Self-Triggered Model Predictive Control for Continue Linear Constrained System: Robustness and Stability

This paper investigates the self-triggered model predictive control (MPC) with integral sliding mode method (ISM) of networked nonlinear continuous-time system subject to state and input constraints with additive disturbances and uncertainties. In the proposed scheme, the constrained optimization problem is solved aperiodically to generate control signals and the next execution time, leading to possible reductions in both computation and communication. The motivation of using ISM approach is to reject matched uncertainties. First, a self-triggered condition that involves comparing the cost function values with different execution periods is derived. Second, the robust MPC with ISM control strategy is rigorously studied depending on the self-triggered scheme. Finally, a numerical example is used to test the proposed algorithm and to verify our theoretical findings.

The model predictive control (MPC) of Cyber-Physical-Systems (CPSs) suffer from the drawbacks of heavy communication and computation cost. This paper studies the self-triggered model predictive control (STMPC) problem for continuous-time nonlinear systems subject to system constraints under cyber-physical scenarios. A novel STMPC strategy is proposed which allows adaptively to select sampling number during two triggering time instants, and new self-triggered condition is designed. Furthermore, the feasibility of the designed algorithm and the closed-loop stability is theoretically ensured. We show that the closed-loop system is asymptotically stable under the proposed self-triggered strategy. Finally, a simulation example is provided to verify the effectiveness of the proposed algorithm.

This paper investigates the self-triggered model predictive control for networked linear systems. The self-triggered mechanism is designed based on switched cost functions, which can be used to enhance systems performance and are more appropriate for complex industrial requirements in contrast with a single cost function approach. The model predictive controller is designed by solving an optimization problem and the self-triggered condition is designed. With the proposed self-triggered mechanism, much network computation and communication burden are reduced and Zeno behavior can be excluded. Moreover, asymptotic stability of the networked systems with model predictive control strategy is shown via average dwell-time technique. Finally, the numerical simulation example is given to illustrate the effectiveness of the proposed methods.

In this paper, the cooperative stabilization problem of linear time-invariant (LTI) systems is studied based on a class of distributed Luenberger observers. A given LTI system is estimated by a network of observers. In case of observability configuration or external attack, each observer has to possess different measurement output of the LTI system. Classical distributed control strategies cannot be directly used because they are assumed to obtain identical information. In view of this problem, a class of distributed observers are designed under an undirected graph, which can recover the state of the LTI system by limited local information. By using any single channel of the estimated state, a control input is proposed to stabilize the LTI system. By analysing the stability of an augmented system which involves the LTI system and the estimation error system, some necessary and sufficient conditions are derived for the existence of control gains that guarantee the stabilization of the LTI system. In addition, some algorithms are proposed to specify the control gains. Lastly, some simulation examples are given to verify the effectiveness of the theoretical result.

This study investigates characteristic root equivalency relations between commensurate order and integer order Linear Time Invariant (LTI) systems. Author introduces some useful properties of a special class of commensurate order systems, which is called characteristic root equivalency class of LTI systems. These properties present potential to facilitate design and analysis efforts of this class of commensurate order systems. In this sense, straightforward stability checking procedures and design approaches for commensurate order root equivalent systems of the first and second order LTI systems are demonstrated. Findings of the study are validated by illustrative examples.

In this paper, the constrained control of systems evolving on matrix Lie groups with uncertainties is considered. The proposed methodology is composed of a nominal Model Predictive Control (MPC), and a feedback controller. The previous work on the control of systems on manifolds is applied to design the nominal MPC, which generates the nominal trajectory. In the nominal MPC, the state and input constraints on the Lie group are transformed into the constraints on the Euclidean space. While to deal with uncertainties, the feedback control used to track the nominal trajectory is designed directly on the Lie group. The tracking error in the feedback control is proved to be bounded in invariant sets, which are further used to revise the constraints in nominal MPC. We prove that the input‐to‐state stability of the entire system under the proposed control methodology with respect to the disturbances can be achieved. The proposed methodology is applied to the constrained attitude control of rigid bodies with uncertainties. In the application example, the detailed mathematical proof and the comparative numerical simulation are presented to demonstrate the feasibility of the proposed methodology.

Receding horizon control (RHC) or model predictive control (MPC) solves online a finite horizon open-loop optimal control problem repeatedly in an infinite horizon context and provides a suboptimal control solution. It has been widely used in industry. For continuous-time (CT) systems, two categories of RHC have been investigated in literature, namely instantaneous RHC and sampled-data RHC. This paper focuses on the sampled-data RHC for continuous linear time-invariant (LTI) systems. The usual way to ensure stability of RHC scheme is to impose some constraints on the terminal cost and/or the terminal state and/or the horizon length. A new RHC algorithm is proposed, which we call updated terminal cost receding horizon control (UTC-RHC). Compared with the standard RHC which uses the same terminal cost all the time, our algorithm updates the terminal cost at each sampling instant. The advantage of UTC-RHC is that the uniform exponential stability of the closed-loop system can be guaranteed without imposing the usual constraints on the terminal state or the terminal cost or the horizon size. Moreover, the UTC-RHC control gain approaches the optimal value associated with the infinite horizon optimal control problems.

Two approaches to control system design for constrained systems are studied. The first involves theoretical investigations of constrained model predictive control algorithms. The second involves extensions of robust linear control theory to handle the nonlinear control schemes commonly used in practice for constrained systems. A novel model predictive control algorithm, with attractive functional and numerical characteristics is developed. This algorithm minimizes peak excursions in the controlled outputs and is particularly suited to regulatory control problems common in continuous process systems. Model predictive control concepts are extended to uncertain linear systems. An on-line optimizing control scheme (RMPC) is developed which has as its objective the minimization of worst-case tracking error for an entire family of linear plants. For model uncertainty descriptions which provide plant impulse response coefficients as affine functions of uncertain parameters, it is shown that the required minimax optimization problem can be recast as a single linear program. The discrete time optimal averaging level control problem is formulated and solved. A finite horizon approximation to the problem is introduced and analytical solutions are obtained in important special cases. A model predictive control formulation is introduced which provides optimal flow filtering and integral action. Analysis tools are provided to characterize the trade-off between flow filtering and rapid integral action. A complete theory is developed for the multivariable anti-windup, bumpless transfer (AWBT) problem. The theoretical framework allows the consideration of any linear time invariant (LTI) control system subject to plant input limitations and substitutions. A general AWBT compensation scheme, applicable to multivariable controllers of arbitrary structure and order, is developed. Conditions are derived under which this general AWBT method reduces to any one of several well-known heuristics for AWBT (e.g., PI anti-reset windup and IMC). The design issues which affect AWBT performance are identified and quantitative analysis methods are developed. Sufficient conditions for nonlinear stability of the AWBT compensated system are provided. These results are a generalization of, and are less conservative than, those available in the AWBT literature. The definition of AWBT performance objectives which are independent of controller structure allows the formulation of a general AWBT synthesis problem. This formal synthesiproblem addresses each of the identified performance objectives in a quantitative manner. The synthesis problem is shown to be a special case of a constrained structure controller synthesis (CSCS) problem. A solution method via reduction to static output feedback is presented and the engineering trade-offs available in the AWBT design are discussed.

In this paper we present a robust self-triggered model predictive control (MPC) scheme for discrete-time linear time-invariant systems subject to input and state constraints and additive disturbances. In self-triggered model predictive control, at every sampling instant an optimization problem based on the current state of the system is solved in order to determine the input applied to the system until the next sampling instant, as well as the next sampling instant itself. This leads to inter-sampling times that depend on the trajectory of the system. By maximizing the inter-sampling time, the amount of communication in the control system is reduced. In order to guarantee robust constraint satisfaction, Tube MPC methods are employed. Specifically, in order to account for the uncertainty in the system, homothetic sets are used in the prediction of the future evolution of the system. The proposed controller is shown to stabilize a closed and bounded set including the origin in its interior.

A robust stability framework for LTI systems with time-varying sampling

Frequency-domain stability conditions for asynchronously sampled decentralized LTI systems

In this paper, we investigate two classes of consensus protocols for networked multi-agent systems: linear time-invariant (LTI) systems and linear time-delay systems. Based on the topology of multi-agent systems, the first-order integrator model is developed. The digraph (directed graph) is employed to show the topology of networked systems, and then a consensus convergence criterion is established. For LTI systems, we prove that their consensus value will converge globally asymptotically to the convex hull of initial states. By solving a set of linear equations, we get the convex combinations of equilibria, and we obtain the convex value of continuous system. If the topology is fixed and time-invariant, the consensus value of the linear time-delay system is also the convex hull of the initial states and is identical to the LTI system. Finally, a network of six agents is presented to show the effectiveness of the results of this paper.

The paper deals with the identification of linear time invariant (LTI) systems by a special observer. An observer emitting an frequency modulated continuous wave (FMCW) signal and having a stretch processor as receiver will be considered for system identification. A thorough derivation of the gathered baseband signal for arbitrary LTI systems will be given. It is shown, that the received signal is approximately given by the transfer function of the LTI system over the frequency sweep of the FMCW signal. The proof relies on an infinite large time-bandwidth product of the transmit signal, such that errors remain in practical applications with a finite time-bandwidth product. Monte–Carlo simulations are conducted to verify the approximation and to quantify its accuracy and remaining errors. The findings are important for e.g. calibration or derivation of a device model in FMCW radar applications.

Chapter 4 - Frequency Analysis: The Fourier Series

Finite-time stabilization of linear systems by bounded event-triggered and self-triggered control