Distributed Model Predictive Control for Heterogeneous Vehicle Platoon with Inter-Vehicular Spacing Constraints

This paper proposes a distributed control scheme for a platoon of heterogeneous vehicles based on the mechanism of model predictive control (MPC). The platoon composes of a group of vehicles interacting with each other via inter-vehicular spacing constraints, to avoid collision and reduce communication latency, and aims to make multiple vehicles driving on the same lane safely with a close range and the same velocity. Each vehicle is subject to both state constraints and input constraints, communicates only with neighboring vehicles, and may not know a priori desired setpoint. We divide the computation of control inputs into several local optimization problems based on each vehicle’s local information. To compute the control input of each vehicle based on local information, a distributed computing method must be adopted and thus the coupled constraint is required to be decoupled. This is achieved by introducing the reference state trajectories from neighboring vehicles for each vehicle and by employing the interactive structure of computing local problems of vehicles with odd indices and even indices. It is shown that the feasibility of MPC optimization problems is achieved at all time steps based on tailored terminal inequality constraints, and the asymptotic stability of each vehicle to the desired trajectory is guaranteed even under a single iteration between vehicles at each time. Finally, a comparison simulation is conducted to demonstrate the effectiveness of the proposed distributed MPC method for heterogeneous vehicle control with respect to normal and extreme scenarios

Metabolic engineering of Corynebacterium glutamicum for efficient production of optically pure (2R,3R)-2,3-butanediol

Background: 2,3-butanediol is an important platform compound which has a wide range of applications, involving in medicine, chemical industry, food and other fields. Especially the optically pure (2R,3R)-2,3-butanediol can be employed as an antifreeze agent and as the precursor for producing chiral compounds. However, some (2R,3R)-2,3-butanediol overproducing strains are pathogenic such as Enterobacter cloacae and Klebsiella oxytoca. Results: In this study, a (3R)-acetoin overproducing C. glutamicum strain, CGS9, was engineered to produce optically pure (2R,3R)-2,3-butanediol efficiently. Firstly, the gene bdhA from B. subtilis 168 was integrated into strain CGS9 and its expression level was further enhanced by using a strong promoter Psod and ribosome binding site (RBS) with high translation initiation rate, and the (2R,3R)-2,3-butanediol titer of the resulting strain was increased by 33.9%. Then the transhydrogenase gene udhA from E. coli was expressed to provide more NADH for 2,3-butanediol synthesis, which reduced the accumulation of the main byproduct acetoin by 57.2%. Next, a mutant atpG was integrated into strain CGK3, which increased the glucose consumption rate by 10.5% and the 2,3-butanediol productivity by 10.9% in shake-flask fermentation. Through fermentation engineering, the most promising strain CGK4 produced a titer of 144.9\ua0g/L (2R,3R)-2,3-butanediol with a yield of 0.429\ua0g/g glucose and a productivity of 1.10\ua0g/L/h in fed-batch fermentation. The optical purity of the resulting (2R,3R)-2,3-butanediol surpassed 98%. Conclusions: To the best of our knowledge, this is the highest titer of optically pure (2R,3R)-2,3-butanediol achieved by GRAS strains, and the result has demonstrated that C. glutamicum is a competitive candidate for (2R,3R)-2,3-butanediol production

STDPG: A Spatio-Temporal Deterministic Policy Gradient Agent for Dynamic Routing in SDN

Dynamic routing in software-defined networking (SDN) can be viewed as a centralized decision-making problem. Most of the existing deep reinforcement learning (DRL) agents can address it, thanks to the deep neural network (DNN)incorporated. However, fully-connected feed-forward neural network (FFNN) is usually adopted, where spatial correlation and temporal variation of traffic flows are ignored. This drawback usually leads to significantly high computational complexity due to large number of training parameters. To overcome this problem, we propose a novel model-free framework for dynamic routing in SDN, which is referred to as spatio-temporal deterministic policy gradient (STDPG) agent. Both the actor and critic networks are based on identical DNN structure, where a combination of convolutional neural network (CNN) and long short-term memory network (LSTM) with temporal attention mechanism, CNN-LSTM-TAM, is devised. By efficiently exploiting spatial and temporal features, CNNLSTM-TAM helps the STDPG agent learn better from the experience transitions. Furthermore, we employ the prioritized experience replay (PER) method to accelerate the convergence of model training. The experimental results show that STDPG can automatically adapt for current network environment and achieve robust convergence. Compared with a number state-ofthe-art DRL agents, STDPG achieves better routing solutions in terms of the average end-to-end delay.Comment: 6 pages,5 figures,accepted by IEEE ICC 202

Mobile Robot Path Planning Based on Ant Colony Algorithm With A* Heuristic Method

This paper proposes an improved ant colony algorithm to achieve efficient searching capabilities of path planning in complicated maps for mobile robot. The improved ant colony algorithm uses the characteristics of A* algorithm and MAX-MIN Ant system. Firstly, the grid environment model is constructed. The evaluation function of A* algorithm and the bending suppression operator are introduced to improve the heuristic information of the Ant colony algorithm, which can accelerate the convergence speed and increase the smoothness of the global path. Secondly, the retraction mechanism is introduced to solve the deadlock problem. Then the MAX-MIN ant system is transformed into local diffusion pheromone and only the best solution from iteration trials can be added to pheromone update. And, strengths of the pheromone trails are effectively limited for avoiding premature convergence of search. This gives an effective improvement and high performance to ACO in complex tunnel, trough and baffle maps and gives a better result as compare to traditional versions of ACO. The simulation results show that the improved ant colony algorithm is more effective and faster