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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
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Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
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In structural engineering, the equilibrium of a system is not only determined by its equations of equilibrium but also with the help of constraints. Constraints refer to restrictions on the motion of a system. The proper combinations of constraints can minimize the total number of constraints needed to maintain a system in mechanical equilibrium. When this happens, the system is said to be statically determinate. For such systems, the unknown reaction supports can be estimated using equilibrium...
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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基于估计器的强化学习共识控制用于具有不连续约束的多代理系统.

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    此摘要是机器生成的。

    本研究引入了具有不连续约束的多代理系统 (MAS) 的新方法,使得最佳的共识控制成为可能. 该方法改变了约束,简化了控制器设计,并确保了系统的稳定性.

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    科学领域:

    • 控制系统工程 控制系统工程
    • 人工智能的人工智能
    • 机器人技术 机器人技术 机器人技术

    背景情况:

    • 多代理系统 (MAS) 经常面临不连续状态约束的挑战,限制了传统的控制方法.
    • 现有的基于屏障函数的倒退技术并不直接适用于不连续的约束.

    研究的目的:

    • 为 MAS 解决具有不连续约束的最佳共识控制问题.
    • 开发一种新的技术来处理MAS中不连续的约束边界.
    • 为了简化控制器设计和消除可行性条件.

    主要方法:

    • 使用类似开关函数的约束边界重建技术,将不连续的边界转换为连续的边界.
    • 改进了坐标转换,包括屏障函数和分布式事件触发估计器.
    • 使用神经网络 (NN) 修改的强化学习策略,具有预测错误和修订的术语.

    主要成果:

    • 拟议的技术严格证明了转换的约束保证了原始约束的满足.
    • 改进的坐标转换消除了可行性条件,简化了控制器设计.
    • 修改后的强化学习策略有效地解决了MASS的最佳共识问题.

    结论:

    • 开发的方法提供了一种有效的解决方案,用于在具有不连续约束的MAS中实现最佳的共识控制.
    • 利亚普诺夫的稳定性理论证实了在拟议的控制战略下MAS的稳定性.
    • 模拟结果验证了拟议方法的实际有效性.