SIMULATION OF AN ADARTIVE FILTER BASED ON THE RECURSIVE LEAST SQUARES METHOD

Modern electronics systems are high-speed, compact and require the use of energy-efficient digital electronics devices (DED’s) such as microcontrollers, programmable logic integrated circuits (FPGA’s), digital signal processors. Application of the DED’s is a hardware implementation of high - performance digital signal processing (DSP) algorithms based on the target architecture of the electronic device. In order to accellarate of the design process in the direct hardware implementation of DSP algorithms, simulation models are created to enable optimizing the design process at the stage of a creation of the programming part for FPGA. The paper presents the results of a study of the adaptive filter (AF) model based on the recursive least squares method (RLS). According to the analysis of time and frequency parameters of the AF model has been conducted during simulation it was found that the qualitative filtering process starting from the 24th order and further increasing the AF order does not significantly improve signal filtering, but only increases the required hardware resources. In process of the verification of the proposed simulation model, the AF-based noise reduction system has been modeled and the THD level of 7.103 % was obtained for the built-in AF unit, which is more than one and a half times higher than the proposed AF unit 4.323 %, which confirmed the efficiency of the developed AF unit. Thus, during the study, the optimal order of AF has been determined, which will allow more efficient use of FPGA resources during the hardware implementation of AF. In accordance with the results of the study, the correctness and efficiency of the created hardware-oriented simulation model has been proved, as well as the hardware-oriented structure of the adaptive RLS filter for future implementation on FPGA nas been shown.

Online parameter estimation and adaptive control with limited information in structures

An online parameter estimation is important along with the adaptive control, that is, a time-dependent plant. This study uses both online identification and the simple adaptive control algorithm with velocity feedback. The recursive least squares method was used to identify the stiffness and damping parameters of the structure’s stories. Identification was carried out online without initial estimation and only by measuring the structural responses. The limited information regarding sensor measurements, parameter convergence, and the effects of the covariance matrix is examined. The integration of the applied online identification, the appropriate reference model selection in simple adaptive control, and adopting the proportional integral filter was used to limit the structural control response error. Some numerical examples are simulated to verify the ability of the proposed approach. Despite the limited information, the results show that the simultaneous use of online identification with the recursive least squares method and simple adaptive control algorithm improved the overall structural performance.

Real-time fractional order system identification using recursive least squares method

In this paper, we address the problem of fractional order systems real-time identification based on recursive least squares technique. The fractional order model is approximated using the Charef Singularity function method and Grünwald numerical approximation for fractional order integral and derivative. We show by numerical simulation example that the identified model represents the original system efficiently.

A Method for Parameter Identification of Combined Integrating Systems

This manuscript presents a novel structure of combined integration in the process industry and proposes an efficient method for identifying its parameters. Combined integrating processes are delay-time processes that widely exist in the industry. Conventional identification methods have a low-identification accuracy, a large vibration amplitude of the identification curve, and a poor effect for this kind of process. In this paper, a new variable forgetting factor recursive least squares method was adopted to ameliorate this problem. The method could quickly track the mutation of the ideal parameters of the process and accurately identify which of the parameters has high precision, small oscillation, and a smooth curve. The simulation results indicate that the proposed method is a significant improvement compared to the ordinary recursive least squares method and the recursive least squares with a fixed forgetting factor method, and a concise program can be verified. The experimental simulation based on the actual cut tobacco rebaking industrial process shows that the proposed method has improved identification precision and the best following effect.