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1.
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   

2.
In this paper, a robust self-triggered model predictive control (MPC) scheme is proposed for linear discrete-time systems subject to additive disturbances, state and control constraints. To reduce the amount of computation on controller sides, MPC optimization problems are only solved at certain sampling instants which are determined by a novel self-triggering mechanism. The main idea of the self-triggering mechanism is to choose inter-sampling times by guaranteeing a fast decrease in optimal costs. It implies a fast convergence of system states to a compact set where it is ultimately bounded and a reduction of computation times to stabilize the system. Once the state enters a terminal region, the system can be stabilized to a robust invariant set by a state feedback controller. Robust constraint satisfaction is ensured by utilizing the worst-case set-valued predictions of future states in such a way that recursive feasibility is guaranteed for all possible realisations of disturbances. In the case where a priority is given to reducing communication costs rather than improvement in control performance in a neighborhood of the origin, a feedback control law based on nominal state predictions is designed in the terminal region to avoid frequent feedback. Performances of the closed-loop system are demonstrated by a simulation example.  相似文献   

3.
In this paper, we study the robust cooperative output regulation problem of heterogeneous linear multi-agent systems with system uncertainties and directed communication topology. A robust distributed event-triggered control scheme is proposed based on the internal model principle. To avoid continuous monitoring of measurement errors for the event-triggering condition, a novel self-triggered control scheme is further proposed. Moreover, by introducing a fixed timer in the triggering mechanisms, Zeno behavior can be excluded for each agent. An example is finally provided to demonstrate the effectiveness of the proposed self-triggered control scheme.  相似文献   

4.
In the present study, a novel technique is suggested for the adaptive non-linear model predictive control based on the fuzzy approach in three stages. In the presented approach, in the first stage, the prediction and control horizons are obtained from a fuzzy system in each control step. Another fuzzy system is employed to determine the weight factors before the optimization stage of developing new controller. The proposed controller gives the parameters of the model predictive control (MPC) in each control step in order to improve the performance of nonlinear systems. The proposed control scheme is compared with the traditional MPC and Generic Model Control for controlling MED-TVC process. The performances of the three proposed controllers have been investigated in the absence and presence of disturbance in order to evaluate the stability and robustness of the proposed controllers. The results reveal that the novel adaptive controller based on fuzzy approach performs better than the two other controllers in set-point tracking and disturbance rejection with lower IAE criteria. In addition, the average computational time for the adaptive MPC exhibits a decline of 34% in comparison with the traditional MPC.  相似文献   

5.
This paper presents an integrated and practical control strategy to solve the leader–follower quadcopter formation flight control problem. To be specific, this control strategy is designed for the follower quadcopter to keep the specified formation shape and avoid the obstacles during flight. The proposed control scheme uses a hierarchical approach consisting of model predictive controller (MPC) in the upper layer with a robust feedback linearization controller in the bottom layer. The MPC controller generates the optimized collision-free state reference trajectory which satisfies all relevant constraints and robust to the input disturbances, while the robust feedback linearization controller tracks the optimal state reference and suppresses any tracking errors during the MPC update interval. In the top-layer MPC, two modifications, i.e. the control input hold and variable prediction horizon, are made and combined to allow for the practical online formation flight implementation. Furthermore, the existing MPC obstacle avoidance scheme has been extended to account for small non-apriorily known obstacles. The whole system is proved to be stable, computationally feasible and able to reach the desired formation configuration in finite time. Formation flight experiments are set up in Vicon motion-capture environment and the flight results demonstrate the effectiveness of the proposed formation flight architecture.  相似文献   

6.
This paper addresses an output tracking problem for discrete-time high-order fully actuated (DHOFA) systems and its application in the control of air-bearing spacecraft (ABS) simulator. A HOFA system model, as a novel system representation, is applied to establish the dynamics of discrete-time control systems. Accordingly, a HOFA predictive control scheme is presented to deal with this problem, which is composed of a HOFA feedback for stabilization and a HOFA predictive control for tracking. In this scheme, a Diophantine equation is exploited to construct an incremental HOFA (IHOFA) prediction model to substitute a reduced-order prediction model, and then a cost function involving tracking performance is minimized by using multi-step output predictions. A sufficient and necessary condition is proposed to discuss the stability and tracking performance of the closed-loop DHOFA systems, it is simple to utilize in system analysis and extend in practice. Two experiments of the control of ABS simulator are shown to illustrate the feasibility of the presented HOFA predictive control scheme.  相似文献   

7.
This paper addresses the issue of resilient control in the presence of denial-of-service (DoS) attacks for a class of cyber-physical systems. The primary objective is to design a static output feedback controller and event-triggered condition simultaneously such that the globally exponential stability of the closed-loop system is ensured. Compared with stepwise techniques, the co-design achieves the trade-off between control performance and communication cost. The control co-design process is formulated as a bilinear matrix inequality (BMI) problem, which involves nonlinear terms. A successive convex optimization approach is proposed to solve the BMI problem. Further, we develop a self-triggered communication scheme to reduce the cost caused by continuous event detection. It is shown that the proposed event/self-triggered strategy is Zeno-free and excludes singular triggering. Finally, a numerical example is presented to demonstrate the validity of the proposed method.  相似文献   

8.
Many control problems in process systems feature multi-objective optimization problems that involve several and often conflicting objective functions, such as economic profit and environmental concerns. In this paper, we consider a class of multi-objective model predictive control (MO-MPC) problems where nonlinear systems are subject to state and control constraints and multiple economic criteria are conflicting. Using the lexicographic optimization, we propose a prioritized MO-MPC scheme with guaranteed stability for economic optimization. At each sampling time, the MPC action is computed by solving a set of sequentially ordered single objective optimized control problems. Some sufficient conditions are established to ensure recursive feasibility and asymptotic stability of the MO-MPC in the context of economic criteria optimization. Two examples of multi-objective control of a coupled-tank system and a free-radical polymerization process are exploited to illustrate the effectiveness of the proposed MPC scheme and to evaluate the performance by some comparison experiments.  相似文献   

9.
This paper studies the event-triggered model predictive control (MPC) of a stabilizable linear continuous-time system. The optimization problem associated with the proposed MPC strategy is formulated exploiting newly designed control constraints. Compared with the conventional tube-based MPC, where the constant tightened control constraints are employed, the proposed MPC strategy exploits the time-varying control constraints, which allows the control signal to take larger values in the beginning along the prediction horizon, resulting in potentially improved system performance. The re-computation of the control signal is triggered by the deviation of the predicted system state and the real system state. Furthermore, conditions are derived based on which the design parameters can be tuned to ensure the recursive feasibility of the optimization and the stability of the closed-loop system. Finally, the effectiveness of the proposed MPC strategy is verified using a numerical example.  相似文献   

10.
In this paper, the pinning synchronization of delayed complex networks (DCNs) is investigated under self-triggered control (STC). The framework of synchronization analysis of DCNs under STC is established. Specifically, a new dynamic event-triggered scheme (DETS) is proposed for the DCNs firstly. The scheme concerns internal dynamic variables, which plays a crucial role in ensuring the exclusion of Zeno behavior. Secondly, to avoid continuous monitoring triggering condition, an effective self-triggered scheme (STS) is proposed. Different from the previous works, based on the extended Grönwall inequality, the lower bound for the inter-event time of the STS is estimated explicitly. A numerical example is provided to demonstrate the effectiveness of the theoretical results.  相似文献   

11.
In this paper, a two-layer model predictive control (MPC) hierarchical architecture of dynamic economic optimization (DEO) and reference tracking (RT) is proposed for non-Gaussian stochastic process in the framework of statistical information. In the upper layer, with state feedback and dynamic economic information, the economically optimal trajectories are estimated by entropy and mean based dynamic economic MPC, which uses the nonlinear dynamic model instead of the steady-state model. These estimated optimal trajectories from the upper layer are then employed as the reference trajectories of the lower layer control system. A survival information potential based MPC algorithm is used to maintain the controlled variables at their reference trajectories in the nonlinear system with non-Gaussian disturbances. The stability condition of closed-loop system dynamics is proved using the statistical linearization method. Finally, a numerical example and a continuous stirred-tank reactor are used to illustrate the merits of the proposed economic optimization and control method.  相似文献   

12.
In this paper, a method is proposed to reject disturbances in the model predictive control (MPC) strategy. In addition, uncertainties in the system parameters (i.e., internal disturbances) are considered as well. To achieve these goals, adaptive neural networks are designed as the predictor model and as the nonlinear disturbance observer, respectively. The disturbances are rejected via the optimization problem of the MPC. Stability of the closed-loop system is studied based on the Input-to-State Stability method. The proposed method is applied to the pH neutralization process and CSTR system and its effectiveness in optimal rejection of the disturbances and satisfying the system constrains is compared with the feed-forward control method.  相似文献   

13.
In this work, a new design method of model predictive control (MPC) is proposed for uncertain systems with input constraints. By using a new method to deal with actuator constraints, our method can reduce the conservativeness. For the design of the robust MPC controllers, a sequence of feedback control laws is used and a parameter-dependent Lyapunov function is chosen to further reduce the conservativeness. The effectiveness and performance of our MPC design method are demonstrated by an example.  相似文献   

14.
This paper proposes a novel MPC approach called conditional scenario-based model predictive control (CSB-MPC), developed for discrete-time linear systems affected by parametric uncertainties and/or additive disturbances, which are correlated and with bounded support. At each control period, a primary set of equiprobable scenarios is generated and subsequently approximated to a new reduced set of conditional scenarios in which each has its respective probabilities of occurrence. This new set is considered for solving an optimal control problem in whose cost function the predicted states and inputs are penalised according to the probabilities associated with the uncertainties on which they depend in order to give more importance to predictions that involve realisations with a higher probability of occurrence. The performances of this new approach and those of a standard scenario-based MPC are compared through two numerical examples, and the results show greater probabilities of not transgressing the state constraints by the former, even when considering a smaller number of scenarios than the scenario-based MPC.  相似文献   

15.
The main results of this paper are concentrated on the nonlinear model predictive control (MPC) tracking optimization based on high-order fully actuated (HOFA) system approaches. The proposed HOFA MPC strategy makes full use of full-actuation property to eliminate the nonlinear dynamics of the system, and then the nonlinear optimization problem is equivalently transformed into a series of easy-solve linear convex optimization problems. Different from general nonlinear MPC methods and the current optimal control of the HOFA system approach, an analytical controller with smooth and less energy is obtained by the moving horizon optimization. And it is proven that the proposed controller can stabilize the corresponding tracking error closed-loop system. Finally, not limited to FA systems, as examples, a nonlinear numerical under-actuated model in the mathematical sense and a benchmark nonlinear under-actuated mechanical system are transformed into corresponding equivalent HOFA systems, the simulation results are given to verify the effectiveness of the proposed strategy.  相似文献   

16.
In this paper, we formulate and study a reliability-performance balancing problem (RPBP) for long-term operational and unattended control systems with degrading actuators. It preliminarily explores a new type of autonomous maintenance method to extend the useful lifetime of the control system. The actuator, as the crucial component of a control system, executes calculated control actions and thereby is often exposed to the high-load working environment. As the actuator degrades, the control action will gradually alter with increasing magnitude to maintain the desired control performance, but this will accelerate the actuator degradation and thus reduce the useful lifetime (use reliability) of the control system. Therefore, conditionally balancing the control performance and use reliability is meaningful, for which a novel dynamic regulation strategy under the model predictive control (MPC) framework is proposed. Specifically, we model the actuator degradation using a diffusion Wiener process coupled with the control action or system state, and the corresponding actuator reliability is derived. By fusing the degradation model and system dynamics, a degradation-incorporated state space (DISS) model is formulated, in which the basic idea is to consider the actuator degradation as an extended state variable and to control it accordingly. Based on the DISS model, a mixed-index nonlinear MPC integrated with a weight tuning strategy is proposed to achieve a satisfactory balance between control performance and use reliability in the presence of actuator degradation. Further, the reference curve and the upper bound of actuator degradation are given for constructing the objective function and the constraint in the MPC optimization problem. An illustrative example is presented to demonstrate the availability of the proposed method.  相似文献   

17.
In this paper, the scaled consensus of resource-limited multi-agent systems with second-order integrator dynamics and undirected topologies is investigated. In order to reduce bandwidth and computation requirements, a scaled consensus protocol based on periodic edge-event driven control is proposed. It is proven that all the agents could converge to a scaled consensus state while the interaction topology is connected. Moreover, a self-triggered scheme is proposed so as to further reduce communication times between agents. Notably, the event-detecting period is introduced so that Zeno behavior could be excluded in our model. Finally, simulations are given to demonstrate the effectiveness of our theoretical results.  相似文献   

18.
This paper studies the consensus problem of multi-agent systems by event- and self-triggered control. An event-triggered algorithm with periodic event detection and relative state measurements is designed to determine all event times. Furthermore, a self-triggered control algorithm with periodic event detection and quantization is proposed to further reduce resource consumption. In the proposed control strategies, only relative state information, or called edge information, is utilized by controllers, and all edge state information incident to each agent is processed together. The results are illustrated by two numerical experiments.  相似文献   

19.
In this paper, the problem of observer-based model predictive control (MPC) for a multi-channel cyber-physical system (CPS) with uncertainties and hybrid attacks is investigated via interval type-2 Takagi-Sugeno (IT2 T-S) fuzzy model. Both denial-of-service (DoS) and false data injection (FDI) attacks are studied due to the vulnerability of wireless transmission channels. The objective of the addressed problem is to improve the security performance of the multi-channel CPS under malicious attacks, which has not been adequately investigated in the existing MPC algorithms. Moreover, uncertainties which appear not only in the membership functions but in both state and input matrices are considered. In this paper, different from the method that FDI attacks are handled by the bounded functions, an off-line observer is designed to actively defend against the FDI attacks. Meanwhile, an on-line MPC optimization algorithm, which minimizes the upper bound of objective function respecting input constraints, is presented to obtain the secure controller gains. Finally, an illustrative example is provided to verify the effectiveness and superiority of presented approach.  相似文献   

20.
In this paper, a stable model predictive control approach is proposed for constrained highly nonlinear systems. The technique is a modification of the multistep Newton-type control strategy, which was introduced by Li and Biegler. The proposed control technique is applied on a constrained highly nonlinear aerodynamic test bed, the twin rotor MIMO system (TRMS) to show the efficacy of the control technique. Since the accuracy of the plant model is vital in MPC techniques, the nonlinear state space equations of the system are derived considering all possible effective components. The nonlinear model is adaptively linearized during the prediction horizon. The linearized models of the system are employed to form a linear quadratic objective function subject to a set of inequality constraints due to the system input/output limits. The stability of the control system is guaranteed using the terminal equality constraints technique. The satisfactory performance of the proposed control algorithm on the TRMS validates the effectiveness and the reliability of the approach.  相似文献   

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