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1.
This study focused on controlling a class of nonlinear systems with actuation time delays. We proposed a novel output-feedback controller in which the magnitude of the input commands is saturated and can be adjusted by varying control parameters. In this design, a predictor term is used to compensate for delays in the input, and auxiliary systems are exploited to provide a priori bounded control commands and account for the lack of full-state information. The stability analysis results revealed that uniformly ultimately bounded tracking is guaranteed despite modeling uncertainties and additive time-varying disturbances in the system dynamics. The performance of the controller was evaluated through simulation.  相似文献   

2.
In this research, a hybrid adaptive bionic fuzzy control strategy is developed for a class of complicated nonlinear multiple-input-multiple-output (MIMO) systems with dead-zone input. The first component of the bionic adaptive controller is a general phrase for tunning system parameters depending on the present state, and the second component is a trend-based compensation for adjusting the system parameters. This technique makes the system more intelligent and boosts its anti-interference capabilities. The stability and convergence are analyzed using the Lyapunov synthetic method, and thus the parameter restrictions of the MIMO system are provided. Finally, the strong anti-interference of the system is verified by the simulations.  相似文献   

3.
In this paper, an adaptive Takagi–Sugeno (T–S) fuzzy controller based on reinforcement learning for controlling the nonlinear dynamical systems is proposed. The parameters of the T–S fuzzy system are learned using the reinforcement learning based on the actor-critic method. This on-line learning algorithm improves the controller performance over the time, which it learns from its own faults through the reinforcement signal from the external environment and tries to reinforce the T–S fuzzy system parameters to converge. The updating parameters are developed using the Lyapunov stability criterion. The proposed controller is faster in learning than the T–S fuzzy that parameters learned using the gradient descent method under the same conditions. Moreover, it is able to handle the load changes and the system uncertainties. The test is carried out based on two mathematical models. In addition, the proposed controller is applied practically for controlling a direct current (DC) shunt machine. The results indicate that the response of the proposed controller has a good performance compared with other controllers.  相似文献   

4.
In this paper, the global stability of coupled control systems (CCSs) is discussed. Assembling the energy of each vertex system with the help of graph theory, a systematic method for constructing a global Lyapunov function of CCSs is proposed. Then, two kinds of stability criteria by Lyapunov-type theorem and coefficient-type theorem with the condition of the system topology are derived. Subsequently, the theoretical results are applied to the microgrid and the criterion of global asymptotical stability of the microgrid is developed. Meanwhile, based on the actual demand of the microgrid, the secondary frequency distributed consistency sliding mode control of the microgrid is proposed using the consensus algorithm. In the presence of a time-varying load, the control can not only quickly stabilize the frequency at the equilibrium point but also dynamically achieve active power sharing. Finally, the simulation of an islanded microgrid is conducted to test the validity and feasibility of our results.  相似文献   

5.
Raising the level of biological realism by utilizing the timing of individual spikes, spiking neural networks (SNNs) are considered to be the third generation of artificial neural networks. In this work, a novel variable-structure-systems based approach for online learning of SNN is developed and tested on the identification and speed control of a real-time servo system. In this approach, neurocontroller parameters are used to define a time-varying sliding surface to lead the control error signal to zero. To prove the convergence property of the developed algorithm, the Lyapunov stability method is utilized. The results of the real-time experiments on the laboratory servo system for a number of different load conditions including nonlinear and time-varying ones indicate that the control structure exhibits a highly robust behavior against disturbances and sudden changes in the command signal.  相似文献   

6.
This paper investigates the problem of decentralized adaptive backstepping control for a class of large-scale stochastic nonlinear time-delay systems with asymmetric saturation actuators and output constraints. Firstly, the Gaussian error function is employed to represent a continuous differentiable asymmetric saturation nonlinearity, and barrier Lyapunov functions are designed to ensure that the output parameters are restricted. Secondly, the appropriate Lyapunov–Krasovskii functional and the property of hyperbolic tangent functions are used to deal with the unknown unmatched time-delay interactions, and the neural networks are employed to approximate the unknown nonlinearities. At last, based on Lyapunov stability theory, a decentralized adaptive neural control method is proposed, and the designed controller decreases the number of learning parameters. It is shown that the designed controller can ensure that all the closed-loop signals are 4-Moment (or 2 Moment) semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges to a small neighborhood of the origin. Two examples are provided to show the effectiveness of the proposed method.  相似文献   

7.
Decentralized adaptive neural backstepping control scheme is developed for uncertain high-order stochastic nonlinear systems with unknown interconnected nonlinearity and output constraints. For the control of high-order nonlinear interconnected systems, it is assumed that nonlinear system functions are unknown. It is for the first time to control stochastic nonlinear high-order systems with output constraints. Firstly, by constructing barrier Lyapunov functions, output constraints are handled. Secondly, at each recursive step, only one adaptive parameter is updated to overcome over-parameterization problems, and RBF neural networks are used to identify unknown nonlinear functions so that the difficulties caused by completely unknown system functions and stochastic disturbances are tackled. Finally, based on the Lyapunov stability method, the decentralized adaptive control scheme via neural networks approximator is proposed, ultimately reducing the number of learning parameters. It is shown that the designed controller can guarantee all the signals of the resulting closed-loop system to be semi-globally uniformly ultimately bounded (SGUUB), and the tracking errors for each subsystem are driven to a small neighborhood of zero. The simulation studies are performed to verify the effectiveness of the proposed control strategy.  相似文献   

8.
The purpose of this study is to enhance the transient performance and mitigate the possible boundary-crossing issue during the design of a neural network-based intelligent prescribed performance control for robotic manipulators that suffer from input saturation. Initially, an auxiliary system is created utilizing the saturation signal, which is then used to modify the prescribed performance boundaries when saturation takes place. This ensures that the tracking errors adhere to the performance constraints even if the available control effort is limited. To further enhance the transient performance of the closed-loop system, a composite learning-based online identification scheme employing a Gaussian function to adaptively adjust the learning rate is utilized instead of a fixed-learning-rate weight updating law to train the neural network. This approach facilitates the reduction of the undesired weight oscillations at the beginning of the control process when the neural network is not sufficiently trained. Lastly, the stability of the closed-loop system is demonstrated by applying the Lyapunov approach, and simulation results support the effectiveness of the identification and control schemes proposed in this study.  相似文献   

9.
In this paper, a self-triggered model predictive controller (MPC) strategy for nonholonomic vehicle with coupled input constraint and bounded disturbances is presented. First, a self-triggered mechanism is designed to reduce the computation load of MPC based on a Lyapunov function. Second, by designing a robust terminal region and proper parameters, recursive feasibility of the optimization problem is guaranteed and stability of the the closed-loop system is ensured. Simulation results show the effectiveness of proposed algorithm.  相似文献   

10.
This article investigates the stability analysis for a class of continuous-time switched systems with state constraints under pre-specified dwell time switchings. The state variables of the studied system are constrained to a unit closed hypercube. Firstly, based on the definition of set coverage, the system state under saturation is confined to a convex polyhedron and the saturation problem is converted into convex hull. Then, sufficient conditions are derived by introducing a class of multiple time-varying Lyapunov functions in the framework of pre-specified dwell time switchings. Such a dwell time is an arbitrary pre-specified constant which is independent of any other parameters. In addition, the proposed Lyapunov functions can efficiently eliminate the “jump” phenomena of adjacent Lyapunov functions at switching instants. The feature of this paper is that the definition of set coverage is utilized to replace the restriction on the row diagonally dominant matrices with negative diagonal elements to analyze stability. The other feature of the constructed time-varying Lyapunov functions is that there are two time-varying functions. One of the two time-varying functions contains the jump rate, which will present a certain degree of freedom in designing the dwell time switching signal. An iterative linear matrix inequality (LMI) algorithm is presented to verify the sufficient conditions. Finally, two examples are presented to show the validity of the method.  相似文献   

11.
In this paper, a novel tracking control scheme for continuous-time nonlinear affine systems with actuator faults is proposed by using a policy iteration (PI) based adaptive control algorithm. According to the controlled system and desired reference trajectory, a novel augmented tracking system is constructed and the tracking control problem is converted to the stabilizing issue of the corresponding error dynamic system. PI algorithm, generally used in optimal control and intelligence technique fields, is an important reinforcement learning method to solve the performance function by critic neural network (NN) approximation, which satisfies the Lyapunov equation. For the augmented tracking error system with actuator faults, an online PI based fault-tolerant control law is proposed, where a new tuning law of the adaptive parameter is designed to tolerate four common kinds of actuator faults. The stability of the tracking error dynamic with actuator faults is guaranteed by using Lyapunov theory, and the tracking errors satisfy uniformly bounded as the adaptive parameters get converged. Finally, the designed fault-tolerant feedback control algorithm for nonlinear tracking system with actuator faults is applied in two cases to track the desired reference trajectory, and the simulation results demonstrate the effectiveness and applicability of the proposed method.  相似文献   

12.
This paper investigates the problem for stability of neutral-type dynamical neural networks involving delay parameters. Different form the previously reported results, the states of the neurons involve multiple delays and time derivative of states of neurons include discrete time delays. The stability of such neural systems has not been given much attention in the past literature due to the difficulty of finding Lyapunov functionals which are suitable for stability analysis of this type of neural networks. This paper constructs a generalized Lyapunov functional by introducing new terms into the well-known Lyapunov functional that enables us to conduct a theoretical investigation into stability analysis of delayed neutral-type neural systems. Based on this modified novel Lyapunov functional, sufficient criteria are derived, which guarantee the existence, uniqueness and global asymptotic stability of the equilibrium point of the neutral-type neural networks with multiple delays in the states and discrete delays in the time derivative of the states. The applicability of the proposed stability conditions rely on testing two basic matrix properties. The constraints impose on the system matrices are determined by using nonsingular M-matrix condition, and the constraints imposed on the coefficients of the time derivative of the delayed state variables are derived by exploiting the vector-matrix norms. We also note that the obtained stability conditions have no involvement with the delay parameters and expressed in terms of nonlinear Lipschitz activation functions. We present a constructive numerical example for this class of neural networks to give a systematic procedure for determining the imposed conditions on the whole system parameters of the delayed neutral-type neural systems.  相似文献   

13.
In this paper, the appointed-time prescribed performance and finite-time tracking control problem is investigated for quadrotor unmanned aerial vehicle (QUAV) in the presence of time-varying load, unknown external disturbances and unknown system parameters. For the position loop, a novel appointed-time prescribed performance control (ATPPC) strategy is proposed based on adaptive dynamic surface control (DSC) frameworks and a new prescribed performance function to achieve the appointed-time convergence and prescribed transient and steady-state performance. For the attitude loop, a new finite-time control strategy is proposed based on a new designed sliding mode control technique to track the desired attitude in finite time. Some assumptions of knowing system parameters are canceled. Finally, the stability of the closed-loop system is proved via Lyapunov Theory. Simulations are performed to show the effectiveness and superiority of the proposed control scheme.  相似文献   

14.
This paper studies the adaptive tracking control problem for a class of uncertain high-order fully actuated (HOFA) systems with actuator faults and full-state constraints. Firstly, we design a novel nonlinear transformation function (NTF) only related to state and constraint boundaries and capable of handling asymmetric time-varying constraints. With the designed function, we obtain an equivalent totally unconstrained HOFA model which is generally simpler to design controllers than first-order state-space model. Then, the adaptive fault-tolerant controller is constructed with the help of the HOFA approach. By applying the Lyapunov stability theory, it is rigorously proved that the output tracking error converges to zero asymptotically, other signals of the resulting closed-loop systems are bounded, and full-state constraints are not violated for all time. Finally, the simulation results verify the efficiency of the proposed control design method.  相似文献   

15.
This paper precedes chaos control of fractional-order chaotic systems in presence of uncertainty and external disturbances. Based on some basic properties on fractional calculus and the stability theorems, we present a hybrid adaptive intelligent backstepping-sliding mode controller (FAIBSMC) for the finite-time control of such systems. The FAIBSMC is proposed based on the concept of active control technique. The asymptotic stability of the controller is shown based on Lyapunov theorem and the finite time reaching to the sliding surfaces is also proved. Illustrative and comparative examples and simulation results are given to confirm the effectiveness of the proposed procedure, which consent well with the analytical results.  相似文献   

16.
A novel adaptive sliding-mode control system is proposed in order to control the speed of an induction motor drive. This design employs the so-called vector (or field oriented) control theory for the induction motor drives. The sliding-mode control is insensitive to uncertainties and presents an adaptive switching gain to relax the requirement for the bound of these uncertainties. The switching gain is adapted using a simple algorithm which does not imply a high computational load. Stability analysis based on Lyapunov theory is also performed in order to guarantee the closed loop stability. Finally, simulation results show not only that the proposed controller provides high-performance dynamic characteristics, but also that this scheme is robust with respect to plant parameter variations and external load disturbances.  相似文献   

17.
Traditional approximate/adaptive dynamic programming (ADP) methods can handle a very special class of systems subject to symmetry constraints. In this study, I extend the exiting ADP to a broader class of nonlinear dynamic systems with asymmetry constraints. Firstly, I propose a novel nonquadratic cost function, based on which the developed optimal controller by solving Hamilton–Jacobi–Bellman equation can limit its value to arbitrarily prescribed bound. Then, to avoid “curse of dimensionality”, I approximately implement the addressed controller via single-network adaptive critic design. Fuzzy Hyperbolic Model is introduced to construct the single critic network by approximating optimal cost function, from which I further derive the optimal control law. The potential advantages are that the control structure is simple and the computational load is low. Lyapunov synthesis proves the ultimately uniformly bounded stability of closed-loop control system. Finally, numerical simulation results verify the efficiency and superiority of the proposed approach.  相似文献   

18.
This paper is concerned with a leader-follower consensus problem for networked Lipschitz nonlinear multi-agent systems. An event-triggered consensus controller is developed with the consideration of discontinuous state feedback. To further enhance the robustness of the proposed controller, modeling uncertainty and switching topology are also considered in the stability analysis. Meanwhile, a time-delay equivalent approach is adopted to deal with the discrete-time control problem. Particularly, a sufficient condition for the stochastic stabilization of the networked multi-agent systems is proposed based on the Lyapunov functional method. Furthermore, an optimization algorithm is developed to derive the parameters of the controller. Finally, numerical simulation is conducted to demonstrate the effectiveness of the proposed control algorithm.  相似文献   

19.
In this paper, a novel adaptive integrated guidance and control (IGC) scheme is proposed for skid-to-turn (STT) missile with partial state constraints and actuator faults. Considering the strict-feedback form of the IGC model, the dynamic surface control (DSC) approach is adopted to design the IGC scheme. To prevent the attack angle, sideslip angle and velocity deflection angle from violating the constraints, the barrier Lyapunov function (BLF) and modified saturation function are employed in the IGC design procedure. Moreover, an auxiliary system is constructed to remove the adverse effects that caused by the modified saturation function. The adaptive laws are constructed to estimate the actuation effectiveness of actuators and the upper bounds of lumped uncertainties in the IGC model. It is theoretically shown that all signals in the closed-loop system are bounded while the state constraints are not violated in presence of actuator faults and uncertainties. Numerical simulation results are presented to verify the effectiveness and robustness of the proposed IGC scheme.  相似文献   

20.
State constraints and uncertain vehicle dynamics severely affect control stability and performance of connected and autonomous vehicle (CAV). To this end, this study puts forward a safe and sub-optimal longitudinal control protocol for CAV platoon with uncertain vehicle dynamics and state constraints. For platoon leader, a second order disturbance observer with L2 stability is presented to estimate lumped uncertainty coupled in vehicle dynamics. By iteratively utilizing control barrier functions and control Lyapunov function, state constraints and speed trajectory tracking stability condition are encoded into control constraints. Based on disturbance observer and encoded constraints, an extended quadratic programming is established as trajectory control law for platoon leader. For platoon followers, backstepping method and disturbance observer accounting for forward communication network are synthesized as formation control law. Besides, conditions of individual vehicle stability and string stability for formation control law are analyzed. Simulation results show that the leader of platoon can automatically switch its drive mode between speed cruising and safe headway keeping, respectively. Furthermore, each follower in platoon can follow its predecessors coordinatively and precisely.  相似文献   

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