Adaptive MPC method based on control compensation for disturbance and model parameters

An approach to identifying the ship motion mathematical model and disturbance parameters in voyage conditions is presented in the paper. The approach is intended for use under constraints during the voyage, i.e., under off-course restrictions and during the voyage in the appointed limited-width corridor. To determine the ship model parameters and static disturbance moment value, it was proposed to use spectral analysis of the angular velocity signal. The formulas and test procedure for identifying the model parameters, along with practical recommendations on the approach implementation were given in the paper. The simulation results show that the accuracy of parameters allows to use these data for the autopilot controller synthesis and adjustment. Thus, the rudder deviation angle may be of about one degree, so the ship deviates from the course only by a small angle during the identification process. Tests on the high seas have proved that the proposed approach can be practiced.

<div class="section abstract"><div class="htmlview paragraph">Electric vehicle (EV) is a worldwide researching focus due to its environmental friendliness, but the inaccurate Remaining Driving Range (RDR) estimation hinders the EVs' popularity, and an accurate determination of the battery Residual Usable Energy (RUE) is the key factor to obtain a precise RDR value. A common RUE estimation method is based on State-of-Charge (SOC) estimation, in which the RUE is proportionally related to the current SOC. However, the battery voltage varies significantly under real-world conditions, and the traditional method results in certain estimation errors. An adaptive RUE prediction method (AEP) is introduced in this paper, in which the dynamic voltage is predicted based on the future discharge profile and a battery model, while the RUE is then calculated by the predicted voltage and current sequences. To prevent the model errors, the model parameters are onboard adapted by comparing the predicted and collected voltage values during discharge, through which the adaptive method could properly describe the dynamic discharge process. Based on a prismatic lithium Nickel-Cobalt-Manganese oxide/Graphite (NCM-G) battery, the RUE values are calculated through different methods under dynamic operation profiles with different discharge rates. As revealed from the results, the AEP could reduce the estimation error by more than 50% compared with the traditional method, and the accuracy is more satisfied for the cases with lower SOC limit. As a result, the presented adaptive method could effectively enhance the RUE accuracy and hence benefit the RDR prediction performance.</div></div>

An adaptive compressor characteristic map method based on the Bézier curve

This paper developed an adaptive backstepping fuzzy sliding control (ABFSC) approach for a micro gyroscope. Based on backstepping design, an adaptive fuzzy sliding mode control was proposed to adjust the fuzzy parameters with self-learning ability and reject the system nonlinearities. With the Lyapunov function analysis of error function and sliding surface function, a comprehensive controller is derived to ensure the stability of the proposed control system. The proposed fuzzy control scheme does not need to know the system model in advance and could approximate the system nonlinearities well. The adaptive fuzzy control method has self-learning ability to adjust the fuzzy parameters. Simulation studies were implemented to prove the validity of the proposed ABFSMC strategy, showing that it can adapt to the changes of external disturbance and model parameters and has a satisfactory performance in tracking and approximation.

On parameters identification of computational models of vibrations during quiet standing of humans

Adaptive prediction of epileptic seizures from intracranial recordings

Control of a helicopter model is a problem of both theoretical and practical interest. With the proliferation of autonomous unmanned aerial vehicles (UAVs) (Castillo et al., 2005; Valavanis, 2007) autopilot modes have become very important. Dynamic properties of a controlled helicopter depend on both its structure and aerodynamic qualities as well as on the control law applied. The problem of output regulation has received much attention and especially during the last decade, its nonlinear version has been intensively developed (Isidori & Byrnes, 1990), (Slotine & Li, 1991). The well known approach to decoupling problem solution based on the Non-linear Inverse Dynamics (NID) method (Balas et al., 1995) may be used if the parameters of the plant model and external disturbances are exactly known. Usually, incomplete information about systems in real practical tasks takes place. In this case adaptive control methods (Astrom & Wittenmark, 1994) or control systems with sliding mode (Utkin, 1992) may be used for solving this control problem. A crucial feature of the sliding mode techniques is that in the sliding phase the motion of the system is insensitive to parameter variation and disturbances in the system. A way of the algorithmic solution of this problem under condition of incomplete information about varying parameters of the plant and unknown external disturbances is the application of the Localization Method (LM) (Vostrikov & Yurkevich, 1993), which allows to provide the desired transients for nonlinear time-varying systems. A development of LM is applied in the present paper, and proposed in (Blachuta et al., 1999; Czyba & Blachuta, 2003; Yurkevich, 2004), method which based on two ideas. The first – the use of high gain in feedback to suppress the disturbances and varying parameters; the second – the use of higher order output derivatives in the feedback loop. The high gain and ”dynamics” of the controller are separated by means of the summing junction with set point signal placed between them. This structure is the implementation of the model reference control with the reference model transfer function which is equal to the inverse of the controller ”dynamics”. It becomes that the proposed structure and method is insensitive to plant parameters changes and external disturbances, and works well both lineal, nonlinear, stationary and nonstationary objects. In the present paper, the proposed method is applied to control of the helicopter model, which is treated as a multivariable system. 10

In this paper, we present an adaptive control method for a conically shaped dielectric elastomer actuator (CSDEA) with different loads to achieve its tracking control objective. Firstly, a dynamic model of the CSDEA is constructed to describe its asymmetrical, rate-dependent hysteresis behavior and creep behavior simultaneously. Then, an offline parameter identification method based on the nonlinear-least-squares algorithm is presented to obtain the nominal values of the model parameters of the CSDEA corresponding to the load of 200 (g). The obtained values are regarded as the initial values of the adaptive online parameter identification. Next, an adaptive online parameter identification method (AOPIM) based on the least-mean-square algorithm is proposed, which can dynamically calculate and update the model parameter values to cope with the load changing of the CSDEA. Lastly, based on two kinds of analytical inverses of the dynamic model and the proposed AOPIM, two adaptive inverse compensators are respectively designed to realize the tracking control of the CSDEA. The control experiments with different desired trajectories and different loads are implemented to demonstrate the effectiveness of the presented adaptive control method. Since the root-mean-square errors of the results of all control experiments are lower than 2.7%, the presented method is remarkable from the perspective of the practical application. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The dielectric elastomer material has beneficial properties of high energy density, low mass density, large deformation, fast response and good biological compatibility. Thus, the soft actuator based on the dielectric elastomer material (SADE) has shown great potentials for being used in soft robots. Nevertheless, the SADE has the complex nonlinear behaviors, which brings a big challenge for its precision control. To deal with this issue, some approaches have been presented in previous literatures. However, the load of the SADE is usually fixed in these studies. This paper focuses on the tracking control of the SADE with different loads. To this end, a dynamic model of the CSDEA is constructed, whose parameter values are dynamically calculated and updated to cope with the load changing of the CSDEA. Through calculating the analytical inverse of the dynamic model, two adaptive inverse compensators are developed. The control experimental results demonstrate that the presented adaptive control method is effective. Considering that the load of the SADE is usually diverse in practical applications, this study pays the way for the application of the SADE.

An adaptive Kriging surrogate method for efficient joint estimation of hydraulic and biochemical parameters in reactive transport modeling

The tradition PI control method based on a model is simple, and easy to realize. But, it has not a good restraining effect on varieties of load disturbance and model parameters. To resolve the problem, this paper presents a self-tuning PI control method based on neural network. The PI parameters are adjusted by using a load disturbance observer and neural network identifier so that the influence that caused by the load disturbance and model parameters varieties are weakened, and the system robustness is improved. The simulation result shows that the method is valid.

Underwater thermal vehicles, as ocean observation tools, are frequently affected by environment disturbances such as waves and currents, which may cause degradation of the observation accuracy of the vehicles. Consequently, it is important to design a controller for a vehicle that can resist ocean disturbance. In this study, an underwater thermal vehicle principle is introduced, and the mathematical model is established in the vertical plane motion. On this basis, an adaptive digital disturbance suppression control method is proposed. For known disturbance parameters, this controller could compensate for external disturbances by pre-setting control parameters using the internal model principle and parameterizations method. For the case where the disturbance parameters are unknown, disturbance parameter estimation method based on forgetting factor least-squares method is proposed to transform the unknown parameter disturbance into a disturbance with known parameters, which is then suppressed by the adaptive digital disturbance rejection control approach. This solution could effectively solve the challenges caused by parameter uncertainty and unknown time-varying ocean external disturbances. Finally, simulations are carried out for the Petrel underwater thermal glider as an example. The simulation results show the proposed control method’s superiority and inherent robustness.

The Markov random field (MRF) model, whose model parameters specify the amount of smoothness in an image, is a popular approach to image magnification. The model parameters must be estimated accurately in order to obtain an elegant solution. The conventional parameter estimation methods consider an image to be homogeneous and have a high computational complexity. However, images are usually not homogenous; using only one set of parameters cannot describe a whole image effectively. We therefore devise an adaptive parameter estimation method for the MRF model to reduce the blocky artifact while preserving the edges in the (high-resolution) HR image. In our method, an initial estimated HR image is divided into small blocks, and the respective parameters are then estimated. Their values are defined as inversely proportional to their energy in the corresponding direction. Then, the gradient descent algorithm is employed iteratively to obtain an improved HR image in a Bayesian MAP framework. Experimental results show that, when compared to the MRF model with a fixed set of parameters, using the MRF model with our adaptive parameter estimation method can produce a magnified image with the edges and texture well preserved. Both the PSNR and visual quality of our proposed method are much better than the fixed-parameter method.

An Experimental Evaluation of an Adaptive MPC Scheme based on Output Error Models Parameterized using Orthonormal Basis Filters∗∗This work was partially funded under the TEQIP- Phase II program sponsored by Govt. of India and World Bank

A new ensemble algorithm for support vector machine (SVM) based on attributes reduction and parameters disturbance is proposed, which is applied to analog circuit fault diagnosis. Firstly, the feature space is divided in several subspaces by attributes reduction algorithm with assurance of high classification capability. And then, for each subspace, the model parameters are disturbed in “low-bias region”. The final result is obtained by using the majority voting procedure twice. Take Sallen-key band-pass filter as simulation instance, and the fault diagnosis result indicates that the algorithm presented in the paper has better performance then single SVM, Adaboost algorithm, “Attribute Bagging” algorithm and so on.

It is essential to control accurately the critical variables in patients whose normal control systems have been faced with trouble. Vital parameters were different for different people; also with respect to similar changes in the parameters, the responses were different. This issue led to changes in the model parameters. It should be noted that even a small change in some of the model parameters can lead to death of patients. Therefore, the purpose of this study was to design a controller which was robust with respect to model parameter changes and disturbances entered to the system; this controller must be able to control their blood sugar levels in a suitable settling time. One of the issues discussed in this paper is type-2 fuzzy method, which is to regulate blood glucose levels. The use of type-2 fuzzy systems allows us to model the effects of uncertainty in the systems which are based on special rules. It also gives us an opportunity to minimize the uncertainty effects, but unfortunately, because of the complexity in use and comprehension of type-2 fuzzy sets than type-1, they were less used. These complexities are due to its three-dimensional nature and the direct dependence of its relations to the development principles; this is how its computational complexity is made.