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1.
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   

2.
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   

3.
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   

4.
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   

5.
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   

6.
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   

7.
Self-driving vehicles must be equipped with path tracking capability to enable automatic and accurate identification of the reference path. Model Predictive Controller (MPC) is an optimal control method that has received considerable attention for path tracking, attributed to its ability to handle control problems with multiple constraints. However, if the data acquired for determining the reference path is contaminated by non-Gaussian noise and outliers, the tracking performance of MPC would degrades significantly. To this end, Correntropy-based MPC (CMPC) is proposed in this paper to address the issue. Different from the conventional MPC model, the objective of CMPC is constructed using the robust metric Maximum Correntropy Criterion (MCC) to transform the optimization problem of MPC to a non-concave problem with multiple constraints, which is then solved by the Block Coordinate Update (BCU) framework. To find the solution efficiently, the linear inequality constraints of CMPC are relaxed as a penalty term. Furthermore, an iterative algorithm based on Fenchel Conjugate (FC) and the BCU framework is proposed to solve the relaxed optimization problem. It is shown that both objective sequential convergence and iterate sequence convergence are satisfied by the proposed algorithm. Simulation results generated by CarSim show that the proposed CMPC has better performance than conventional MPC in path tracking when noise and outliers exist.  相似文献   

8.
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   

9.
The high-performance control requires the system to be stable, fast and accurate simultaneously. However, various systems (e.g., motors, industrial robots) generally face technical challenges such as nonlinearities, uncertainties, external disturbances and physical constraints, which make it difficult to reach the hardware potential of the systems to track the desired trajectories when satisfying the high-performance control requirements. Therefore, take a two-order nonlinear system for example, an optimization-based adaptive neural sliding mode control based on a two-loop control structure is proposed in this paper, where the outer and inner loops are designed separately to achieve different control requirements. Namely, the outer loop is designed as a model predictive control (MPC)-based optimization problem, which can optimize the desired trajectories to meet the state and input constraints, and maximize the converging speed of transient response as fast as possible, and the inner loop is designed with a recurrent neural network (RNN)-based adaptive neural sliding mode controller, which can guarantee the tracking of the replanned desired trajectories from outer loop as accurate as possible. The stability of the system is guaranteed by Lyapunov theorem, the optimal tracking performance is achieved under nonlinearities, uncertainties, external disturbances and physical constraints, and comparative simulation with a motor system is carried out to verify the effectiveness and superiority of the proposed approach.  相似文献   

10.
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   

11.
In this paper, a compound control strategy is proposed to realize the trajectory tracking task of quadrotors under operating constraints and disturbances. Disturbances caused by model uncertainties, environmental noises, and measurement disturbances are divided into matched disturbances and unmatched ones, which are compensated and suppressed separately by using two control components. The integral sliding mode control component is designed to actively reject the matched disturbances, and the control system is then transformed into an equivalent control system subject to equivalent disturbances only related to the unmatched disturbances. The remaining equivalent disturbances are treated by a robust model predictive control component based on the idea of constraints tightening, which minimizes the tracking error in an optimization framework and takes both state and input constraints into account explicitly. The derived compound control strategy is based on these two control components. Conditions are provided to guarantee the robust constraint satisfaction, recursive feasibility and closed-loop stability of the tracking error system. An illustrative example on the quadrotors shows the efficiency and robustness of this compound tracking control algorithm.  相似文献   

12.
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   

13.
This paper addresses the design of a sampled-data model predictive control (MPC) strategy for linear parameter-varying (LPV) systems. A continuous-time prediction model, which takes into account that the samples are not necessarily periodic and that plant parameters vary continuously with time, is considered. Moreover, it is explicitly assumed that the value of the parameters used to compute the optimal control sequence is measured only at the sampling instants. The MPC approach proposed by Kothare et al. [1], where the basic idea consists in solving an infinite horizon guaranteed cost control problem at each sampling time using linear matrix inequalities (LMI) based formulations, is adopted. In this context, conditions for computing a sampled-data stabilizing LPV control law that provides a guaranteed cost for a quadratic performance criterion under input saturation are derived. These conditions are obtained from a parameter-dependent looped-functional and a parameter-dependent generalized sector condition. A strategy that consists in solving convex optimization problems in a receding horizon policy is therefore proposed. It is shown that the proposed strategy guarantees the feasibility of the optimization problem at each step and leads to the asymptotic stability of the origin. The conservatism reduction provided by the proposed results, with respect to similar ones in the literature, is illustrated through numerical examples.  相似文献   

14.
This paper presents a robust quasi-min–max model predictive control algorithm for a class of nonlinear systems described by linear parameter varying (LPV) systems subject to input constraints and unknown but bounded disturbances. The proposed control algorithm solves a semi-definite programming problem that explicitly incorporates a finite horizon cost function and linear matrix inequalities (LMI) constraints. For the purpose of the recursive feasibility of the optimization, the dual-mode approach is implied. Input-to-state stability (ISS) and quasi-min–max MPC are combined to achieve the closed-loop ISS of the controller with respect to the disturbance in LMI paradigm. Two examples of continuous stirred tank reactor (CSTR) and couple-mass-spring system are used to demonstrate the effectiveness of the proposed results.  相似文献   

15.
In this paper, a self-triggered model predictive control (MPC) strategy is developed for discrete-time semi-Markov jump linear systems to achieve a desired finite-time performance. To obtain the multi-step predictive states when system mode jumping is subject to the semi-Markov chain, the concept of multi-step semi-Markov kernel is addressed. Meanwhile, a self-triggered scheme is formulated to predict sampling instants automatically and to reduce the computational burden of the on-line solving of MPC. Furthermore, the co-design of the self-triggered scheme and the MPC approach is adjusted to design the control input when keeping the state trajectories within a pre-specified bound over a given time interval. Finally, a numerical example and a population ecological system are introduced to evaluate the effectiveness of the proposed control.  相似文献   

16.
This paper solves the problem of adaptive neural dynamic surface control (DSC) for a class of full state constrained stochastic nonlinear systems with unmodeled dynamics. The concept of the state constraints in probability is first proposed and applied to the stability analysis of the system. The full state constrained stochastic nonlinear system is transformed to the system without state constraints through a nonlinear mapping. The unmodeled dynamics is dealt with by introducing a dynamic signal and the adaptive neural dynamic surface control method is explored for the transformed system. It is proved that all signals of the closed-loop system are bounded in probability and the error signals are semi-globally uniformly ultimately bounded(SGUUB) in mean square or the sense of four-moment. At the same time, the full state constraints are not violated in probability. The validity of the proposed control scheme is demonstrated through the simulation examples.  相似文献   

17.
The optimal control strategy constructed in the form of a state feedback is effective for small state perturbations caused by changes in modeling uncertainty. In this paper, we investigate a robust suboptimal feedback control (RSPFC) problem governed by a nonlinear time-delayed switched system with uncertain time delay arising in a 1,3-propanediol (1,3-PD) microbial fed-batch process. The feedback control strategy is designed based on the radial basis function to balance the two (possibly competing) objectives: (i) the system performance (concentration of 1,3-PD at the terminal time of the fermentation) is to be optimal; and (ii) the system sensitivity (the system performance with respect to the uncertainty of the time-delay) is to be minimized. The RSPFC problem is subject to the continuous state inequality constraints. An exact penalty method and a novel time scaling transformation approach are used to transform the RSPFC problem into the one subject only to box constraints. The resulting problem is solved by a hybrid optimization algorithm based on a filled function method and a gradient-based algorithm. Numerical results are given to verify the effectiveness of the developed hybrid optimization algorithm.  相似文献   

18.
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   

19.
This paper is concerned with the adaptive control problem for a class of linear discrete-time systems with unknown parameters based on the distributed model predictive control (MPC) method. Instead of using the system state, the state estimate is employed to model the distributed state estimation system. In this way, the system state does not have to be measurable. Furthermore, in order to improve the system performance, both the output error and its estimation are considered. Moreover, a novel Lyapunov functional, comprised of a series of distributed traces of estimation errors and their transposes, has been presented. Then, sufficient conditions are obtained to guarantee the exponential ultimate boundedness of the system as well as the asymptotic stability of the error system by solving a nonlinear programming (NP) problem subject to input constraints. Finally, the simulation examples is given to illustrate the effectiveness and the validity of the proposed technique.  相似文献   

20.
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   

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