Adaptive Inversion Research Articles

Optimized dead-zone inverse control using reinforcement learning and sliding-mode mechanism for a class of high-order nonlinear systems

An optimized control method is developed for a class of high-order nonlinear dynamic systems having controller dead-zone phenomenon. Dead-zone refers to the controller with zero behavior within a certain range, so it will inevitably affect system performance. In order to make the optimized control eliminate the effect of dead zone, the adaptive dead-zone inverse and reinforcement learning (RL) techniques are combined. The main idea is to find the desired optimized control using RL as the input of dead-zone inverse function and then to design the adaptive algorithm to estimate the unknown parameters of dead-zone inverse function, so that the competent system control can be yielded from the dead-zone function. However, most existing RL algorithms are difficult to apply in the dead zone inverse methods because of the algorithm complexity. The proposed RL greatly simplifies the algorithm because it derives the training rules from the negative gradient of a simple positive function yielded by the partial derivative of Hamilton–Jacobi-Bellman (HJB) equation. Meanwhile, the proposed dead-zone inverse method also requires fewer adaptive parameters. Finally, the proposed control is attested through theoretical proofs and simulation examples.

Event-triggered adaptive neural inverse optimal output-feedback control of steer-by-wire vehicle systems with prescribed performance

In this article, an event-triggered adaptive neural network (NN) output-feedback inverse optimal control issue is investigated for the steer-by-wire vehicle (SBWV) systems. Firstly, NNs are utilized to approximate the unknown nonlinear dynamics and the auxiliary system of SBWV systems is established. Then, a NN state observer is constructed to estimate the unmeasured states. To obtain better tracking performance, the prescribed performance technique is introduced to constrain the tracking error. An event-triggered mechanism (ETM) is established to decrease the numbers of controller execution times. Subsequently, an event-triggered adaptive NN inverse optimal output-feedback control algorithm is proposed by employing the backstepping control theory. It is proved that the developed control method can not only ensure the stability of the SBWV systems, but also guarantee the tracking error does not exceed the prescribed performance bound and converges to a small neighborhood of zero. Finally, simulation results are given to verify the validity of the proposed control method.

Adaptive neural inverse optimal control for uncertain switched nonlinear systems: an improved predefined-time method

For the first time, this paper introduces an improved predefined-time inverse optimal method to address the adaptive tracking control problem of a class of uncertain switched nonlinear systems. The proposed approach enables the system to achieve optimal performance without resorting to the HJB equation. Firstly, we design two-time constants to improve the traditional predefined-time lemma. The improved predefined-time lemma exhibits faster convergence. Subsequently, to realise the design of a predefined-time inverse optimal controller, a specific form of the auxiliary controller is designed using Sontag functions and nonsingular functions. Furthermore, the stability of the system and the optimality of the inverse are proven through the use of a common Lyapunov function method, ensuring that tracking errors converge to the neighbourhood of zero within the predefined time. Finally, the rationality of the proposed control structure is demonstrated through the example results.

Inverse Optimal Adaptive Neural Control for State-Constrained Nonlinear Systems.

Optimizing a performance objective during control operation while also ensuring constraint satisfactions at all times is important in practical applications. Existing works on solving this problem usually require a complicated and time-consuming learning procedure by employing neural networks, and the results are only applicable for simple or time-invariant constraints. In this work, these restrictions are removed by a newly proposed adaptive neural inverse approach. In our approach, a new universal barrier function, which is able to handle various dynamic constraints in a unified manner, is proposed to transform the constrained system into an equivalent one with no constraint. Based on this transformation, a switched-type auxiliary controller and a modified criterion for inverse optimal stabilization are proposed to design an adaptive neural inverse optimal controller. It is proven that optimal performance is achieved with a computationally attractive learning mechanism, and all the constraints are never violated. Besides, improved transient performance is obtained in the sense that the bound of the tracking error could be explicitly designed by users. An illustrative example verifies the proposed methods.

Research on an Ice Tolerance Control Method for Large Aircraft Based on Adaptive Dynamic Inversion

Considering the effect of icing on aircraft control performance, this paper proposes an adaptive dynamic inverse ice tolerance control method based on piecewise constant. A control allocation algorithm is introduced to compensate for the change of control surface performance caused by icing. This method can achieve satisfactory disturbance estimation accuracy under a given sampling time, and thus ensure a closed-loop system error within an acceptable range. The proposed design method is applied to the design of a flight control law for a transport aircraft, aiming to solve the problem of ice-tolerant flight control, reduce the influence of icing conditions on controllability and safe flight of the transport aircraft, and thus improve the flight quality of the transport aircraft. The simulation results are verified under the influence of both standby ice type and failure ice type, and the interference effect on aircraft aerodynamic parameters is further added. The simulation results show that adaptive dynamic inverse control based on piecewise constant can overcome the influence caused by icing and aerodynamic parameter interference, achieve accurate tracking of command, and provide excellent fault tolerance and robustness, which ensures that the transport aircraft can achieve the desired control performance and safe flight capability.

Inversion of image-only intrinsic parameters for steel fibre concrete under combined rate-temperature conditions: An adaptively enhanced machine learning approach

Concrete not only bears quasi-static loads during the service of engineering structures, but also bears impact or explosion due to accidental accidents, so more and more attention has been paid to the study of concrete deformation characteristics and stress distribution. Obtaining the correct constitutive parameters is crucial for the study of the mechanical behavior of concrete, and the determination of constitutive parameters is essentially an inverse process, which is very challenging. In this paper, third-order Bessel curves are used to construct dynamic constitutive equations for steel-fibre concrete under rate-temperature union conditions, and to establish a database of the constitutive parameters corresponding to the factors influencing the mechanical behaviour of concrete. In order to select highly accurate and adaptive intelligent inversion models, Firstly, the black-winged kite optimisation algorithm (BKA) has been improved by improving the black-winged kite leader condition and integrating the optimal perturbation strategy of Morlet wavelet factor, which proves the superiority of the MBKA algorithm by comparing it with the BKA, Dung Beetle Optimisation Algorithm (DBO), Grey Wolf Optimisation Algorithm (GWO), and Harris Hawk Algorithm (HHO) in searching for the optimal results. Secondly, based on the database, the MBKA-LSSVR model, MBKA-LSSVR-Adaboost model, CNN-GRU model and CNN-LSTM model were built sequentially, respectively. The final results show that the MBKA-LSSVR-Adaboost model has the highest accuracy and the best performance for the parameter Pi (i = 0,1,2,3), and the inverted stress-strain curve proves that the proposed method is effective and accurate in determining the proposed constitutive parameters.

Adaptive structure-based full-waveform inversion

In simultaneous-shot full-waveform inversion (FWI), the re-started limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm can be used to suppress crosstalk, but crosstalk cannot be completely eliminated from the inversion results. To further solve the crosstalk problem caused by the interference among individual shots in simultaneous-shot FWI, adaptive structure-based smoothing is applied to the FWI with the restarted L-BFGS algorithm. Structure-based smoothing can mitigate the crosstalk by highlighting structure boundaries. To perform structure-based smoothing, an implicit anisotropic diffusion equation is solved. We carry out a multiscale inversion strategy. Because the FWI results in the low-frequency band contain less structural information and more crosstalk, structure-based smoothing is applied to the frequency band at frequencies higher than the peak frequency to prevent negative effects from the model structure in the low-frequency band. The estimated structural information is iteratively updated during the inversion process. Furthermore, structure-based smoothing is only added to the iterations with invariant encoding to reduce oversmoothing. The invariant encoding means that the shot encoding remains unchanged between iterations. Numerical experiments with an overthrust model indicate that the proposed adaptive structure-based FWI method can effectively suppress artifacts and provide a high-resolution inversion result, even when the encoded data are contaminated with strong noise. Another advantage of the adaptive structure-based FWI method is that no seismic migration needs to be performed, which makes it more efficient for real data.

Robust joint adaptive multiparameter waveform inversion with attenuation compensation in viscoacoustic media

Full-waveform inversion (FWI) has been proven as an effective method to estimate subsurface parameters by iteratively reducing the data residual between the predictions and the observations. Nevertheless, FWI is greatly dependent on the initial model, and a poor initial model will lead to an incorrect solution. Furthermore, owing to the anelasticity of the earth, seismic waves will attenuate during propagation, which results in an attenuated gradient and makes the convergence rate of FWI even worse in viscoacoustic medium. Commonly, multiparameter (e.g. velocity and Q) waveform inversion can be used to mitigate these problems. Benefiting from the theory of Q-compensated wavefield propagation, we develop a Q-compensated joint multiparameter waveform inversion method to weaken the nonlinearity of the FWI objective function, which enables it to cope with challenges related with attenuation-induced gradient energy loss and cycle skipping simultaneously. We refer to our Q-compensated joint multiparameter FWI scheme as QJMFWI. The main contributions of QJMFWI are as follows: (1) given the difficulty associated with the estimation of velocity and Q simultaneously in viscoacoustic media, QJMFWI provides a straightforward waveform inversion method for velocity and Q model construction, by which we can obtain velocity and Q information with improved accuracy and resolution; and (2) compared with conventional FWI methods, QJMFWI relaxes the requirement for a good initial velocity and Q model, which can avoid trapping into local minima. Numerical and field data examples demonstrate that QJMFWI is an effective method to invert for accurate subsurface parameters in viscoacoustic media.

Advancing Quantitative Seismic Characterization of Physical and Anisotropic Properties in Shale Gas Reservoirs with an FCNN Framework Based on Dynamic Adaptive Rock Physics Modeling

Quantitative seismic methods are crucial for understanding shale gas reservoirs. This study introduces a dynamic adaptive rock physics model (DARPM) designed to systematically quantify the relationship between physical parameters and elastic parameters within shale formations. The DARPM uniquely adapts to changes in formation dip angle, allowing adaptive reservoir property assessment. An innovative adaptive rock physics inversion methodology is subsequently proposed to compute values for reservoir physical and seismic anisotropy parameters. This is achieved using well log data and building upon the foundation laid by the established DARPM. We introduce the RPM-FCNN (rock physics model—fully connected neural network) framework, seamlessly integrating the DARPM with the corresponding inversion results into a comprehensive model. This framework facilitates a quantitative analysis of the nonlinear relationship between elastic and reservoir physical parameters. Utilizing the trained RPM-FCNN framework, the spatial distribution of reservoir and seismic anisotropic characteristics can be precisely characterized. Within this framework, the organic matter mixture aspect ratio indicates the continuity of organic matter, while the organic matter porosity reveals the maturity of organic matter. Simultaneously, seismic anisotropy characteristics signify the degree of stratification within the reservoirs. This method, therefore, establishes a robust foundation for identifying favorable areas within shale gas reservoirs.

Adaptive neural inverse optimal control with predetermined tracking accuracy for nonlinear MIMO systems

Adaptive neural inverse optimal control with predetermined tracking accuracy for nonlinear MIMO systems

Adaptive linear inversion of Moho topography in the Tibetan Plateau by combining gravity and seismic data

The Tibetan Plateau is a region where the Indian and Asian-European plates collide. A high-precision Moho topography model is important for the study of plate motion and internal tectonics of the Tibetan Plateau. However, previous gravity-based models failed in representing adequately the Moho topography because crustal effects and biases in the inversion parameters were not fully considered. To address these issues, we extracted the gravity effect caused by the crust using wavelet multi-scale analysis, and used an adaptive linear inversion method with available gravity and seismic data to estimate more accurate inversion parameters. With these two improvements, we inverted for a high-precision Moho topography model of the Tibetan Plateau. The results show that the majority of the Tibetan Plateau has a Moho depth of 60–70 km. The Moho depth in the center of the Qaidam Basin is about 50 km, and the Moho topography between the east and west of the basin has obvious undulations. The Moho depth of the Tarim Basin ranges from 38–50 km. While the undulation of Moho is limited in the center of the basin, obvious uplifts are shown in northern and southern basin. The plate driving force between the Tibetan Plateau and the Tarim Basin may be the primary cause of this phenomenon. By comparing the difference with the seismic data, we found that the RMS of our model is 2.8 km smaller than that of the CRUST 1.0 model, which shows that our model is more accurate.

Robust fuzzy adaptive yaw moment control of humanoid robot with unknown backlash nonlinearity

To achieve an excellent dynamic balance during humanoid robot walks, it is essential to counteract the undesired yaw moment and deal with the nonlinearities existing in the system dynamics. In this paper, the problems of the dynamic balance optimization and yaw moment control for humanoid robot are investigated. First we formulate a constrained dynamic model of compliant joints for robots. Then, a yaw moment control approach based on quadratic objective function is proposed for computing optimal ankle torque to equilibrate the undesired yaw moment. In order to deal with the complicated optimization problem with inequality and equality constraints, a Quadratic Programming (QP) solver based on Linear Variational Inequality (LVI) is adopted. In comparison with the yaw moment control approaches studied previously, the main contributions of this paper are summarized as follows. 1) our newly proposed control scheme is more effective in counteracting the undesired yaw moment for the case of handling object-carrying tasks during walking. 2) a novel adaptive backlash inverse is included in the proposed scheme to cancel the unknown actuator backlash nonlinearity without a prior knowledge of the backlash nonlinearity. By the strict Lyapunov argument, the asymptotic convergence of the system tracking error to arbitrarily small neighborhood of the origin is proved. The simulation examples involving humanoid robot walking are constructed to validate the proposed algorithm.

3-D Adaptive Regularization Nonlinear Inversion of LWD Ultradeep Resistivity in Anisotropic Formation Based on Finite Volume Method of Secondary Field Coupled Potentials and Explicit Fréchet Derivative

3-D Adaptive Regularization Nonlinear Inversion of LWD Ultradeep Resistivity in Anisotropic Formation Based on Finite Volume Method of Secondary Field Coupled Potentials and Explicit Fréchet Derivative

An Adaptive Layering Dual-Parameter Regularization Inversion Method for an Improved Giant Trevally Optimizer Algorithm

An Adaptive Layering Dual-Parameter Regularization Inversion Method for an Improved Giant Trevally Optimizer Algorithm

An adaptive clustering constraint inversion method for gravity data

Clustering constraint is one of the effective ways to improve reliability of the inversion results by incorporating petrophysical information based on some mathematical techniques, such as the Fuzzy C-means clustering (FCM) algorithm. However, for most practical geophysical works, enough reliable petrophysical information is not available. In this way, the FCM inversion method is difficult to be performed. In order to solve this problem to some extent, we proposed an adaptive FCM inversion method (AFCM) in this study, with which the cluster information contained in the gravity data can be extracted adaptively and automatically, and then the FCM inversion can be executed without prior petrophysical information. We presented the detailed steps of the proposed method and illustrated its effectiveness on synthetic and field example tests. The obtained inversion results demonstrate that the AFCM method yields much better results compared with the conventional inversion methods without clustering information, as well as those obtained by using the FCM methods with wrong petrophysical information.

Inverse optimally adaptive neural output‐feedback control of stochastic nonlinear systems

SummaryIn this article, for a class of stochastic nonlinear systems with non‐strict feedback, a neural adaptive inverse optimal output feedback control design scheme is presented. First, according to the existing inverse optimal criterion, an auxiliary system is established. On this basis, a novel observer is built to evaluate the unpredictable states. Second, in the control process, neural networks (NNs) are applied to estimate the unknown functions. Based on NNs and the backstepping technology, an adaptive neural inverse optimal output feedback controller is established. It is indicated that the proposed scheme could ensure the semi‐globally uniformly ultimately bounded of the closed‐loop system and also achieve the objective of inverse optimality. Eventually, an example is applied to testify the feasibility of this scheme.

MTAINV3D: A Three-dimensional Adaptive Magnetotelluric Inversion Tool Using Unstructured Grids

Currently, 3D magnetotelluric inversion often uses a fixed inversion grid. Too sparse inversion grids cannot approximate the complex subsurface conductivity distribution, and may also lead to inversion results failing to converge. Too dense inversion grids will increase not only the computational cost, but also the non-uniqueness of the inversion. Aiming at the above problems, we have developed a three-dimensional magnetotelluric adaptive inversion strategy with unstructured finite element and limited memory BFGS (L-BFGS) algorithms. Firstly, based on the unstructured finite element method, the high accuracy forward responses of the 3D geo-electric model with arbitrary complex terrain and conductivity distributions can be obtained. Then, the forward and inversion meshes are decoupled by using the nested forward and inversion tetrahedral meshes. The Thikhonov regularization objective function with smooth constraints is established and minimized by L-BFGS optimization algorithm with inexact line search procedure, where the gradient of the objective function is solved by using the adjoint principle. Most importantly, an inversion grid adjustment strategy based on the dual constraints of sensitivity matrix and model gradient is proposed, which are used to guide the automatic refinement of inversion grid in the optimization process. Finally, a topographic model is used to validate and test the performance of the proposed adaptive inversion algorithm.

Adaptive Bounding-Based Dynamic Inversion Control of a Coaxial Compound Helicopter

Adaptive Bounding-Based Dynamic Inversion Control of a Coaxial Compound Helicopter

Adaptive Inverse Compensation Fault-Tolerant Control for a Flexible Manipulator With Unknown Dead-Zone and Actuator Faults

This study presents an adaptive inverse compensation control of a flexible single-link manipulator with an unknown dead-zone and actuator faults. First, a dead-zone inverse model and a smooth inverse operator are constructed to address dead-zone nonlinearity. Second, an adaptive fault-tolerant control is utilized to compensate for the partial loss of the effectiveness of the actuator. Third, the coupling errors of the dead-zone and faults are effectively removed and resolved by introducing an estimate for unknown bounds. Then, the direct Lyapunov theory is used to guarantee uniformly ultimately bounded stability in the controlled system. Finally, the simulations and experiments demonstrate the efficiency of the presented method.

Decentralized Adaptive Neural Inverse Optimal Control of Nonlinear Interconnected Systems.

Existing methods on decentralized optimal control of continuous-time nonlinear interconnected systems require a complicated and time-consuming iteration on finding the solution of Hamilton-Jacobi-Bellman (HJB) equations. In order to overcome this limitation, in this article, a decentralized adaptive neural inverse approach is proposed, which ensures the optimized performance but avoids solving HJB equations. Specifically, a new criterion of inverse optimal practical stabilization is proposed, based on which a new direct adaptive neural strategy and a modified tuning functions method are proposed to design a decentralized inverse optimal controller. It is proven that all the closed-loop signals are bounded and the goal of inverse optimality with respect to the cost functional is achieved. Illustrative examples validate the performance of the methods presented.