Experimental Validation of Speech Improvement-Based Stratified Adaptive Finite-Time Saturation Control of Omnidirectional Service Robot
针对嘈杂环境中的人机交互，提出一种基于语音增强的分层自适应有限时间饱和控制方法，通过九种语音指令训练支持向量机分类器，并设计自适应轨迹和控制器实现全方位服务机器人的有限时间零位姿误差，实验验证了方法的有效性。
To implement the human–robot interactions in a noisy environment, the speech improvement-based (SIB) stratified adaptive finite-time saturation control (SAFTSC) for omnidirectional service robot (OSR) is developed. From the outset, the feature vectors of nine designed speech commands are extracted from their frequency signals and then trained by multiclass support vector machine. Two background noises are on-line filtered with the characteristic: “the smaller error in power spectrum is, the larger recovery from noisy power spectrum is.” Comparisons among without or with noise, and filtering are addressed. To achieve the zero pose error of OSR in finite time, an adaptive finite-time indirect trajectory (AFTIT) is constructed. To track the AFTIT with the zero error in finite time, the adaptive finite-time saturation control (AFTSC) is also established. Both AFTIT and AFTSC possess nonlinear switching gain increasing the high-frequency motion capability to fulfill the classified speech command. Simply put, the proposed SIB stratified AFTSC includes the speech improvement for classification, the AFTIT, and the AFTSC. Besides the stability of the closed-loop system is verified by the Lyapunov stability theory, three categories of SIB experiments are compared.