A Novel Distributed Bipartite Consensus Control of Nonlinear Multiagent Systems via Prioritized Strategy Approach

Composite‐observer‐based adaptive robust control of electrical‐optical gyro‐stabilized platform with largely unknown modeling uncertainties and partial state feedback

“…That ensures all signals in the closed‐loop systems are bounded, which leads to the results in Theorem 1.Remark Results of Theorem 1 indicate that the proposed DOFCESO controller can effectively compensate for the unknown input dead‐zone nonlinearity, unknown actuator failures, and nonlinear dynamics. In addition, as discussed in Reference 42, the transient and steady state tracking errors can be made smaller by suitable choice of parameters k 1 , k 2 , k 3 , ω e 1 , and ω e 2 . It can be seen, from (), the inverse relationship between the size of the steady state compact set and the parameters k 1 , k 2 , k 3 , ω e 1 , and ω e 2 , since λ 1 tends to increase with the increasement these parameters; the homodromous relationship between the size of the steady state compact set and the parameters σ 1 and σ 2 because C tends to decrease along with the decreasement the parameters σ 1 and σ 2 .…”

“…(3) Unlike the existing results in References 38–43, where the adaptive controller requires enough accurate measurement signals information, this article presents a practical output feedback adaptive controller via integrating the desired trajectory‐based parameter adaptation and the modified ESO possesses much stronger robustness against the unmodeled disturbances. Besides the excellent tracking performance, the proposed controller also has some practical advantages, such as lower noise‐sensitivity, less memory requirement, more efficient computation.…”

Dynamic‐estimator‐based adaptive secure containment control for constrained nonlinear multi‐agent systems under denial‐of‐service attacks

“…where f 1,1 = 0.1cos 2 (x 1,1 ) + 0.1x 2 1,1 , f 1,2 = 0.1 cos(x 1,1 x 1,2 ) 2 + 0.1sin 2 (x 1,1 x 1,2 ), f 2,1 = 0.2sin 2 (x 2,1 ) + 0.1x 2 2,2 , f 2,2 = 0.1sin (x x 2,2 ) + 0.6sin 4 (x 2,1 x 2,2 ), f 3,1 = 0.4cos 2 (x 3,1 ) + 0.1x 2 3,1 , f 3,2 = 0.1cos 2 (x 3,1 x 3,2 ) + 0.5sin 3 (x 3,1 x 3,2 ). Figure 1 shows a connection topology graph G with two leaders and three followers, which presents the connections between agents.…”

“…The last few decades have witnessed substantial development of multi-agent systems with a great many technological breakthroughs in the control field. [1][2][3][4][5] The multi-agent systems have important theoretical research value and a wide range of engineering backgrounds, and universally exist in many practical applications, such as cooperative control of mobile robots, 6 formation control of quadrotor aircrafts, 7,8 design of sensor networks 9,10 and so on. Since various uncertainties and nonlinearities are unavoidable in many industrial and military areas, the consensus of uncertain nonlinear multi-agent systems has become an active research topic.…”

Adaptive output‐feedback control for a class of nonlinear systems based on optimized backstepping technique

“…4 In 1995, Krstic et al systematically organized and summarized this method, thus establishing the basic framework of backstepping design. 5 In recent years, great progress has been made in the combination of backstepping control method with neural network (NN) control 6,7 and fuzzy control, 8 which provides a system framework for tracking design and regulation strategy. [9][10][11][12][13] The above mentioned control methods mainly study the stability of closed-loop systems and do not consider the optimal control problem.…”