Recursive identification for Hammerstein–Wiener systems with dead-zone input nonlinearity

For ARMAX models, modified recursive extended least squares identification algorithms are presented. The basic idea lies in two aspects. One is to decompose the original system into two subsystems. The other is that the most recent information is used to update the parameters, which is different from the hierarchical principle. A simulation example is employed to test the effectiveness of the proposed algorithms.

Identification of an Induction Motor by a Microcomputer based Structure

Many modern control systems become gradually more complicated and, consequently, the approach to control design approaches is both difficulty and complex. Moreover, if such a complex interconnected system is subjected to non‐smooth nonlinearities in the actuator, then unexpected difficulties, degradation or, even worse, instability will arise in the system performance. Therefore, a new decentralized sliding mode control (DSMC) approach for such a class of complex interconnected systems subjected to non‐smooth (deadzone) nonlinearities is proposed in this paper. Based on sliding mode control (SMC) theory, the proposed DSMC laws guarantee the global reaching condition of the sliding mode in uncertain complex interconnected systems with deadzone nonlinearities, that is, they can ensure that the sliding mode is reached in finite time and with prescribed transient behavior. In the sliding mode, the investigated uncertain complex interconnected system with deadzone nonlinearities in the actuator still are insensitive to system uncertainties and external disturbances. The proposed DSMC laws can work effectively for uncertain complex interconnected systems either with or without deadzone nonlinearities in the actuator. However, this cannot be guaranteed by the traditional DSMC design for systems without input deadzone nonlinearities. Furthermore, the sliding motion can be controlled to converge within a specified exponential speed. Finally, two illustrative examples with a series of computer simulations are presented to demonstrate the effectiveness of the proposed DSMC laws.

A recursive algorithm for sampling properties of physical clusters such as size distribution and percolation is presented. The approach can be applied to any system with periodic boundary conditions, given a spatial definition of a cluster. We also introduce some modifications in the algorithm that increases the efficiency considerably if one is only interested in percolation analysis. The algorithm is implemented in Fortran 90 and is compared with a number of iterative algorithms. The recursive cluster identification algorithm is somewhat slower than the iterative methods at low volume fraction but is at least as fast at high densities. The percolation analysis, however, is considerably faster using recursion, for all systems studied. We also note that the CPU time using recursion is independent on the static allocation of arrays, whereas the iterative method strongly depends on the size of the initially allocated arrays.

Mechatronic systems identification using an impulse response recursive algorithm

This paper addresses the problem of recursive identification of Wiener nonlinear systems whose linear subsystems are observable state-space models. The maximum likelihood principle and the recursive identification technique are employed to develop a recursive maximum likelihood identification algorithm which estimates the unknown parameters and the system states interactively. In comparison with the developed recursive maximum likelihood algorithm, a recursive generalized least squares algorithm is also proposed for identification of such Wiener systems. The performance of the developed algorithms is validated by two illustrative examples.

In this paper, a new modeling method of the variable speed variable pitch wind turbine is proposed. More specifically, the linear parameter varying model of wind turbines is optimized by utilizing the variable forgetting factor recursive least squares identification algorithm, which improves the accuracy of the online estimation of the aerodynamic parameters. As the power factor results in the nonlinear characteristics of the wind turbine, the nonlinear power factor is approximate to a polynomial function of the tip speed ratio and the pitch angle. Then the relationship between the aerodynamic parameters and the power factor can be deduced. Furthermore, the solving problem of aerodynamic parameters can be transformed into the identification of the power factor. It is depicted in the simulation results that the modeling method of wind turbines can be optimized from the proposed variable forgetting factor recursive least squares identification algorithm, and the controller obtained from the optimized model can improve stability.

To identify the OEAR model with non-uniformly sampled input data, a recursive Bayesian identification algorithm with covariance resetting is proposed in this paper. Comparing with the conventional recursive least squares algorithm based on auxiliary model, the presented algorithm considers the variance of the colored noise and can estimate the parameter with high accuracy. Furthermore, the algorithm integrates the prior probability density function of the parameters and the prior probability density function of the process data together, and achieves better performance than the maximum likelihood algorithm. To improve the convergence rate, a new covariance resetting method is also integrated in the algorithm. A simulation example demonstrates the performance of the proposed algorithm.

A radio-controlled aircraft was built and equipped with air-data and inertial sensors. A radio frequency link was added to transmit data and receive commands from a ground station. Data from several flight tests were used to characterize the dynamic response of the aircraft. Despite the high level of noise associated with the low-cost sensor suite, consistent identification of critical aircraft parameters was obtained. Flight tests were also conducted with actuator failures induced on one elevator, one aileron, and one engine. Recursive parameter identification produced parameters tracking the effects of the failures, such as reduced effectiveness of pitch commands due to a locked elevator, or roll and sideslip due to engine failure. The identified parameters were also used in reconfigurable control experiments, where knowledge of the aircraft parameters was used to compensate for the effect of failures, reducing the pilot's workload. Overall, the paper demonstrates that recursive identification and reconfigurable control algorithms are implementable in real-time, even in low-cost platforms. They can be designed to effectively compensate for actuator failures and aircraft damage.

Adaptive fuzzy control for full states constrained systems with nonstrict-feedback form and unknown nonlinear dead zone

To achieve the requirements of lightweight, low energy consumption, and low inertia of an underwater vehicle manipulator system, a cable-driven manipulator is installed on the underwater vehicle to form a cable-driven flexible-joint-based underwater vehicle manipulator system (CDFJ–UVMS). The CDFJ–UVMS is a complex nonlinear system subject to model uncertainties, complex marine environment disturbances, and actuator dead-zone nonlinearity. To design track controllers, the CDFJ–UVMS dynamics is divided into two parts: known and unknown. Subsequently, a radial basis function neural network is adopted to approximate the unknown nonlinearity. A neural network performance observer is constructed, whose estimation error is then used to design a novel neural disturbance observer (NDO) to estimate the total disturbance. Finally, an adaptive neural network control method is proposed for the CDFJ–UVMS based on the NDO, neural network compensator, and neural performance observer. The stability of the closed-loop system is analyzed using the Lyapunov method. The proposed control algorithm is applied to a CDFJ–UVMS with two cable-driven joints and compared with other control methods to show the effectiveness of the proposed control algorithm.

Health monitoring system for transmission shafts based on adaptive parameter identification

Recursive prediction error method is one of the main tools for analysis of controlled auto-regressive systems with quantised output. In this study, a recursive identification algorithm is proposed based on the auxiliary model principle by modifying the standard stochastic gradient algorithm. To improve the convergence performance of the algorithm, a particle filtering technique, which approximates the posterior probability density function with a weighted set of discrete random sampling points is utilised to correct the linear output estimates. It can exclude those invalid particles according to their corresponding weights. The performance of the particle filtering technique-based algorithm is much better than that of the auxiliary model-based one. Finally, results are verified by examples from simulation and engineering.

Recursive identification of time varying systems using ARX models is considered, with focus on the accuracy of the transfer function estimates. Three recursive identification algorithms are studied, the least mean squares algorithm, the recursive least squares algorithm and the Kalman filter respectively. The model accuracy is studied in terms of algorithm design variables, input and disturbance signal properties and variations of the true system. Using asymptotic methods approximate expressions for the model quality are derived. The derived expressions are validated by simulations.

In adaptive filters, several recursive algorithms have been used to track state-space model vectors in nonstationary environments. So far, kernel recursive algorithms showed the best results in this regard. With this letter, we aim to propose an algorithm based on a nonlinear function of the error, motivated by the extended recursive least-squares algorithm. Simulations were performed on the problem of tracking a nonlinear Rayleigh fading multipath channel and on a system identification. The results showed that the proposed algorithm can overcome the extended kernel version ones.