A sustainable circular economy involves designing and promoting new products with the least environmental impact through increasing efficiency. The emergence of autonomous vehicles (AVs) has been a revolution in the automobile industry and a breakthrough opportunity to create more sustainable transportation in the future. Autonomous vehicles are supposed to provide a safe, easy-to-use and environmentally friendly means of transport. To this end, improving AVs’ safety and energy efficiency by using advanced control and optimization algorithms has become an active research topic to deliver on new commitments: carbon reduction and responsible innovation. The focus of this study is to improve the energy consumption of an AV in a vehicle-following process while safe driving is satisfied. We propose a cascade control system in which an autonomous cruise controller (ACC) is integrated with an energy management system (EMS) to reduce energy consumption. An adaptive model predictive control (AMPC) is proposed as the ACC to control the acceleration of the ego vehicle (the following vehicle) in a vehicle-following scenario, such that it can safely follow the lead vehicle in the same lane on a highway. The proposed ACC appropriately switches between speed and distance control systems to follow the lead vehicle safely and precisely. The computed acceleration is then used in the EMS component to find the optimal engine torque that minimizes the fuel consumption of the ego vehicle. EMS is designed based on two methods: type 1 fuzzy logic system (T1FLS) and interval type 2 fuzzy logic system (IT2FLS). Results show that the combination of AMPC and IT2FLS significantly reduces fuel consumption while the ego vehicle follows the lead vehicle safely and with a minimum spacing error. The proposed controller facilitates smarter energy use in AVs and supports safer transportation.
This paper examines the speed control issue for fully unknown Interior Permanent Magnet Synchronous Motors (IPMSMs). A full adaptive controller is proposed to control the speed of these motors while all physical parameters, adaptive variation bounds, and the load torque are unknown. Applying a four‐step backstepping strategy, which constructs the infrastructure of the whole controller design, and utilizing proper Lyapunov functions leads to generating proper adaptive control rules. The desired control performance is basically satisfied by rejection of the load torque and motor uncertainties. Optimizing the performance criteria, integral tracking error signals have been taken into account in the backstepping procedure in order to enhance the efficiency, robustness, and reduced steady‐state errors. The closed‐loop system stability is analyzed and proved utilizing Lyapunov and Barbalat lemmas. To evaluate the high performance of the designed controller, an illustrative example is simulated whereas the obtained results confirm the efficient treatment of the proposed method for the fully unknown system. The comparison of the simulation results with one of the recent papers in the literature verifies the effectiveness of the proposed method.
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