The stiffness of cable-based robots is studied in this paper. Since antagonistic forces are essential for the operation of cable-based manipulators, their effects on the stiffness should be considered in the design, control, and trajectory planning of these manipulators. This paper studies this issue and derives the conditions under which a cable-based manipulator may become unstable because of the antagonistic forces. For this purpose, a new approach is introduced to calculate the total stiffness matrix. This approach shows that, for a cable-based manipulator with all cables in tension, the root of instability is a rotational stiffness caused by the internal cable forces. A set of sufficient conditions are derived to ensure the manipulator is stabilizable meaning that it never becomes unstable upon increasing the antagonistic forces. Stabilizability of a planar cable-based manipulator is studied as an example to illustrate this approach.

An autonomous vehicle sideslip angle estimation algorithm is proposed based on consensus and vehicle kinematics/ dynamics synthesis. Based on the velocity error measurements between the reduced Inertial Navigation System (R-INS) and the global navigation satellite system (GNSS), a velocity-based Kalman filter is formalized to estimate the velocity errors, attitude errors, and gyro bias errors of the R-INS. The observability issue of the heading error, which affects sideslip estimation, is analyzed. Then, to enhance the observability and improve the estimation accuracy of the heading error under normal driving conditions, a consensus Kalman information filter is developed to synthesize the vehicle kinematics and dynamics and estimate the heading error. Within the developed consensus framework, one node augments a novel heading error measurement from a linear vehicle-dynamic-based sideslip estimator and another node adopts the heading error from the GNSS course. Next, based on the vehicle lateral excitation level, a weighting scheme is proposed to fuse the error state estimates from the velocity-based and consensus Kalman state observers. The stability of the proposed state observers is also investigated. Comprehensive experimental studies, including critical slalom, slight/normal double lane change, and normal driving maneuvers, were conducted to verify the proposed estimation framework; they confirm the reliability and accuracy of the estimator in various automated driving conditions even in comparison with state-of-the-art methods that utilize more measurements (dual-antenna GNSS). Also, this novel multisensor framework is extendable to leverage speed information from other sensors such as cameras and light detection and ranging (LiDAR) to increase reliability and accuracy.

This study presents an integrated multi-objective controller for electric vehicles ( EVs) to achieve four main control objectives concurrently, i.e. slip control in traction and braking, lateral stability control, handling improvement, and rollover prevention. The model-based integrated controller provides a centralised structure to improve the overall stability of the vehicle. The proposed controller considers the interactions of different stability objectives and delivers an optimal solution by including all objectives, simultaneously. The main stability issues in vehicle dynamics including slip control in traction and braking, lateral stability, handling improvement, and rollover prevention are integrated and incorporated in this controller. The stability objectives are realised through proper distribution of torques on four independent wheels of EVs. The unified integrated controller is developed using the Model Predictive Control (MPC) approach. Barrier functions are employed to confine the state variables for slip control, lateral stability, and rollover prevention within the handling limits and safe regions of manoeuverability. Simulation results show that the proposed integrated controller can be effectively used for control of different stability and safety issues of EVs in different situations.

Committed to optimizing the fuel economy of hybrid electric vehicles (HEVs), improving the working conditions of the engine, and promoting research on deep reinforcement learning (DRL) in the field of energy management strategies (EMSs), this article first proposed a DRL-based EMS combined with a rule-based engine start–stop strategy. Moreover, considering that both the engine and the transmission are controlled components, this article developed a novel double DRL (DDRL)-based EMS, which uses a deep Q-network (DQN) to learning the gear-shifting strategy and uses a deep deterministic policy gradient (DDPG) to control the engine throttle opening, and the DDRL-based EMS realizes the multiobjective synchronization control by different types of learning algorithms. After off-line training, the simulation result of the online test shows that the fuel consumption gaps of the proposed DRL- and DDRL-based EMSs are −0.55% and 2.33% compared to that of the deterministic dynamic programming (DDP)-based EMS by overcoming some inherent flaws of DDP, respectively. The computational efficiency has been significantly improved, and the average output time per action is 0.91 ms. Therefore, the control strategy that combines learning- and rule-based controls and the multiobjective control strategies both have the potential to ensure optimization and real-time efficiency.

Human drivers instinctively reason with commonsense knowledge to predict hazards in unfamiliar scenarios and to understand the intentions of other road users. However, this essential capability is entirely missing from traditional decision-making systems in autonomous driving. In response, this paper presents DriveLLM, a decision-making framework that integrates large language models (LLMs) with existing autonomous driving stacks. This integration allows for commonsense reasoning in decision-making. DriveLLM also features a unique cyber-physical feedback system, allowing it to learn and improve from its mistakes. In real-world case studies, the proposed framework outperforms traditional decision-making methods in complex scenarios, including difficult edge cases. Furthermore, we propose a novel approach that allows the decision-making system to interact with human inputs while guarding against adversarial attacks. Empirical evaluations demonstrate that this framework responds correctly to complex human instructions. The code and test data is available: https://github.com/DriveLLM/DriveLLM