Theory of Crawler Electromechanical System

The electromechanical system of a crawler is a multi-input, multioutput strongly coupled nonlinear system. In this study, an adaptive inverse control method based on kriging algorithm and Lyapunov theory is proposed to improve control accuracy during adaptive driving. The electromechanical coupling model of the electromechanical system is established on the basis of the dynamic analysis of the crawler. In accordance with the kriging algorithm, the inverse model of the electromechanical system of the crawler is established by offline data. The adaptive travel control law of the crawler is obtained on the basis of Lyapunov theory. Combined with the kriging algorithm, the adaptive driving reverse control method is designed, and the online system is used to update and perfect the inverse system model in real time. Finally, the virtual prototype model of the crawler is established, and the control effect of the adaptive inverse control method is verified by theoretical analysis and virtual prototype simulation.

Adaptive Inverse Neural Network Based DC Motor Speed and Position Control Using FPGA

In this research two types of controllers are designed in order to control the speed and position of DC motor. The first one is a conventional PID controller and the other is an intelligent Neural Network (NN) controller that generate a control signal DC motor. Due to nonlinear parameters and movable laborers such saturation and change in load a conventional PID controller is not efficient in such application; therefore neural controller is proposed in order to decreasing the effect of these parameter and improve system performance. The proposed intelligent NN controller is adaptive inverse neural network controller designed and implemented on Field Programmable Gate Array (FPGA) board. This NN is trained by Levenberg-Marquardt back propagation algorithm. After implementation on FPGA, the response appear completely the same as simulation response before implementation that mean the controller based on FPGA is very nigh to software designed controller. The controllers designed by both m-file and Simulink in MATLAB R2012a version 7.14.0.

RETRACTED ARTICLE: Research on hot-rolling steel products quality control based on BP neural network inverse model

Taking the hot-rolling products made by an iron and steel company as the research object, this paper builds the inverse model reflecting the relationship between the hot-rolling steel product performance indicators, the chemical composition of steel and the rolling technological parameters by using the BP neural network. So the purpose of getting technological parameters is achieved, according to the given steel performance indicators. Combining the BP neural network, adaptive inverse control with internal model control theory, this paper builds the BP neural network inverse model with multiple input and single output based on internal model control. Therefore, it realizes the inverse mapping between the output and the input variables of the BP neural network. And the output variables can be obtained according to the input variables. Besides, this paper also gives the detailed steps to solve the inverse model. Then, the model is applied to the hot-rolling steel products quality control system. The performance indicators of the hot-rolling products are set up, and the rolling technological parameters—the rolling crimp temperature—are solved. The model realizes the controllability of rolling technological parameters. Finally, through the verification of hot-rolling products quality control positive system, the error is in line with the enterprise production requirements.

Adaptive Flow Separation Control Over an Asymmetric Airfoil

A closed-loop adaptive flow control system was experimentally demonstrated to control the flow separation over an NACA 64A210 airfoil. To control the flow separation, piezoelectrically driven synthetic jet actuators and pressure sensors were used. Before performing the closed-loop flow separation control experiment, the effectiveness of the synthetic jet actuator was investigated by open-loop experiments for an NACA 64A210 airfoil in various operating conditions to examine the effective control parameter. The pressure gradient, which was calculated from the difference of mean pressure coefficients between two sensor positions, was found to be a criterion for flow recovery in these experiments. Therefore, the pressure gradient was selected as a control parameter for separated flow. An adaptive flow control system using adaptive inverse control and extremum-seeking control was developed to control the pressure gradient for separated flow over an NACA 64A210 airfoil. The performance of the resulting flow control system was compared with a simple PID control system. Experimental results based on the proposed adaptive flow control system demonstrated the satisfactory tracking performance of the pressure gradient for the pressure recovery. As a result, the proposed adaptive flow control approach enhanced the aerodynamics performances in terms of lift and drag coefficients and lift/drag ratio in the closed-loop control system.

Network Control System (NCS) for Performance Improvement of Dual Converter Power Supply System Using Adaptive Inverse Dynamic Mode (AIDMC) Control Technique

With recent advances in power electronics, dual converter power supply based electric variable-speed drives are significantly used in many industries uses. Power electronic motor drivers are becoming able to efficiently read the inflexible characteristics of DC motor to prerequisites the load. By controlling the input armature voltage, the DC motor speed automatically changed. Half-bridge converter, semi converter, full bridge converter and dual converter are some of the thyristor controlled rectifier circuits are widely used in variable speed drives. In This work presents single phase dual converter power supply system based on adaptive inverse dynamic mode control (AIDMC) technique. The efficient dual converter control solution for industrial network control system (NCS) is proposed based on the industrial grade requirements, designed for low to high voltage operation. As the era of connecting the devices with the cloud is being increased gradually, the proposed method uses an NCS protocol for monitoring as well as controlling the dual converter system. The proposed method provides us the control over forward and reverse rotation, forward and reverse regeneration. From the simulation results, the proposed result offers variable DC voltage which is capable of four quadrant operation of the drive in a speed-torque plane by using MATLAB Simulink environment. A hardware setup also developed to validate the simulation. Over 96% accuracy achieved full load condition for proposed dual converter power supply system based on AIDMC controller.

Robust Adaptive Nonlinear Dynamic Inversion Control for an Air-breathing Hypersonic Vehicle

This paper presents a robust adaptive nonlinear dynamic inversion control approach for the longitudinal dynamics of an air-breathing hypersonic vehicle. The proposed approach adopts a fast adaptation law using high-gain learning rate, while a low-pass filter is synthesized with the modified adaptive scheme to filter out the high-frequency content of the estimates. This modified high-gain adaptive scheme achieves a good transient process and a nice robust property with respect to parameter uncertainties, without exciting high-frequency oscillations. Based on input-output linearization, the nonlinear hypersonic dynamics are transformed into equivalent linear systems. Therefore, the pole placement technique is applied to design the baseline nonlinear dynamic inversion controller. Finally, the simulation results of the modified adaptive nonlinear dynamic inversion control law demonstrate the proposed control approach provides robust tracking of reference trajectories.

Adaptive inverse control of a galvanometer scanner considering the structural dynamic behavior

In industrial applications, scanner systems are utilized for several operations, but do not always meet the desired dynamics. Various controller-based approaches, which concentrate on simple trajectories or changes in operation points, have been presented within the last years; however, these are not sufficient for many production processes. In this paper, adaptive inverse control is utilized to enhance the scanner’s dynamics. The influence of structural dynamics is compensated by taking the behavior of the galvanometers’ mirrors into account. The performance of the new approach is verified by experimental results, reducing the optical error by almost 95% compared to current scanner systems.

Inverse control of multi-dimensional Taylor network for permanent magnet synchronous motor

PurposeThe purpose of this paper is to propose a new control strategy based on adaptive inverse control aiming at high performance control of permanent magnet synchronous motor (PMSM).Design/methodology/approachThis scheme adopts the vector control with double closed-loop structure and introduces a multi-dimensional Taylor network (MTN) inverse control method into velocity-loop. First, the invertibility of PMSM’s mathematical model is proved. Second, a novel dynamic network (MTN) is presented, which has simple structure and faster computing speed. Besides, to realize the high-precision speed control, three MTNs are applied to achieve system modeling, inverse modeling and noise disturbance elimination which correspond to the function of the adaptive identifier, adaptive feed-forward controller and nonlinear adaptive filter, respectively.FindingsThis scheme is designed with the full consideration of the PMSM’s particularity. For the PMSM’s unknown dynamics and time-varying characteristics, the variable forgetting factor recursive least squares algorithm is adopted to improve identification ability, and the weight-elimination algorithm is used to remove redundant regression items in the MTN identifier and inverse controller. In addition, to reduce the influence arose from measurement noise and other stochastic factors, adaptive MTN filter is introduced to eliminate noise disturbance. The computational results show that the proposed scheme possesses excellent control performance and better robustness against the load disturbance.Originality/valueThe paper presents a new inverse control scheme with MTN which is practical and flexible, and the MTN-based control system is very promising for real-time applications.