Despite the rise of development in continuum manipulator technology and application, a model-based feedback closed-loop control appropriate for continuum robot designs has remained a significant challenge. Complicated by the soft and flexible nature of the manipulator body, control of continuum structures with infinite dimensions proves to be difficult due to their complex dynamics. In this paper, a novel strategy is designed for trajectory control of a multi-section continuum robot in three-dimensional (3D) space to achieve accurate orientation, curvature, and section length tracking. The formulation connects the continuum manipulator dynamic behavior to a virtual discrete-jointed robot whose degrees of freedom are directly mapped to those of a continuum robot section under the hypothesis of constant curvature. Based on this connection, a computed torque control architecture is developed for the virtual robot, for which kinematics and dynamic equations are constructed and exploited, with appropriate transformations developed for implementation on the continuum robot. The control algorithm is implemented on a six Degree-of-Freedom two-section OctArm continuum manipulator. Experiments show that the proposed method could manage simultaneous extension/contraction, bending, and torsion actions on multi-section continuum robots with decent performance (arc length and curvature error of ±4mm and ±0.35 m $-1$ ). The designed dynamic controller can reduce the curvature tracking error and rise time by up to 48.1% and 94.8% compared to the traditional PID controller during two-section maneuvers.

The lithium-ion battery pack in hybrid electric vehicles is an important energy storage device that requires proper thermal management. A considerable amount of heat is generated by the battery cells owing to their internal resistance during charging and discharging, especially for peak vehicle loads. This study focuses on developing a smart controlled thermal management solution in which a vapor compression system is integrated. A lumped-parameter cylindrical battery thermal model is developed with a Kalman observer to estimate the transient changes in the temperatures of the battery surface, the battery core, and the cooling air flowing around the cells. For the first time, the optimal cooling air temperature of the battery is investigated using optimal control theory. A model predictive controller is then introduced to regulate the refrigerant compressor and to track the ideal cooling air temperature. In a case study, the power consumption of the thermal management system and the behavior of the internal temperature of the battery are investigated under an urban assault cycle. For various operation configurations and conditions, the numerical results demonstrate that the peak error of the core temperature of the battery can be tracked within 0.25 °C of the target value and the energy consumption of the cooling system can be reduced by up to 58%.

Semi-autonomous vehicles equipped with assistive control systems may experience degraded lateral behaviors when aggressive driver steering commands compete with high levels of autonomy. This challenge can be mitigated with effective operator intent recognition, which can configure automated systems in context-specific situations where the driver intends to perform a steering maneuver. In this article, an ensemble learning-based driver intent recognition strategy has been developed. A nonlinear model predictive control algorithm has been designed and implemented to generate haptic feedback for lateral vehicle guidance, assisting the drivers in accomplishing their intended action. To validate the framework, operator-in-the-loop testing with 30 human subjects was conducted on a steer-by-wire platform with a virtual reality driving environment. The roadway scenarios included lane change, obstacle avoidance, intersection turns, and highway exit. The automated system with learning-based driver intent recognition was compared to both the automated system with a finite state machine-based driver intent estimator and the automated system without any driver intent prediction for all driving events. Test results demonstrate that semi-autonomous vehicle performance can be enhanced by up to 74.1% with a learning-based intent predictor. The holistic framework that integrates human intelligence, machine learning algorithms, and vehicle control can help solve the driver-system conflict problem leading to safer operation.

This paper proposes a control strategy to maximize the wind energy captured in a variable speed wind turbine, with an internal induction generator, at low to medium wind speeds. The proposed strategy controls the tip-speed ratio, via the rotor angular speed, to an optimum point at which the efficiency constant (or power coefficient) is maximum for a particular blade pitch angle and wind speed. This control method allows for aerodynamic rotor power maximization without exact wind turbine model knowledge. Representative numerical results demonstrate that the wind turbine can be controlled to achieve near maximum energy capture.