European Journal of Control最新文献

This paper is devoted to the design of adaptive nonlinear prescribed performance controllers for balancing constraint and robustness. Firstly, a shift function is introduced to enhance robustness in the initial stage. Secondly, a new finite-time performance function and a regulation of dynamic adjustments are developed for the first time. Due to the regulation, it can be prevented by the proposed scheme that tracking errors approach or exceed performance boundaries during operation, which ensures both robustness and constraint, and the performance function is differentiable of order n, which can be used in backstepping design for higher-order systems without approximators. Finally, the designed controller is more robust in both transient and stable states, whose effectiveness and superiority are validated by stability analysis, matlab simulation, and an actual test on a real robotic arm.

The applications of reference governors to systems with unmeasured set-bounded disturbances can lead to conservative solutions. This conservatism can be reduced by estimating the disturbance from output measurements and canceling it in the nominal control law. In this paper, a reference governor based on such an approach is considered and time-varying, disturbance and state estimation errors bounding sets are derived. Consequently, the traditional implementation of a reference governor, which exploits a constraint admissible positively-invariant set of constant commands and initial states, is replaced by one which utilizes a time-dependent sequence of similar sets (which are not necessary nested). Examples are reported which include two applications to longitudinal control of aircraft that illustrate handling of elevator uncertainty and wing icing.

An adaptive control method based on a fuzzy state observer is designed for air handling unit (AHU) systems, where the intensity of humidity source and heat load are considered to be uncertain, and approximated by fuzzy logics. Besides, the supply air temperature is unmeasurable, which is observed by a state observer. To avoid the system equilibrium point offset, the prediction errors generated by the system and serial-parallel estimation models are introduced into the controller design based on backstepping. Finally, the superiority of proposed adaptive fuzzy controller is analyzed by simulation comparison with feedback linearization and PID controllers in two cases.

We study decentralized derivative-dependent control of large-scale $n$th-order systems with input delays via delayed feedback implementation. The unavailable derivatives can be approximated by finite differences giving rise to a time-delayed feedback. In the centralized case, an efficient simple linear matrix inequalities (LMIs)-based method for designing of such static output-feedback and its sampled-data implementation was recently suggested. In the present paper, we extend this design to large-scale systems in the presence of input delays and disturbed measurements. Under the assumption of the stabilizability of the system with small enough input delays and small enough interactions by a state-feedback that depends on the output and its derivatives, a delayed static output-feedback that stabilizes the system is presented by using the current and past disturbed measurements. To compensate the errors due to the input delays, we add the appropriate terms to the corresponding Lyapunov–Krasovskii functional that lead to LMIs conditions. The efficient bounds on the delays preserving that the resulting system is input-to-state stable (ISS) are found by verifying the LMIs. In addition, we employ the vector Lyapunov functional method that may allow larger couplings compared with the existing method. Finally, the effectiveness of the proposed methods is illustrated by numerical examples.

This paper investigates the issue of active fault-tolerant control for multi-agent systems using an event-triggered mechanism, aiming to achieve state-consistency control under a leader–follower framework. The proposed control protocol consists of two main components: leader input estimation and fault tolerant controller design under an event-triggered mechanism. Firstly, a leader–follower error system is constructed, where the leader’s input is considered as a supplementary vector for the system state, resulting in an augmented error system. Then, a distributed state observer is designed for this augmented error system to transform the leader’s input estimation problem into an augmented state estimation issue. Furthermore, an event-triggered mechanism is designed to optimize network communication resources by updating the control law only when specific triggered conditions are met. Next, taking into account the errors introduced by the distributed observer and event-triggered mechanism, the problem of determining the state error feedback gain matrix is transformed into a linear matrix inequality problem using $H_{\infty }$ robustness design and Lyapunov stability analysis methods. Finally, the effectiveness of this theoretical framework is validated through a practical example involving several VTOLs.

In this paper, we study the optimal control problem for the state governed by stochastic differential equation with delay and partially observed by a noisy process. Some variational inequalities and a necessary condition for optimality are established. Meanwhile, we introduce two kinds of adjoint equation which are shown to be equivalent. As an application, a linear–quadratic system and a financial problem are presented to demonstrate our results. In particular, its numerical simulation and some figures are used to illustrate the effect of delay on optimal solutions.

A new low-order dynamic model is proposed to predict the nonlinear heat transfer in heat exchangers, using a modeling approach that does not require detailed information about the flow arrangement. On the primary side, a model of a throttling valve is added to include the control signal’s influence on the secondary side’s output temperature. The model does not consider time delays but uses variable time constants that depend on the mass flow rates. A two-step procedure is proposed to estimate the model parameters: first, the complete parameter vector is estimated to solve a parameter estimation problem. Then, a subset of the estimated parameters is tuned online using an Unscented Kalman Filter to fit the model further to reality. The accuracy of the model as well as the implementation of the parameter estimation are demonstrated using an example from practice.

In this paper, we consider a linear time-invariant discrete-time system and study the output null controllability problem, i.e., the problem of steering the output to zero in a finite number of steps. We assume that we only know the structure of the system, i.e., the zero/nonzero location in the system matrices. Hence, we consider a structural version of the output null controllability problem. We represent the structure of the system by means of a directed graph and present a graph theoretic sufficient condition for the problem to be generically solvable. Here generically solvable means that the problem is solvable for almost all systems with the same structure. We illustrate the conditions using an example.

In this study, we present the Indirect Bang-Singular Algorithm (IBSA), a straightforward computational framework developed to solve a wide range of Singular Optimal Control Problems (SOCP) with state-inequality constraints. The algorithm is a type of arc-classification technique which reformulates the SOCP as a nonlinear programming problem over the switching times, and possibly some other parameters such as the co-states’ initial values at the entry time to a singular interval. We derive the singular control feedback using the Pontryagin’s maximum principle and analyze the possibility of an interval where multiple controls are simultaneously singular. Furthermore, we incorporate the state-inequality constraints using the direct-adjoining method. Owing to the linear property of the co-state dynamics, the co-state variables and consequently, the singular controls are computed automatically using MATLAB’s symbolic platform. The nonlinear programming is constructed in a manner to circumvent the challenges posed by state-inequality constraints in more intricate scenarios involving singular controls expressed in terms of incomplete state-feedback functions. We also present several theorems that are integral to devising a straightforward computational approach for solving SOCPs. To assess the effectiveness of the proposed algorithm, we solve the following novel problems: (1) time–fuel-optimal commercial aircraft cruise flight in a vertical plane (i.e., with state-inequality constraint, a scalar singular control, and wind shear effects), and (2) the free-routing time–fuel-optimal commercial aircraft flight in a vertical plane (i.e., with state-inequality constraint, a dual-entry singular control, and wind shear effects). Notably, the optimality of the graphed results has been carefully inspected through first and second-order optimality conditions.

Exponential stability of system with time-varying delay is studied in this paper. By taking intermediate polynomials and slack variables, we propose novel matrix-refined weighted functions (MRWFs). Then, based on the MRWFs, we construct a suitable Lyapunov-Krasovskii functional (LKF) and propose an exponential stability condition. Finally, numerical examples verify that the stability condition proposed in this paper is more effective, which is less conservative than some past criteria.