Lattice piecewise affine approximation of explicit nonlinear model predictive control with application to trajectory tracking of mobile robot

To promote the widespread use of mobile robots in diverse fields, the performance of trajectory tracking must be ensured. To address the constraints and nonlinear features associated with mobile robot systems, we apply nonlinear model predictive control (MPC) to realize the trajectory tracking of mobile robots. Specifically, to alleviate the online computational complexity of nonlinear MPC, this paper devises a lattice piecewise affine (PWA) approximation method that can approximate both the nonlinear system and control law of explicit nonlinear MPC. The kinematic model of the mobile robot is successively linearized along the trajectory to obtain a linear time-varying description of the system, which is then expressed using a lattice PWA model. Subsequently, the nonlinear MPC problem can be transformed into a series of linear MPC problems. Furthermore, to reduce the complexity of online calculation of multiple linear MPC problems, we approximate the optimal solution of the linear MPC by using the lattice PWA model. That is, for different sampling states, the optimal control inputs are obtained, and lattice PWA approximations are constructed for the state control pairs. Simulations are performed to evaluate the performance of our method in comparison with the linear MPC and explicit linear MPC frameworks. The results show that compared with the explicit linear MPC, our method has a higher online computing speed and can decrease the offline computing time without significantly increasing the tracking error.

LiMuSE: Lightweight Multi-modal Speaker Extraction

The past several years have witnessed significant progress in modeling the Cocktail Party Problem in terms of speech separation and speaker extraction. In recent years, multi-modal cues, including spatial information, facial expression and voiceprint, are introduced to speaker extraction task to serve as complementary information to each other to achieve better performance. However, the front-end model, for speaker extraction, become large and hard to deploy on a resource-constrained device. In this paper, we address the aforementioned problem with novel model architectures and model compression techniques, and propose a lightweight multi-modal framework for speaker extraction (dubbed LiMuSE), which adopts group communication (GC) to split multi-modal high-dimension features into groups of low-dimension features with smaller width which could be run in parallel, and further uses an ultra-low bit quantization strategy to achieve lower model size. The experiments on the GRID dataset show that incorporating GC into the multi-modal framework achieves on par or better performance with 24.86 times fewer parameters, and applying the quantization strategy to the GC-equipped model further obtains about 9 times compression ratio while maintaining a comparable performance compared with baselines. Our code will be available at https://github.com/aispeech-lab/LiMuSE.