Adaptive Finite-Time Tracking Control of Nonholonomic Multirobot Formation Systems With Limited Field-of-View Sensors

Dai, Shi‐Lu; Lu, Ke; Fu, Jun

doi:10.1109/tcyb.2021.3063481

Cited by 70 publications

(25 citation statements)

References 39 publications

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“…Besides, different from Reference 28, the performance function ( 13) is independent of system order n, which reduces the computational complexity for high-order controlled systems. Therefore, compared with the existing works, [24][25][26][27][28] such a performance function ( 13) is easy-to-implement in practice. Moreover, from the proof process of Lemma 5, it is more difficult to prove that the high-order derivatives of 𝜓(t) are bounded on [0, +∞), which makes the controller design become challenging.…”

Section: Similar Tomentioning

confidence: 99%

“…As a consequence, it is of great practical significance to study the finite-time prescribed performance control, and many scholars have carried out preliminary research. [24][25][26][27][28] For example, a finite-time performance function was proposed in work 26 to guarantee the performance constraint of the tracking error for delayed SFNSs with quantized input. Moreover, the finite-time performance guaranteed adaptive consensus control problem was investigated in work 28 for uncertain multiagent systems in the presence of external disturbances.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic learning‐based event‐triggered control of strict‐feedback systems with finite‐time prescribed performance

Wang

Yang

2022

Intl J Robust & Nonlinear

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This paper is concerned with the event-triggered neural learning control for strict-feedback nonlinear systems (SFNSs) subject to predefined tracking performance. A system transformation approach is utilized to transform the original SFNSs into the normal form, which makes it easily to verify the convergence of neural weights since only one neural network (NN) approximator is employed. Then, a novel finite-time performance function is first proposed to characterize the performance constraints of tracking error, thus guaranteeing the prescribed tracking performance. On this basis, a new lemma is given, which is crucial to ensure the stability of the closed-loop system. By using the error transformation technique, the constrained tracking control problem is converted into the stabilization problem of unconstrained error system. Subsequently, by introducing the first-order sliding mode differentiator into the backstepping procedure and with the help of the high-gain observer, a novel adaptive neural tracking control scheme is presented to ensure that the prescribed tracking performance is satisfied and all closed-loop signals are ultimately bounded. After ensuring the satisfaction of the partial persistent excitation condition for radial basis function NN, the neural weight estimates are verified to converge to the ideal values, and then the convergent weights are stored as the constant values. By utilizing the stored knowledge, an event-triggered neural learning controller is constructed to accomplish the same or similar control tasks, which can effectively improve the transient control performance, save the communication resource, and relieve the online computation at the same time. Finally, the effectiveness of the presented scheme is testified by numerical and practical examples.

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Section: Similar Tomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic learning‐based event‐triggered control of strict‐feedback systems with finite‐time prescribed performance

Wang

Yang

2022

Intl J Robust & Nonlinear

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“…It plays a vital role in formation tracking control to pursue performance of fast and accurate trajectory tracking, therefore, to reasonably deal with the problem of finite‐time control (FTC) and asymptotic tracking control (ATC), the FTC and ATC schemes were widely investigated in References 22‐32. The authors of References 22 and 23 studied the global finite‐time stabilization problem for a class of switched nonlinear systems via output feedback method, it effectively achieve tracking errors converging near zero in a finite time.…”

Section: Introductionmentioning

confidence: 99%

“…The authors of Reference 24 invoked finite‐time adaptive control programme for nonlinear systems with uncertain parameters based on the command filters technology. For practical nonholonomic multirobot formation systems, the authors of Reference 25 put forward adaptive finite‐time tracking control method. Not only does it achieve trajectory tracking within a limited time, but it also achieves collision avoidance and connectivity maintenance between robots.…”

Section: Introductionmentioning

confidence: 99%

Adaptive fuzzy finite‐time formation asymptotic tracking control for nonholonomic multirobot systems with inputs quantization

Dong,

2023

Adaptive Control & Signal

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SummaryThis paper investigates the finite‐time formation tracking control problem for nonholonomic multirobot systems with inputs quantization. The fuzzy logic systems (FLSs) and adaptive method are introduced to approximate unknown nonlinear functions and parameters. Multirobot needs to keep safe and effective communicating range, the barrier function and prescribed performance method are employed to ensure collision avoidance and connectivity maintenance. Then, in order to make each robot more precisely and quickly track referenced leader's trajectory and consider the finite channel bandwidth in practical applications, the travel range of robots can be maintained at more secure region and formation can operate normally within the given bandwidth. By combining the command filter and finite‐time theory, a novel decentralized finite‐time formation asymptotic tracking (FTFAT) control strategy is further proposed based on inputs quantization technology, it guarantees that all signals are bounded and the formation tracking errors asymptotically converge to zero in finite‐time. Finally, the practicability of FTFAT strategy is demonstrated absolutely for nonholonomic multirobot systems.

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“…Adaptive neural control (ANC) with backstepping technique can be used to achieve a good control performance for the strict‐feedback systems (SFSs) 1‐9 . However, the techniques listed above are all for continuous‐time cases, few works concern about the ANC technique for the discrete‐time case in spite of the popularity of digital control technology.…”

Section: Introductionmentioning

confidence: 99%

Model‐based event‐triggered neural learning control for discrete‐time strict‐feedback systems

Shi

Wang

2022

Intl J Robust & Nonlinear

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In this article, the model-based event-triggered neural learning control problem is investigated for a class of discrete-time strict-feedback systems. First, with the case of sufficient network resources, an adaptive neural controller is designed with a new neural weight update law, which avoids the n-step delay of traditional adaptive neural control methods. The proposed controller performs the tracking control task while ensuring that the radial basis function neural network can accurately identify the unknown system nonlinear dynamics and the estimated neural weights can converge to their ideal values. Second, with the case of limited network resources, the convergent neural weights can be reused to construct the neural network model, event-trigger conditions, and the neural learning controller, which can improve the tracking control performance and save the communication resources. Especially, compared with the traditional event-triggered adaptive neural controller, the neural learning controller can reduce the burden of online calculations since the constant neural weights are used to construct the neural network model and trigger conditions. For the proposed control scheme, simulation results are given to verify its effectiveness.

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Adaptive Finite-Time Tracking Control of Nonholonomic Multirobot Formation Systems With Limited Field-of-View Sensors

Cited by 70 publications

References 39 publications

Dynamic learning‐based event‐triggered control of strict‐feedback systems with finite‐time prescribed performance

Dynamic learning‐based event‐triggered control of strict‐feedback systems with finite‐time prescribed performance

Adaptive fuzzy finite‐time formation asymptotic tracking control for nonholonomic multirobot systems with inputs quantization

Model‐based event‐triggered neural learning control for discrete‐time strict‐feedback systems

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