Model‐free adaptive formation control for unknown multiinput‐multioutput nonlinear heterogeneous discrete‐time multiagent systems with bounded disturbance

Abstract

Based on the model-free adaptive control, the distributed formation control problem is investigated for a class of unknown heterogeneous nonlinear discrete-time multiagent systems with bounded disturbance. Two equivalent data models to the unknown multiagent systems are established through the dynamic linearization technique considering the circumstances with measurable and unmeasurable disturbances. Based on the obtained data models, two distributed controllers are designed with only using the input/output and disturbance data of the neighbor agents system. The tracking error of the closed-loop system driven by the proposed controllers is shown to be bounded by the contraction mapping principle and inductive methods. An example illustrates the effectiveness of the proposed two distributed controllers.

Model‐free adaptive consensus tracking control for unknown nonlinear multi‐agent systems with sensor saturation

“…Recently, some model-free control approaches have been proposed, for instance, model-free adaptive control (MFAC), 27,28 model-free adaptive iterative learning control (MFAILC), 29,30 virtual reference feedback tuning (VRFT), 31 reinforcement learning (RL), 32,33 and so forth. 34 An MFAC scheme is first investigated for SISO systems by Hou and Jin, 35 and then it is extended to MIMO systems.…”

“…(iii) The proposed DMFACTC algorithm is formulated by utilizing input and saturated output information, where the output data also includes measurement disturbance. Compared with the existing model-free strategies, [27][28][29][30][31][32][33][34][35][36][37][38][39][40] the proposed approach requires less I/O measurement data of MASs that can reduce the consumption of communication and storage resources. (iv) In our work, elements of unknown dynamics, sensor saturation, measurement disturbance, fixed and switching communicate topologies are synthesized.…”

Data‐driven consensus control for a class of unknown nonlinear multiagent systems with time delays

“…MASs can complete tasks that a single individual cannot complete alone. Additionally, for complex and redundant tasks, MASs can play the essential role of providing coordination and mutual assistance, especially for problems of vehicle and unmanned aerial vehicle (UAV) formation control, urban road network traffic signal control, consistent coordinated tracking control, sensor networks, power grid restoration, medium-voltage alternating current AC systems in all-electric ship power systems [2][3][4][5][6][7] etc.…”

“…In refs. [7,27,28], the problem of formation control for a class of multi-input multi-output (MIMO) systems was discussed. Additionally, the MFAC method can be combined with other data-driven methods, such as ILC, to complete the task of formation control or consensus, as in refs.…”

Robust optimal tracking control for multiplayer systems by off‐policy Q‐learning approach

“…However, the DT systems converted by employing digital sampling controllers become more popular since digital sampling controllers are preferred from the perspective of industrial applications with the rapid progress of digital technologies. The most relevant result 16 reported a kind of model‐free adaptive robust control technique for DT multiagent systems, while performance optimality cannot be guaranteed. In addition, the information on the reference signal dynamics still needs to be known a priori in Zhang et al, 13 Gao et al, 14 and Odekunle et al 15 when learning the optimal tracking control policies for systems with multiple players or agents.…”