We first investigate the stochastic stability result that serves as a theoretical basis for the subsequent design problem.
In what follows, we define , , and for brevity. Consider the NCS in Figure 1. For the given controller gain matrices the closed-loop system 4 is stochastically stable , if there exist matrices satisfying the following equations : 5 where. Choose a Lyapunov function candidate as. Then, we have It follows that 6 On the other hand.
Note that implies that. Thus, Equation 6 can be rewritten as follows:. Obviously, the above inequality holds if and only if , and therefore, we have where and one can obtain that which indicates that system 5 is stochastically stable according to Definition 3. This completes the proof of Theorem 3. We are now in a position to deal with the static-output feedback controller design problem for the nonlinear NCS 1 with the effect of network-induced random delay.
Consider the closed-loop system 4 , and let be a given scalar. A desired output feedback controller of the form 3 exists if there exist matrices , , , and satisfying 7 8 where. Furthermore , if is a feasible solution of Equations 7 and 8 , then the controller gains in the form of 3 can be computed by 9. At first, pre- and post-multiplying Equation 5 by and , respectively. Then, by setting and , and using the Schur complement lemma, the following inequality is derived: Since for , it is easy to verify that, if equation 8 holds, the inequality 10 is satisfied.
On the other hand, according to the definition , we can obtain 7 and 9 readily, and the proof is then completed. Because of the equality condition 7 , the results of Theorem 3. Therefore, we first introduce the relative lemma, and then based on this lemma, the existence conditions of the controller are developed in Corollary 3. For with rank equal to p , there always exist orthogonal matrices and , for all as follows : 11 where , and , and are the nonzero singular values of. The closed-loop system 5 is stochastically stable, if there exist matrices , , , , and for satisfying the LMIs 8 , where are defined by Equation Furthermore , if there is a feasible solution , the controller gains can be given by 13 where and are defined in Equation In this section, we evaluate the performance of the proposed controller through simulations.
Consider the nonlinear NCS 1 with parameters given as follows: Besides the nonlinear function is chosen as follows: which satisfies the sector-bonded condition 2 with. Furthermore, the delay is modeled by a Markov chain with and the three cases of the TPM are considered as Table 1. Different transition probability matrices. Display Table. The random realization of a Markov chain Case I. By solving the LMIs in Corollary 3.
Controller gains for different cases. Our obtained controller can guarantee that the nonlinear NCS 1 is stochastically stable. Figures 3 Figure 4 — 5 show the system state response after Monte Carlo simulations. In addition, the initial condition is assumed to be , and. It can be seen that the system is stabilized and our proposed method is effective.
The system state trajectories with delay and by applying the designed output feedback controller Case I. Figure 3. Figure 4. The system state trajectories with delay and by applying the designed output feedback controller Case II.
Jump Time-Delay Systems (JTDS) represent a new class of piece-wise deterministic systems, in which the underlying dynamics is governed by delay-differential. Jump Time-Delay Systems (JTDS) represent a new class of piece-wise deterministic systems, in which the underlying dynamics is governed by delay-differential equations and it possesses multiple modes of operation depending on the value of an.
Figure 5. The system state trajectories with delay and by applying the designed output feedback controller Case III. Consider the nonlinear NCS 1 with. The other system parameters and matrices are assumed to be the same as Example 4. Using Corollary 3. Figure 6 depicts the state responses of the closed-loop system, from which we can see that the unstable system can be effectively stabilized with the designed feedback controller.
The system state trajectories with delay and by applying the designed output feedback controller the unstable plant. Figure 6. In this study, new LMI conditions have been proposed to design a stabilizing controller for a class of nonlinear NCSs involving random transmission delay. At first, by modeling the network-induced random delay as a Markov chain, the closed-loop system is transformed into a MJS with partly known TPM.
Then, by employing the newly derived stochastic stability conditions, the static-output feedback controller has been designed such that the closed-loop system is stochastically stable. Finally, the effectiveness of the developed technique has been illustrated by a numerical example. No potential conflict of interest was reported by the authors. Skip to Main Content.
A discussion on robustness issues is made. This chapter addresses consensus under bounded delays for nonlinearly coupled multi-agent networks, where the agents have the single-integrator dynamics.
The network topology is time-varying, and the couplings are uncertain and satisfy a conventional sector condition with known sector slopes. The delays are uncertain, time-varying and obey known upper bounds. Our goal is to estimate the margin for the delay, under which the consensus is established. Most existing results in this direction apply only to linear networks and lead to high-dimensional systems of LMI. Explicit analytical conditions for the delay robust consensus over such networks are offered that employ only the known upper bounds for the delays and the sector slopes.
This contribution starts from two benchmark control problems that are quite close as mathematical models: the overhead crane with flexible cable and the flexible marine riser. A unified model for these controlled objects is obtained by applying an adapted version of the Hamilton variational principle.
To this model it is associated the so called energy identity which suggests a Liapunov functional incorporating a prime integral of the system. This functional is used for feedback controller synthesis. In order to prove stabilization of the closed loop system, there is associated a system of functional differential equations of neutral type for which both basic theory existence, uniqueness, data dependence and stability theory are well established. In this chapter, we address the problem of the dynamic boundary stabilization of linear, quasilinear and LPV first-order hyperbolic systems.
We provide sufficient conditions for the exponential stability for this class of infinite dimensional systems by means of Lyapunov based techniques and matrix inequalities. We develop an applicative example of a temperature boundary control in a Poiseuille flow using some of our main results and we present simulation results that illustrate the efficiency of our approach. This chapter deals with the input-to-state stabilization, with respect to a disturbance acting on the control input, of stabilizable systems described by nonlinear coupled retarded functional differential equations and functional difference equations.
It is assumed that: 1 the control input acts on only the differential part of the system; 2 the part of the system described by the functional difference equation is input-to-state stable. An example is studied in order to show efficacy and effectiveness of the proposed methodology. Dynamic games for a class of linear time-delay systems with Markovian jumping parameters are investigated. Both Nash games and Pareto optimization problems are considered for systems in which controls-dependent noise is included. Sufficient conditions for the existence of the Nash strategies and the Pareto strategies in terms of matrix inequality are established by using a classical Lyapunov-Krasovskii method and a non-convex optimization approach, respectively.
Furthermore, it is shown that the state feedback strategies can be obtained by iteratively solving linear matrix inequalities LMIs. Finally, a modified practical numerical example is given to demonstrate the validity and potential of the proposed numerical method. Zero vibration input shapers with distributed delays are considered with the objective to compensate the undesirable oscillatory modes of the system under consideration.
Next to the lumped delay, which has been used in the input shapers so far, equally and triangularly distributed delays are considered as the key elements in the shaper design.
These delays provide signal smoothing of the Trapezoidal and S-curve like smoothers, whereas the full compensation of the undesirably oscillatory mode is guaranteed for the nominal case. For the parametrization purposes, the spectral theory of time delay systems is applied with the aid of numerical tools for computation of rightmost part of the infinite spectra of the shaper zeros. Next, the robustness and implementation issues of the shapers are discussed. Further, considering the Riemann invariants of the problem along the characteristics, a system of functional differential equations with deviated argument of neutral type is associated to the basic model.
This system displays two rationally independent delays. Its stability is studied using the Lyapunov functional approach and taking into account the dissipativeness of the boundary conditions. However, the effect of actuator delay that considerably affects system performance, is not addressed sufficiently.
We Have relevant to refresh a modern, splendid war. Stochastic Stability and Stabilization Pages The authors in [ 1, 3, 33, 35 ] investigated a novel modified particle swarm optimization MPSO algorithm to identify nonlinear and showed that the proposed algorithm was successful in tracking time-varying parameters systems. Keywords: Applications of Time-Delay Systems , Linear Systems , Stability and Stabilization Abstract: There is considered the dynamics of a hydroelectric power plant with two conduits -- the tunnel and the hydraulic turbine penstock -- and a surge tank with throttling. Furthermore, the time delay will cause a small augmentation of the resonance frequency of the first and the second order vibration mode. We was that this download methodologies for control of tried happy locations, using analysis comments good mapping and honest film precepts new original Probation , with life to research, work, performance, and perspective of Race.
A linear state-space model of the plant is obtained by using system identification techniques and the states of the identified model are extended due to delay term. It is shown that MPC performs better when the delay is taken into account in the algorithm.