The least means squares （LMS） adaptive filter algorithm was used in active suspension system. By adjusting the weight of adaptive filter, the minimum quadratic performance index was obtained. For two-degree-of-freed...The least means squares （LMS） adaptive filter algorithm was used in active suspension system. By adjusting the weight of adaptive filter, the minimum quadratic performance index was obtained. For two-degree-of-freedom vehicle suspension model, LMS adaptive controller was designed. The acceleration of the sprung mass,the dynamic tyre load between wheels and road,and the dynamic deflection between sprung mass and unsprung mass were determined as the evaluation targets of suspension performance. For LMS adaptive control suspension, compared with passive suspension, acceleration power spectral density of sprung mass acceleration under the road input model decreased 8-10 times in high frequency resonance band or low frequency resonance band. The simulation results show that LMS adaptive control is simple and remarkably effective. It further proves that the active control suspension system can improve both the riding comfort and handling safety in various operation conditions, and the method is fit for the active control of the suspension system.展开更多
The Least Mean Square(LMS)adaptive filtering algorithm is a significant filtering algorithm widely used in noise processing and other fields that automatically adjusts the values of filter coefficients according to th...The Least Mean Square(LMS)adaptive filtering algorithm is a significant filtering algorithm widely used in noise processing and other fields that automatically adjusts the values of filter coefficients according to the results,aimed at optimizing the filtered results.Based on a basic serial LMS adaptive filtering algorithm,we propose a vectorized parallel processing scheme for the LMS adaptive filtering algorithm in this work.By combining the characteristics of the algorithm processing flow and those of the parallel technologies used in vector Digital Signal Processes(DSPs),the optimizations such as loop fusion,double-word accessing,and vector shuffling of the LMS algorithm are studied in depth,and the loop unrolling optimization method is used to accelerate the calculation of the algorithm further.Experimental research was conducted on the high-performance FT-M7002 DSP platform in this paper.The results show that,compared with the running performance of the LMS adaptive filtering algorithm in Texas Instruments(TI)’s dsplib library on the TMS320C6678 processor,the optimization effect of the proposed optimization algorithm in this paper can achieve a maximum speed-up ratio of up to 6.9×for medium-scale data.The merged memory access optimization implemented on the GPU platform achieves an average 1.5x speedup compared to the basic parallel scheme.展开更多
With independence assumption, this paper proposes and proves the superior step-size theorem on least mean square (LMS) algorithm, from the view of minimizing mean squared error (MSE). Following the theorem we construc...With independence assumption, this paper proposes and proves the superior step-size theorem on least mean square (LMS) algorithm, from the view of minimizing mean squared error (MSE). Following the theorem we construct a parallel variable step-size LMS filters algorithm. The theoretical model of the proposed algorithm is analyzed in detail. Simulations show the proposed theoretical model is quite close to the optimal variable step-size LMS (OVS-LMS) model. The experimental learning curves of the proposed algorithm also show the fastest convergence and fine tracking performance. The proposed algorithm is therefore a good realization of the OVS-LMS model.展开更多
文摘The least means squares （LMS） adaptive filter algorithm was used in active suspension system. By adjusting the weight of adaptive filter, the minimum quadratic performance index was obtained. For two-degree-of-freedom vehicle suspension model, LMS adaptive controller was designed. The acceleration of the sprung mass,the dynamic tyre load between wheels and road,and the dynamic deflection between sprung mass and unsprung mass were determined as the evaluation targets of suspension performance. For LMS adaptive control suspension, compared with passive suspension, acceleration power spectral density of sprung mass acceleration under the road input model decreased 8-10 times in high frequency resonance band or low frequency resonance band. The simulation results show that LMS adaptive control is simple and remarkably effective. It further proves that the active control suspension system can improve both the riding comfort and handling safety in various operation conditions, and the method is fit for the active control of the suspension system.
基金funded by the Hunan Provincial Natural Science Foundation of China(No.2023JJ50019)the National Science and Technology Major Project(No.2022ZD0119003).
文摘The Least Mean Square(LMS)adaptive filtering algorithm is a significant filtering algorithm widely used in noise processing and other fields that automatically adjusts the values of filter coefficients according to the results,aimed at optimizing the filtered results.Based on a basic serial LMS adaptive filtering algorithm,we propose a vectorized parallel processing scheme for the LMS adaptive filtering algorithm in this work.By combining the characteristics of the algorithm processing flow and those of the parallel technologies used in vector Digital Signal Processes(DSPs),the optimizations such as loop fusion,double-word accessing,and vector shuffling of the LMS algorithm are studied in depth,and the loop unrolling optimization method is used to accelerate the calculation of the algorithm further.Experimental research was conducted on the high-performance FT-M7002 DSP platform in this paper.The results show that,compared with the running performance of the LMS adaptive filtering algorithm in Texas Instruments(TI)’s dsplib library on the TMS320C6678 processor,the optimization effect of the proposed optimization algorithm in this paper can achieve a maximum speed-up ratio of up to 6.9×for medium-scale data.The merged memory access optimization implemented on the GPU platform achieves an average 1.5x speedup compared to the basic parallel scheme.
文摘With independence assumption, this paper proposes and proves the superior step-size theorem on least mean square (LMS) algorithm, from the view of minimizing mean squared error (MSE). Following the theorem we construct a parallel variable step-size LMS filters algorithm. The theoretical model of the proposed algorithm is analyzed in detail. Simulations show the proposed theoretical model is quite close to the optimal variable step-size LMS (OVS-LMS) model. The experimental learning curves of the proposed algorithm also show the fastest convergence and fine tracking performance. The proposed algorithm is therefore a good realization of the OVS-LMS model.
