The design of robust controllers for continuous-time (CT) non-linear systems with completely unknown non-linearities is a challenging task. The inability to accurately identify the non-linearities online or offline mo...
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The design of robust controllers for continuous-time (CT) non-linear systems with completely unknown non-linearities is a challenging task. The inability to accurately identify the non-linearities online or offline motivates the design of robust controllers using adaptive dynamic programming (adp). In this study, an adp-based robust neural control scheme is developed for a class of unknown CT non-linear systems. To begin with, the robust non-linear control problem is converted into a non-linear optimal control problem via constructing a value function for the nominal system. Then an adp algorithm is developed to solve the non-linear optimal control problem. The adp algorithm employs actor-critic dual networks to approximate the control policy and the value function, respectively. Based on this architecture, only system data is necessary to update simultaneously the actor neural network (NN) weights and the critic NN weights. Meanwhile, the persistence of excitation assumption is no longer required by using the Monte Carlo integration method. The closed-loop system with unknown non-linearities is demonstrated to be asymptotically stable under the obtained optimal control. Finally, two examples are provided to validate the developed method.
To improve the power generation efficiency and cells lifetime of proton exchange membrane fuel cells, the oxygen excess ratio (OER) control problem is investigated in this paper. With the use of the double loop cascad...
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To improve the power generation efficiency and cells lifetime of proton exchange membrane fuel cells, the oxygen excess ratio (OER) control problem is investigated in this paper. With the use of the double loop cascade control structure, our work reinforces the iteration adaptive dynamic programming (adp) algorithm and develops a robust OER control strategy drawing upon rich tools and techniques in sliding mode control theory. Our contribution is threefold. First, the proposed OER control policy is insensitive to various dynamics and disturbances by using refined super-twisting sliding mode controller in the external control loop. Second, the adp-based controller is employed in the internal control loop to approximate the real optimal tracking control policy. The algorithm is computationally efficient and easy to implement in hardware platforms. Third, simulation results show that the proposed OER control strategy has a quick, smooth response, which can be further refined by means of numerous advanced neural network algorithms. (C)& nbsp;2022 The Author(s). Published by Elsevier Ltd.& nbsp
This article presents a scenario where a simple simulated organism must explore and exploit an environment containing a food pile. The organism learns to make observations of the environment, use memory to record thos...
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This article presents a scenario where a simple simulated organism must explore and exploit an environment containing a food pile. The organism learns to make observations of the environment, use memory to record those observations, and thus plan and navigate to the regions with the strongest food density. We compare different reinforcement learning algorithms with an adaptive dynamic programming algorithm and conclude that backpropagation through time can convincingly solve this recurrent neural-network challenge. Furthermore, we argue that this algorithm successfully mimics a minimal 'functionally sentient' organism's fundamental objectives and mental environmental-mapping skills while seeking a food pile distributed statically or randomly in an environment.
In this paper we consider the inter-network interference problem in Wireless Body Area Networks (WBANs). We propose a distributed inter-network interference aware power control algorithm motivated by game theory. A po...
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ISBN:
(纸本)9781424456383
In this paper we consider the inter-network interference problem in Wireless Body Area Networks (WBANs). We propose a distributed inter-network interference aware power control algorithm motivated by game theory. A power control game is formulated considering both interference between nearby networks and energy efficiency of WBANs. We derive a distributed power control algorithm called ProActive Power Update (PAPU), which can efficiently find the Nash Equilibrium representing the best tradeoff between energy and network utility. A realistic power control procedure is proposed assuming limited cooperation between WBANs. We compare our algorithm with the adp algorithm where users are punished for interfering with others and we show that our solution can utilize energy much more efficiently by only sacrificing a small amount of network utility. In addition, we show that by adjusting the energy price, PAPU provides a methodology for application scenarios where WBANs have different energy constraints and quality of service requirements.
The deficiencies of massive data like storage difficulty, computation inefficiency, and information redundancy call for ship trajectory compression. While most studies on ship trajectory compression have one or more o...
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The deficiencies of massive data like storage difficulty, computation inefficiency, and information redundancy call for ship trajectory compression. While most studies on ship trajectory compression have one or more of the following drawbacks: the drawback of low compression efficiency;the problem resulted from error ship static information when the distance threshold is set based on ship length or width;poor compression quality for some trajectories, which is caused by experience-based optimal threshold. To solve these problems, we propose the adp (Adaptive-threshold Douglas-Peucker) algorithm based on DP (Douglas-Peucker) algorithm. By determining the key points of each trajectory through the threshold change rate, adp no longer relies on ship static information and makes it easier to determine the threshold, which is what traditional algorithms cannot achieve. Additionally, we use the advantage of matrix operation and the method of reducing points to improve the algorithm's computation efficiency. To verify the feasibility and superiority of the proposed algorithm, we compared our algorithm with DP algorithm, Partition-DP algorithm and Sliding Window algorithm from four aspects, namely, compression rate, Synchronous Euclidean distance, Length Loss Rate and running time. The experimental results prove that our algorithm has advantages over the other three algorithms, especially in threshold setting.
Based on a Space-Time Block Coding Multi-Carrier Code Division Multiple Access (STBC MC-CDMA) system, a Asynchronous Distributed Pricing (adp) algorithm is studied in this paper, in which the cognitive radio(CR) users...
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Based on a Space-Time Block Coding Multi-Carrier Code Division Multiple Access (STBC MC-CDMA) system, a Asynchronous Distributed Pricing (adp) algorithm is studied in this paper, in which the cognitive radio(CR) users announces a price that reflect other users’ compensation paid. The power control is implemented by updating pricing and power level. The adp algorithm is a distributed power control scheme based on the supermodular game theory. Introduced in the STBC MC-CDMA system, it can improve the capacity and the utility of the system, and show faster convergence.
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