A min-max model predictive controller is developed in this paper for tracking control of wheeled mobile robots (WMRs) subject to the violation of nonholonomic constraints in an environment without obstacles. The problem is simplified by neglecting the vehicle dynamics and considering only the steering system. The linearized tracking-error kinematic model with the presence of uncertain disturbances is formed in the frame of the robot. And then, the control policy is derived from the worst-case optimization of a quadratic cost function, which penalizes the tracking error and control variables in each sampling time over a finite horizon. As a result, the input sequence must be feasible for all possible disturbance realizations. The performance of the control algorithm is verified via the computer simulations with a predefined trajectory and is compared to a common discrete-time sliding mode control law. The result shows that the proposed method has a better tracking performance and convergence.

During the production of speech signals, the vowel onset point is an important event containing important information for many speech processing tasks, such as consonant-vowel unit recognition and speech end-points detection. In order to realize accurate automatic detection of vowel onset points, this paper proposes a reliable method using the energy characteristics of homomorphic filtered spectral peaks. The homomorphic filtering helps to separate the slowly varying vocal tract system characteristics from the rapidly fluctuating excitation characteristics in the cepstral domain. The distinct vocal tract shape related to vowels is obtained and the peaks in the estimated vocal tract spectrum provide accurate and stable information for VOP detection. Performance of the proposed method is compared with the existing method which uses the combination of evidence from the excitation source, spectral peaks, and modulation spectrum energies. The detection rate with different time resolutions, together with the missing rate and spurious rate, are used for comprehensive evaluation of the performance on continuous speech taken from the TIMIT database. The detection accuracy of the proposed method is 74.14% for ±10 ms resolution and it increases to 96.33% for ±40 ms resolution with 3.67% missing error and 4.14% spurious error, much better than the results obtained by the combined approach at each specified time resolution, especially the higher resolutions of ±10±30 ms. In the cases of speech corrupted by white noise, pink noise and f-16 noise, the proposed method also shows significant improvement in the performance compared with the existing method.