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        <title>Frontiers in Control Engineering | Nonlinear Control section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/control-engineering/sections/nonlinear-control</link>
        <description>RSS Feed for Nonlinear Control section in the Frontiers in Control Engineering journal | New and Recent Articles</description>
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        <pubDate>2026-04-23T00:01:17.315+00:00</pubDate>
        <ttl>60</ttl>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcteg.2023.1135258</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcteg.2023.1135258</link>
        <title><![CDATA[Obstacle-avoidance trajectory planning and sliding mode-based tracking control of an omnidirectional mobile robot]]></title>
        <pubdate>2023-03-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Zhe Sun</author><author>Shujie Hu</author><author>Xinan Miao</author><author>Bo Chen</author><author>Jinchuan Zheng</author><author>Zhihong Man</author><author>Tian Wang</author>
        <description><![CDATA[Trajectory planning and tracking control play a vital role in the development of autonomous mobile robots. To fulfill the tasks of trajectory planning and tracking control of a Mecanum-wheeled omnidirectional mobile robot, this paper proposes an artificial potential field-based trajectory-planning scheme and a discrete integral terminal sliding mode-based trajectory-tracking control strategy. This paper proposes a trajectory-planning scheme and a trajectory-tracking control strategy for a Mecanum-wheeled omnidirectional mobile robot by using artificial potential field and discrete integral terminal sliding mode, respectively. First, a discrete kinematic-and-dynamic model is established for the Mecanum-wheeled omnidirectional mobile robot. Then, according to the positions of the robot, target, and obstacles, a reference an obstacle-avoidance trajectory is planned and updated iteratively by utilizing artificial potential field functions. Afterward, a discrete integral terminal sliding mode control algorithm is designed for the omnidirectional mobile robot such that the robot can track the planned trajectory accurately. Meanwhile, the stability of the control system is analyzed and guaranteed proved in the sense of Lyapunov. Last, simulations are executed in the scenarios of static obstacles and dynamic obstacles. The simulation results demonstrate the effectiveness and merits of the presented methods.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcteg.2021.721475</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcteg.2021.721475</link>
        <title><![CDATA[Leader-Following Multi-Agent Coordination Control Accompanied With Hierarchical Q(λ)-Learning for Pursuit]]></title>
        <pubdate>2021-11-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Zhe-Yang Zhu</author><author>Cheng-Lin Liu</author>
        <description><![CDATA[In this paper, we investigate a pursuit problem with multi-pursuer and single evader in a two-dimensional grid space with obstacles. Taking a different approach to previous studies, this paper aims to address a pursuit problem in which only some pursuers can directly access the evader’s position. It also proposes using a hierarchical Q(λ)-learning with improved reward, with simulation results indicating that the proposed method outperforms Q-learning.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcteg.2021.744027</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcteg.2021.744027</link>
        <title><![CDATA[Robust Consensus Problem of Heterogeneous Uncertain Second-Order Multi-Agent Systems Based on Sliding Mode Control]]></title>
        <pubdate>2021-10-01T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Ni ZHAO</author><author>Jian-dong ZHU</author>
        <description><![CDATA[This paper investigates the robust consensus problem for heterogeneous second-order multi-agent systems with uncertain parameters. Based on the sliding mode control method, novel robust consensus protocols are designed for the linear multi-agent systems with uncertain parameters and a class of uncertain nonlinear multi-agent systems. Finally, numerical simulations are given to verify the effectiveness of the proposed protocols.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcteg.2021.734220</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcteg.2021.734220</link>
        <title><![CDATA[Secure Dynamic State Estimation for Cyber Security of AC Microgrids]]></title>
        <pubdate>2021-08-18T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Dariush Fooladivanda</author><author>Qie Hu</author><author>Young Hwan Chang</author>
        <description><![CDATA[A timely, accurate, and secure dynamic state estimation is needed for reliable monitoring and efficient control of microgrids. The synchrophasor technology enables system operators to obtain synchronized measurements in real-time and to develop dynamic state estimators for monitoring and control of microgrids. However, in practice, sensor measurements can be corrupted or attacked. In this study, we consider an AC microgrid comprising several synchronous generators and inverter-interface power supplies, and focus on securely estimating the dynamic states of the microgrid from a set of corrupted data. We propose a secure dynamic state estimator which allows the microgrid operator to reconstruct the dynamic states of the microgrid from a set of attacked or corrupted data without any assumption on attacks or corruptions. Finally, we consider an AC microgrid with the same topology as the IEEE 33-bus distribution system, and show that the proposed secure estimation algorithm can accurately reconstruct the attack signals.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcteg.2021.722092</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcteg.2021.722092</link>
        <title><![CDATA[Time and Action Co-Training in Reinforcement Learning Agents]]></title>
        <pubdate>2021-08-06T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ashlesha Akella</author><author>Chin-Teng Lin</author>
        <description><![CDATA[In formation control, a robot (or an agent) learns to align itself in a particular spatial alignment. However, in a few scenarios, it is also vital to learn temporal alignment along with spatial alignment. An effective control system encompasses flexibility, precision, and timeliness. Existing reinforcement learning algorithms excel at learning to select an action given a state. However, executing an optimal action at an appropriate time remains challenging. Building a reinforcement learning agent which can learn an optimal time to act along with an optimal action can address this challenge. Neural networks in which timing relies on dynamic changes in the activity of population neurons have been shown to be a more effective representation of time. In this work, we trained a reinforcement learning agent to create its representation of time using a neural network with a population of recurrently connected nonlinear firing rate neurons. Trained using a reward-based recursive least square algorithm, the agent learned to produce a neural trajectory that peaks at the “time-to-act”; thus, it learns “when” to act. A few control system applications also require the agent to temporally scale its action. We trained the agent so that it could temporally scale its action for different speed inputs. Furthermore, given one state, the agent could learn to plan multiple future actions, that is, multiple times to act without needing to observe a new state.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcteg.2021.707729</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcteg.2021.707729</link>
        <title><![CDATA[Finite-Time Stability of Hybrid Systems With Unstable Modes]]></title>
        <pubdate>2021-08-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Kunal Garg</author><author>Dimitra Panagou</author>
        <description><![CDATA[In this work, we study finite-time stability of hybrid systems with unstable modes. We present sufficient conditions in terms of multiple Lyapunov functions for the origin of a class of hybrid systems to be finite-time stable. More specifically, we show that even if the value of the Lyapunov function increases during continuous flow, i.e., if the unstable modes in the system are active for some time, finite-time stability can be guaranteed if the finite-time convergent mode is active for a sufficient amount of cumulative time. This is the first work on finite-time stability of hybrid systems using multiple Lyapunov functions. Prior work uses a common Lyapunov function approach, and requires the Lyapunov function to be decreasing during the continuous flows and non-increasing at the discrete jumps, thereby, restricting the hybrid system to only have stable modes, or to only evolve along the stable modes. In contrast, we allow Lyapunov functions to increase both during the continuous flows and the discrete jumps. As thus, the derived stability results are less conservative compared to the earlier results in the related literature, and in effect allow the hybrid system to have unstable modes.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcteg.2021.700053</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcteg.2021.700053</link>
        <title><![CDATA[Bearing-Only Adaptive Formation Control Using Back-Stepping Method]]></title>
        <pubdate>2021-07-15T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sulong Li</author><author>Qin Wang</author><author>Enci Wang</author><author>Yangyang Chen</author>
        <description><![CDATA[In this paper, the bearing-only formation control problem of a class of second-order system with unknown disturbance is investigated, where the control law merely depends on the relative bearings between neighboring agents. In order to offset the effect of unknown disturbance on the system, adaptive estimation is introduced. In the design of the control law, the back-stepping design method and the negative gradient method are used. The Barbalat’s lemma is used to prove the global stability of the system. The simulation results prove the effectiveness of the proposed formation control algorithm.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcteg.2021.632417</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcteg.2021.632417</link>
        <title><![CDATA[Deep Reinforcement Learning Algorithms for Multiple Arc-Welding Robots]]></title>
        <pubdate>2021-02-22T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Lei-Xin Xu</author><author>Yang-Yang Chen</author>
        <description><![CDATA[The applications of the deep reinforcement learning method to achieve the arcs welding by multi-robot systems are presented, where the states and the actions of each robot are continuous and obstacles are considered in the welding environment. In order to adapt to the time-varying welding task and local information available to each robot in the welding environment, the so-called multi-agent deep deterministic policy gradient (MADDPG) algorithm is designed with a new set of rewards. Based on the idea of the distributed execution and centralized training, the proposed MADDPG algorithm is distributed. Simulation results demonstrate the effectiveness of the proposed method.]]></description>
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