Adaptive Inverse Model Research Articles

Chinese knot inspired isotropic linear scanning method for improved imaging performance in AFM

Atomic force microscope (AFM) is an important nano-scale surface characterization and measurement method. Raster scanning method (RSM), widely used in AFMs, faces limitations on scanning speed and imaging accuracy. In this paper, an isotropic linear scanning method (ILSM) is proposed to improve the AFM imaging performance. Inspired by Chinese knot, ILSM is constructed by integrating two iterative triangular scanning trajectories in X and Y axes, similar to triangular Lissajous. Compared with the other scanning methods, ILSM features isotropic scanning trajectory across the scanning region. It is also easy to increase either the scanning speed or scanning resolution using ILSM. Subsequently, to address the hysteresis associated with the piezoelectric actuator, a new tracking algorithm is proposed by combining adaptive Kalman filtering and direct inverse modeling approach. Finally, AFM imaging experiments are conducted to validate the effectiveness of the proposed method. It can be found that the artifacts in RSM can be efficiently eliminated using the proposed method, thus improving the imaging quality.

Adaptive Homotopy-Based Inverse Model for the Geometry Scaling of Microwave Filters

Adaptive Homotopy-Based Inverse Model for the Geometry Scaling of Microwave Filters

Nonlinear robust adaptive control for bidirectional stabilization system of all-electric tank with unknown actuator backlash compensation and disturbance estimation

Since backlash nonlinearity is inevitably existing in actuators for bidirectional stabilization system of all-electric tank, it behaves more drastically in high maneuvering environments. In this work, the accurate tracking control for bidirectional stabilization system of moving all-electric tank with actuator backlash and unmodeled disturbance is solved. By utilizing the smooth adaptive backlash inverse model, a nonlinear robust adaptive feedback control scheme is presented. The unknown parameters and unmodelled disturbance are addressed separately through the derived parametric adaptive function and the continuous nonlinear robust term. Because the unknown backlash parameters are updated via adaptive function and the backlash effect can be suppressed successfully by inverse operation, which ensures the system stability. Meanwhile, the system disturbance in the high maneuverable environment can be estimated with the constructed adaptive law online improving the engineering practicality. Finally, Lyapunov-based analysis proves that the developed controller can ensure the tracking error asymptotically converges to zero even with unmodeled disturbance and unknown actuator backlash. Contrast co-simulations and experiments illustrate the advantages of the proposed approach.

An Adaptive Inverse Model Control Method of Vehicle Yaw Stability with Active Front Steering Based on Adaptive RBF Neural Networks

In order to enhance vehicle yaw stability, this paper proposes a novel adaptive inverse model control (AIMC) method for active front steering (AFS) design based on adaptive radial basis function (RBF) neural networks. To approximate the uncertain nonlinearity and modeling errors in vehicle dynamics, an adaptive RBF neural network (ARBFNN) controller is firstly designed. For smaller tracking errors and better robustness, a RBF networks-based AIMC system is further established. Apart from the ARBFNN control law, another two RBF neural networks are utilized, which work as model identifier and inverse model controller, respectively. After being trained offline, both of them are updated by using online learning algorithms. For faster learning and stability, adaptive learning rates are developed. Consequently, the inverse model controller is able to generate required steering wheel angle. Finally, the comparative studies are carried out and the simulation results illustrate the robustness and effectiveness of proposed control strategy.

Large-scale multiobjective optimization with adaptive competitive swarm optimizer and inverse modeling

Competitive swarm optimizer (CSO) is an efficient algorithm for solving larger-scale multiobjective optimization problems (LSMOPs). However, the winners are not updated in original algorithm, and the random parameters model affects the convergence speed. To improve the performance of CSO for solving LSMOPs, an adaptive competitive swarm optimizer with inverse modeling is developed. In the method, an adaptive parameter model is designed to accelerate the convergence speed when the population not traps in local convergence, and the random parameter model is used to help the losers jump out of local convergence when the population traps in stagnation. Moreover, inverse modeling is used to update the winners, and the number of them is maintained by a new designed environment selection method. The exploitation ability of algorithm is improved and the drawback that convergence speed of the algorithm might be slowed by few winners in the early stage of evolution is avoided. In addition, an adjacent individual competition method is proposed to improve the distribution of solutions. Finally, the proposed algorithm is tested on nine benchmark optimization problems, and the results indicate that the average ranks in terms of IGD and HV of the proposed algorithm outperform those of seven compared algorithms.

Continuous Swept-Sine Vibration Realization Combining Adaptive Sliding Mode Control and Inverse Model Compensation for Electro-hydraulic Shake Table

Continuous Swept-Sine Vibration Realization Combining Adaptive Sliding Mode Control and Inverse Model Compensation for Electro-hydraulic Shake Table

Optimal Output-Constrained Active Noise Control Based on Inverse Adaptive Modeling Leak Factor Estimate

Output saturation, mainly caused by the power amplifier, is a critical issue influencing the performance and stability of an adaptive system, such as in active noise control. In this paper, a quadratically constrained quadratic program (QCQP) is defined to achieve optimal control under the averaging-output-power constraint, which ensures the output of the system operates linearly and hence, avoids the output saturation. To solve this QCQP problem recursively in practice, this paper utilizes one of the leaky-based filtered-x least mean square algorithm with an optimal leak factor. However, this method only can be applied when the statistical feature of the control signal with maximum output-power is known, which is difficult to obtain in practice. Hence, by incorporating the adaptive inverse modeling technique, we can derive a practical estimation of the optimal leaky factor, which is applicable to different noise types. Furthermore, as the optimal output-constraint control forces the output to operate linearly, the nonlinear amplifier model is not required for the leak factor estimate. The simulation of the proposed algorithm is carried out on measured nonlinear paths to validate its efficacy.

Control valve stiction compensation by dynamic inversion: a comparative study

PurposeThe purpose of this paper is to improve the performance of control loop suffering from control valve stiction. Control valve stiction is considered as of one of the main causes of oscillation in process variables, which require performing costly unplanned maintenance and process shutdown. An adaptive solution to handle valve stiction while maintaining safety and quality until next planned maintenance is highly desirable to save considerable cost and effort.Design/methodology/approachThis paper implements a new stiction compensation method built using adaptive inverse model techniques and intelligent control theories. Finite impulse response (FIR) model, which is known to be robust, as a compensator for stiction. The parameters of FIR model are tuned in an adaptive way using differential evolution (DE) technique. The performance of proposed method is compared with other two compensation techniques.FindingsThe new method showed excellent performance of the DE–FIR compensator compared to other dynamic inversion methods in terms of minimizing process variability, energy saving and valve stem aggressiveness.Research limitations/implicationsThe compensation ability for all compensators reduces with the increase of stiction severity, thus the over shoot case always shows the worst result. In future works, other optimization techniques will be explored to find the appropriate technique that can extend the FIR model size with smallest computation time that can improve the performance of the compensator in over shoot case. In addition, the estimation of the valve residual life based on the level of stiction and effort required by the controller should be considered.Originality/valueThe presented approach represents an original contribution to the literature. It performs stiction compensation without a need for a prior knowledge on the process or the valve models and guarantees a smooth control of the stem movement with a low control effort. The proposed approach differs from previous adaptive methods as it uses stable FIR models and DE to find the appropriate parameters of the inverse model and handle nonlinear behavior of stiction.

Adaptive Valve Stiction Compensation Using Differential Evolution

This paper proposes a new stiction compensation method that combines adaptive inverse model techniques and intelligent control theories to compensate for a process containing a sticky valve. This new method reduces the cycling caused by the nonlinear dynamic associated with stiction, while avoiding aggressive movements of the valve stem. The parameters of the compensator depend on both the type and degree of stiction, and their estimates are optimized using differential evolution. Both simulation and experimental results validate the approach. The compensation method demonstrates excellent performance in overcoming the effect of stiction and in reducing the oscillations with smooth valve stem movements.

High precision tracking of a piezoelectric nano-manipulator with parameterized hysteresis compensation

High performance scanning of nano-manipulators is widely deployed in various precision engineering applications such as SPM (scanning probe microscope), where trajectory tracking of sophisticated reference signals is an challenging control problem. The situation is further complicated when rate dependent hysteresis of the piezoelectric actuators and the stress-stiffening induced nonlinear stiffness of the flexure mechanism are considered. In this paper, a novel control framework is proposed to achieve high precision tracking of a piezoelectric nano-manipulator subjected to hysteresis and stiffness nonlinearities. An adaptive parameterized rate-dependent Prandtl-Ishlinskii model is constructed and the corresponding adaptive inverse model based online compensation is derived. Meanwhile a robust adaptive control architecture is further introduced to improve the tracking accuracy and robustness of the compensated system, where the parametric uncertainties of the nonlinear dynamics can be well eliminated by on-line estimations. Comparative experimental studies of the proposed control algorithm are conducted on a PZT actuated nano-manipulating stage, where hysteresis modeling accuracy and excellent tracking performance are demonstrated in real-time implementations, with significant improvement over existing results.

Design and implementation of adaptive inverse control algorithm for a micro‐hand control system

The Letter proposes an online tuned adaptive inverse position control algorithm for a micro-hand. First, the configuration of the micro-hand is discussed. Next, a kinematic analysis of the micro-hand is investigated and then the relationship between the rotor position of micro-permanent magnet synchronous motor and the tip of the micro-finger is derived. After that, an online tuned adaptive inverse control algorithm, which includes an adaptive inverse model and an adaptive inverse control, is designed. The online tuned adaptive inverse control algorithm has better performance than the proportional–integral control algorithm does. In addition, to avoid damaging the object during the grasping process, an online force control algorithm is proposed here as well. An embedded micro-computer, cRIO-9024, is used to realise the whole position control algorithm and the force control algorithm by using software. As a result, the hardware circuit is very simple. Experimental results show that the proposed system can provide fast transient responses, good load disturbance responses, good tracking responses and satisfactory grasping responses.

Adaptive online inverse control of a shape memory alloy wire actuator using a dynamic neural network

Shape memory alloy (SMA) actuators exhibit severe hysteresis, a nonlinear behavior, which complicates control strategies and limits their applications. This paper presents a new approach to controlling an SMA actuator through an adaptive inverse model based controller that consists of a dynamic neural network (DNN) identifier, a copy dynamic neural network (CDNN) feedforward term and a proportional (P) feedback action. Unlike fixed hysteresis models used in most inverse controllers, the proposed one uses a DNN to identify online the relationship between the applied voltage to the actuator and the displacement (the inverse model). Even without a priori knowledge of the SMA hysteresis and without pre-training, the proposed controller can precisely control the SMA wire actuator in various tracking tasks by identifying online the inverse model of the SMA actuator. Experiments were conducted, and experimental results demonstrated real-time modeling capabilities of DNN and the performance of the adaptive inverse controller.

Model Reference Adaptive H^|^infin; Control for Distributed Parameter Systems of Hyperbolic Type with Input Nonlinearity by Finite Dimensional Controllers

The problem of constructing model reference adaptive H∞ control for distributed parameter systems of hyperbolic type preceded by unknown input nonlinearity such as dead zone or backlash, is considered in this paper. Distributed parameter systems are infinite dimensional processes, but the proposed control scheme is constructed from finite dimensional controllers. An adaptive inverse model is introduced to estimate and compensate the input nonlinearity. The stabilizing control signal is added to regulate the effect of spill-over terms, and it is derived as a solution of certain H∞ control problem where the residual part of the inverse model and the spill-over term are considered as external disturbances to the process.

Correlations in state space can cause sub-optimal adaptation of optimal feedback control models

Control of our movements is apparently facilitated by an adaptive internal model in the cerebellum. It was long thought that this internal model implemented an adaptive inverse model and generated motor commands, but recently many reject that idea in favor of a forward model hypothesis. In theory, the forward model predicts upcoming state during reaching movements so the motor cortex can generate appropriate motor commands. Recent computational models of this process rely on the optimal feedback control (OFC) framework of control theory. OFC is a powerful tool for describing motor control, it does not describe adaptation. Some assume that adaptation of the forward model alone could explain motor adaptation, but this is widely understood to be overly simplistic. However, an adaptive optimal controller is difficult to implement. A reasonable alternative is to allow forward model adaptation to ‘re-tune’ the controller. Our simulations show that, as expected, forward model adaptation alone does not produce optimal trajectories during reaching movements perturbed by force fields. However, they also show that re-optimizing the controller from the forward model can be sub-optimal. This is because, in a system with state correlations or redundancies, accurate prediction requires different information than optimal control. We find that adding noise to the movements that matches noise found in human data is enough to overcome this problem. However, since the state space for control of real movements is far more complex than in our simple simulations, the effects of correlations on re-adaptation of the controller from the forward model cannot be overlooked.Electronic supplementary materialThe online version of this article (doi:10.1007/s10827-011-0350-z) contains supplementary material, which is available to authorized users.

Semi-active suspension control to improve ride and handling using magnetorheological (MR) damper

This paper presents the simulation study of a semi-active suspension system using non-parametric model of magnetorheological (MR) damper. Hybrid stability augmentation-force control (HSAS-FC) is proposed as the control algorithm. The performance of the proposed control algorithm is analysed using validated full vehicle model and compared with modified hybrid skyhook groundhook control and passive system. The HSAS-FC is used to estimate the desired force needed to simultaneously improve ride dynamics and handling performance. The stability augmentation system (SAS) is to improve ride, meanwhile the force control (FC) is used to control roll rate and pitch rate due to lateral load transfer and longitudinal load transfer. An adaptive inverse current model was developed to produce the required current by the MR damper. The results show the new system is effective in isolating vehicle body from unwanted motions at the body centre of gravity. The amplitude and settling time of unwanted body motions are reduce for ride.

RBF Networks-Based Adaptive Inverse Model Control System for Electronic Throttle

An electronic throttle is a dc-motor-driven valve that regulates air inflow into the combustion system of the engine. An effective controller for electronic throttle is not easy to accomplish since the plant is burdened with strong nonlinear effects of stick-slip friction, spring, and gear backlash. In this brief, an adaptive inverse model control system (AIMCS) is designed for the plant, and two radial basis function (RBF) neural networks are utilized in the AIMCS. The plant is identified by a RBF networks identifier, which provides the sensitivity information of the plant to the control input. And another RBF networks is utilized as inverse model controller established by inverse system method. The RBF networks are offline learned firstly and are online trained using back propagation algorithms. To guarantee convergence and for faster learning, adaptive learning rates are developed. Simulation and experiment results show the effectiveness of the AIMCS.

Development of an adaptive fuzzy logic-based inverse dynamic model for laser cladding process

The precision, performance, and robustness of model-based controllers depend, to a large extent, on the accuracy of the inverse dynamic model which is incorporated in the design of the controller. Due to complex nature of the laser cladding process and presence of time-varying uncertainties, derivation of an accurate mathematical inverse dynamic model of the process is very difficult, and involves many unknown parameters. The inverse dynamic model of the complex nonlinear laser cladding process, which is difficult to be described mathematically, can be described by a fuzzy logic-based inverse dynamic model constructed form input–output data. In this paper, the development of an adaptive fuzzy inverse dynamic model of the laser cladding process, using a systematic fuzzy modelling approach is presented. In a closed-loop laser cladding process, the scanning speed of the substrate is required to produce a clad with desired geometry and quality. In this paper, a fuzzy inverse dynamic model that describes the scanning speed as a function of the cladding parameters in particular the clad height is developed. The developed fuzzy model is validated by comparing the model output with experimental data. The results are very promising and show that fuzzy models can accurately describe the process dynamics.

Adaptive Synthetic Jet Actuator Compensation for A Nonlinear Aircraft Model at Low Angles of Attack

A new technology for the control of next-generation airplanes using virtual wing shaping with synthetic jet actuators is presented. In this approach, an array of actuators is used to modify the aerodynamic performance of a wing and mimic control surface deflections. In the model, the control effect is represented with a nonlinear, parameterized model, derived from experimental data. An adaptive compensation scheme for the implementation of such actuator arrays on a nonlinear tailless aircraft model at low angles of attack has been developed. Adaptive inverse arrays (set of adaptive inverse models of the actuators) are employed for cancelling the effect of the jet nonlinearities. A nonlinear state feedback control law is designed, wherein a set of intermediate states are used as controls for other aircraft states. Parameter projection-based adaptive laws are used to ensure desired closed-loop stability.

Adaptive management of energy transitions in long-term climate change

The UN Framework Convention on Climate Change (UNFCCC) demands stabilization of atmospheric greenhouse gas concentrations at levels that prevent dangerous anthropogenic interference with the climate system. This requires an unprecedented degree of international action for emission reductions and technological change in the energy sector. Extending the established optimal control approach, the paper combines the concepts of adaptive control, inverse modeling and local optimization to climate change decision-making and management. An alternative decision model is described where controls are adjusted towards a moving target under changing conditions. A framework for integrated assessment is introduced, where a basic climate model is coupled to an economic production function with energy as a production factor, which is controlled by the allocation of investments to alternative energy technologies. Investment strategies are shaped by value functions, including utility, costs and climate damages for a given future time horizon, which are translated into admissible emission limits to keep atmospheric carbon concentrations and global mean temperature asymptotically below a given threshold. Conditions for switching between management and technology paths with different costs and carbon intensities are identified. To take account of the substantial uncertainties, an exemplary case discusses the sensitivity of the results to variation of crucial parameters, in particular time discounting, climate damage, taxes and time horizon for decision-making.

Online Adaptive Control of Non-linear Plants Using Neural Networks with Application to Temperature Control System

Although the neural inverse model controllers have demonstrated high potential in the non-conventional branch of non-linear control, their sensitivity to parameter variations and/or parameter uncertainties usually discourage their applications in industry. Indeed, when the controlled system is subject to parameter variations or uncertainties, unsatisfactory tracking performances are obtained. To overcome this problem, a neural inverse model is added to the control scheme and an online update of the weights is provided. Simulations have been carried out to show the robustness of this control algorithm. Moreover, this adaptive neural inverse model controller is implemented on a temperature control system. Good tracking performances are obtained for different set points regulation. The large parameter variations and disturbances have no effect on the tracking performance since they have been compensated online.