Output-feedback Predictive Control Research Articles

Robust dynamic output feedback predictive control for discrete uncertain systems with time-varying delays

A robust dynamic output feedback predictive control approach is developed for a discrete system with time-varying delays, unknown external disturbances, and unmeasurable states. First, the discrete system is transformed into an incremental state deviation model. Based on this model, a novel tracking deviation feedback model is established by extending the output tracking error. Then, a robust predictive control law, possessing more degrees of freedom, is designed. The closed-loop model is further given in conjunction with the feedback model. Second, by using the linear matrix inequality (LMI) method, relaxation technique, and variable transformation method, a less conservative stability condition is given in LMI form, which allows the controller to tolerate a greater range of time-varying delays. The gains of the control law are acquired by solving the stability condition, and the control performance can be significantly enhanced. Finally, by utilizing the TTS20 water tank as a simulation case, the viability and effectiveness of the proposed method are demonstrated.

Event‐triggered output feedback predictive control for Takagi‐Sugeno model with bounded disturbance and redundant channels

AbstractIn this paper, the problem of dynamic output feedback model predictive control (MPC) for the Takagi‐Sugeno (T‐S) model with bounded disturbance and redundant channels is investigated based on an event‐triggered scheme. The redundant channels, described by a set of mutually independent Bernoulli processes, are applied to improve the reliability of data transmission. The state immeasurable is considered, and an event‐triggered state estimator with a time‐varying threshold is designed to estimate the true system state. The estimator and controller gains are parameterized as a non‐convex optimization problem. In order to online solve the resulting optimization problem, the cone complementarity approach (CCA) with iteration optimization is adopted. The mean‐square quadratic boundedness (MSQB) is utilized to characterize the invariance of the system state and guarantee the closed‐loop stability, while the recursive feasibility is ensured via online refreshing the estimation error set. Finally, a numerical simulation example is presented to illustrate the effectiveness of the proposed theoretical method.

Robust Output-feedback Predictive Control for Proximity Eddy Current De-tumbling with Constraints and Uncertainty

Proximity operation can significantly improve the efficiency of eddy current de-tumbling. However, the tumbling motion and non-cooperation of space debris make the chaser execute collision avoidance maneuvers and be influenced by model uncertainty. In this paper, an inertial-oriented safety corridor is proposed by taking the debris' angular momentum as the central axis, which can avoid the frequent collision maneuvers of the chaser. Meanwhile, a desired de-tumbling trajectory under this safety corridor is designed to de-tumble the angular velocity of space debris. Then, a robust output-feedback controller considering safety corridor and model uncertainty is proposed by combining moving horizon estimation and model predictive control. The moving horizon estimation is employed to estimate the system state and model uncertainty which is compensated by a feedforward control law. Furthermore, the model predictive control without terminal ingredients is designed to realize the optimal performance of fuel consumption and the robust tracking stability of the system. Finally, taking the Chinese Sinosat-2 satellite as the simulation case, the effectiveness of the proposed scheme is verified.

Coordinated control of quasilinear multiagent systems via output feedback predictive control

This study considers the coordinated control for quasilinear multiagent systems (QMASs). An output feedback predictive control (OFPC) strategy is given to implement both coordination and simultaneous stability and output consensus (SSOC). In the OFPC strategy, a cost function aiming at coordination relationship is minimized by predictive control thus coordination among QMASs is implemented. Further discussion derives a criterion to maintain the closed-loop QMASs realize the SSOC. Finally, two examples are proposed to richly illustrate the availability of the OPFC strategy.

Output feedback predictive control for discrete quasilinear systems with application to spacecraft flying‐around

AbstractThis paper considers a spacecraft flying‐around problem modeled by discrete quasilinear system under sight coordinate, which can be regarded as an output tracking for discrete quasilinear system. An output feedback predictive control scheme is presented to deal with the flying‐around task. This scheme contains the design of output feedback and the optimization of tracking performance. On the one hand, the completely parametric form of output feedback based on generalized Sylvester equation is developed to stabilize the closed‐loop nominal system. On the other hand, a cost function, aimed to measure tracking performance, is optimized by the design of predictive control such that tracking performance is ensured. Further analysis proposes a necessary and sufficient condition based on Lyapunov method to maintain both bounded stability and tracking performance of the closed‐loop system caused by output feedback predictive control. Compared with the existing method, a numerical simulation is given to richly demonstrate the effectiveness and feasibility of the proposed control scheme.

Hybrid adaptive observer-based output feedback predictive control for the alternating activated sludge process

In this paper, an adaptive observer-based predictive controller is designed for the alternating activated sludge process which represents a nonlinear hybrid system. Precisely, our objective is to control the dissolved oxygen concentration during the aerobic phase. First, a hybrid adaptive observer is designed to estimate conjointly the unmeasured state (the ammonia concentration) and the unknown parameter (the coefficient of performance of heterotrophic biomass). Then the estimated signals are used in the output feedback predictive control law. The convergence of the state estimation, parameter reconstruction and tracking control errors are established through a Lyapunov stability analysis. Numerical simulations are dedicated to highlight the good performances of the developed output feedback control approach.

Static Output Feedback Predictive Control for Cyber-Physical System under Denial of Service Attacks

This paper addresses the static output feedback predictive (SOFP) control problem with cyber-physical system (CPS) subject to Denial-of-Service (DoS) attacks. The effects of DoS attacks are reasonably assumed to the bounded consecutive packet dropouts by considering the energy constraints of an attacker. Then, a novel predictive control sequence, in which only the latest successfully received output is employed, is designed to compensate such packet dropouts caused by DoS attacks. Furthermore, the stability criterion and predictive control design are carefully derived by using the switching Lyapunov functional approach and linear matrix inequality. Compared with the previous works, the proposed predictive control strategy can compensate arbitrary packet dropouts under DoS attacks while only the latest successfully received output is available. At last, a simulation example illustrates the effectiveness of the SOFP control strategy.

Robust output feedback predictive controller with adaptive invariant tubes and observer gains

This work aims to develop a robust output feedback predictive controller that can guarantee the recursive feasibility and the system stability as the invariant tubes and the observer gains can be updated at each sampling time. The recursive feasibility is guaranteed by using the invariant tubes with non-increasing size so the satisfactions of more tightened constraints ensure the satisfactions of less tightened constraints as time proceeds. The system stability is guaranteed by bounding the control error and the estimation error using the invariant tubes that can be updated at each sampling time. The online computational complexity can be reduced as the invariant tubes and the observer gains can be updated at each sampling time. This can be done by computing offline the invariant tubes for the estimation error, the invariant tubes for the control error and the observer gains. The smallest invariant tube containing the estimation error is determined at each sampling time and the corresponding observer gain is chosen as the real-time observer gain. The proposed algorithm can reduce the online computational complexity as compared with the case when the invariant tubes and the observer gains are computed online while the same level of control performance is obtained. In addition, the proposed algorithm can improve the control performance as compared with the case when the invariant tubes and the observer gains are constant.

An Output Feedback Predictive Control for Stochastic Stabilization of a System With Multiple Fixed State Delays and Multiple Markov Input Delays

This paper studies the stochastic stabilization with an output feedback predictive control for a linear system with multiple fixed state delays and multiple Markov input delays. The two equivalent systems are developed through two steps. The original system is transformed to a system with augmented states in a state delay free form. Then, it is further transformed into a system with a predictive state in an input delay free form. The stochastic stability condition of the equivalent system is shown to be a sufficient condition for the stochastic stability of the original system. The stability condition is given as a set of linear matrix inequalities of which matrix size depends only on the maximum state delay, not on the maximum input delay. Simulation results show that the proposed methods provide similar or better performance than the conventional method while their complexities are much smaller in most cases.

The Bilinear Model Predictive Method-Based Motion Control System of an Underactuated Ship with an Uncertain Model in the Disturbance

Ship transportation plays an increasingly important role in and accounts for a large proportion of cargo transport. Therefore, it is necessary to improve the quality of the trajectory control system of the ship for improving the transport efficiency and ensuring maritime safety. This paper deals with the advanced control system for the three-degrees-of-freedom model of the underactuated ship in the condition of uncertain disturbance. Based on the three-degrees-of-freedom model of the underactuated ship, the authors built a bilinear model of the ship by linearizing each nonlinear model section. Then, the authors used the state estimator to compensate for uncertain components and random disturbances in the model. Finally, the authors built the output-feedback predictive controller based on the channel-separation principle combined with direct observation of the continuous model for controlling the motion of the underactuated ship in the case of uncertain disturbance and the bound control signals. The result is that the movement quality of the underactuated ship is very good in the context of uncertain disturbance and bound control signals.

Multi-step output feedback predictive control for uncertain discrete-time T–S fuzzy system via event-triggered scheme

This paper addresses a synthesis approach of multi-step event-triggered output feedback model predictive control (OFMPC) for uncertain discrete-time Takagi–Sugeno (T–S) fuzzy system with bounded disturbance. An event-triggered scheme, which aims at reducing the redundant signal transmission, is designed to determine whether the current measured output should be released or not. The parameter-dependent output feedback predictive controller is presented by minimizing the upper bound of an infinite horizon quadratic objective function respecting input constraint. Based on this result, an additional optimization problem to tighten the bound of the state error is provided, which is of crucial importance on the control performance. The overall multi-step predictive control approach, which parameterizes the infinite horizon control moves into a sequence of output feedback laws, offers more degrees of freedom for optimization and leads to better control performance with the increase of the switching horizon. Simulation results of a continuous stirred tank reactor (CSTR) system are given to demonstrate the effectiveness of the provided techniques.

Dynamic Output Feedback Predictive Control With One Free Control Move for the Takagi–Sugeno Model With Bounded Disturbance

In this paper, the fuzzy model-predictive control for a nonlinear system represented by a discrete-time Takagi–Sugeno model with norm-bounded disturbance is studied. An output feedback algorithm is proposed by parameterizing the infinite horizon control moves and estimated states into one free control move and one free estimated state followed by a dynamic output feedback law. Since the introduced free control move and free estimated state are decision variables that bring more degrees of freedom for the optimization, a larger feasibility region and better control performance can be achieved. By properly designing the constraints in the optimization problem, the recursive feasibility and the convergence of the closed-loop system to the neighborhood of the equilibrium are guaranteed. A numerical example is given to illustrate the effectiveness of the proposed algorithm.

Low latency output feedback predictive control based on optimality conditions

AbstractIn output feedback predictive control the control input is calculated by solving an optimization problem at each time instant based on a state estimate obtained by an observer. To efficiently counteract disturbances it is important to reduce the delay/latency due to the solution of the optimization problem. To this end we present an approach splitting the optimization into two phases: In the inter‐sample period a set of control laws parameterized by future measurements is determined exploiting the optimality conditions. Once the new measurement is available, the controller selects the corresponding control law and determines the optimal input, which can be achieved fast and with a low computational demand.

Two-step output feedback predictive control for Hammerstein systems with networked-induced time delays

ABSTRACTThis paper is mainly concerned with the design problem of two-step model predictive control (MPC) for nonlinear systems represented by Hammerstein model, where the network-induced time delays exist between sensor to controller (S2C) and controller to actuator (C2A) links. We assume that the system state is not measurable, so the state observer is employed to estimate the state. The intermediate variable for the linear part of the system is calculated by minimising the quadratic performance function. The time-delay compensation algorithm of two-step output feedback predictive control (TSOFPC) for Hammerstein systems is presented and validated by a numerical example.

Unknown Inputs Observer-Based Output Feedback Predictive Controller for an Activated Sludge Process

ABSTRACT This paper investigates the control problem of an activated sludge process despite the non-measurable states and the unknown inputs (UIs). Precisely, our objective is to control the global nitrogen concentration () at the settler output, with partial state measurement and UIs. For this MIMO system, a nonlinear predictive controller based on a high-gain observer is proposed. First a state feedback (full information) predictive control low is designed in order to maintain the global nitrogen concentration in accordance with the standard imposed by the European standards for water quality. Then, a high-gain observer-based UI is designed whose aim is to estimate the non-measurable states (the concentrations of the readily biodegradable substrate and the ammonia ) and the UIs (the concentrations of substrate soluble in water and the ammoniacal nitrogen entering the reactor: and ) despite their variable behavior. Finally, estimated states and inputs are used to generate the output feedback predictive control. Simulation results validate the objective of the paper.

Observer-Based Predictive Control with One Free Control Move for NCSs with Data Loss

In this paper, an efficient output feedback predictive control synthesis based on a prespecified observer for networked control systems is presented. The process of random packet loss between the controller and the actuator is described as Markov chain, and a missing data compensation strategy is induced to cope with the poor performance caused by fading links. The provided model predictive control algorithm optimizes an infinite-horizon objective and parameterizes the infinite-horizon control moves into a free control move followed by output feedback. Further, the corresponding constraints about recursive feasibility and stochastic stability are given by utilizing the linear matrix inequality technique. A numerical example is given to illustrate the applicability of the proposed method.

Dynamic Output Feedback-Predictive Control of a Takagi–Sugeno Model With Bounded Disturbance

This paper considers predictive control of a Takagi–Sugeno fuzzy model with bounded disturbance, strictly satisfying the input and state constraints. The membership-function-dependent dynamic output feedback law, as in a previous work, is utilized. The novelty lies in the following technical improvements. For the notion of quadratic boundedness, which specifies closed-loop stability and invariance properties, the full Lyapunov matrix is utilized. The real-time ellipsoidal bound of true state is recursively optimized by invoking the S-procedure. Some relaxation scalars, being optimized by the norm-bounding technique, are introduced for better handling the input and state constraints. The multistep approach, where a sequence of dynamic output feedback laws are optimized at each sampling instant, is given to improve the single-step approach. Numerical examples are given to illustrate the effectiveness of the proposed controllers.

Feedback Linearization for Nonlinear Systems With Time-Varying Input and Output Delays by Using High-Gain Predictors

This note addresses the problem of feedback linearization for nonlinear systems with time-varying input and output delays. To solve the realization issue of future states, a high-gain-observer is constructed as a predictor. Then, the output feedback predictive control is presented. The stability of the closed-loop system is analyzed by using a Lyapunov-Krasovskii functional. It is demonstrated that by designing the observer gain high enough, the closed-loop system recovers the performance of state feedback control with no time delay. However, the gain of the observer is limited by the time delay. Finally, numerical simulations are given to illustrate the effectiveness of the proposed control.

An optimal approach to output-feedback robust model predictive control of LPV systems with disturbances

Summary An observer-based output feedback predictive control approach is proposed for linear parameter varying systems with norm-bounded external disturbances. Sufficient and necessary robust positively invariant set conditions of the state estimation error are developed to determine the minimal ellipsoidal robust positively invariant set and observer gain through offline computation. The quadratic upper bound of state estimation error is updated and included in an -type cost function of predictive control to optimize transient output feedback control performance. Recursive feasibility of the dynamic convex optimization problem is guaranteed in the proposed predictive control strategy. With the input-to-state stable observer, the closed-loop control system states are steered into a bounded set. Simulation results are given to demonstrate the effectiveness of the proposed control strategy. Copyright © 2016 John Wiley & Sons, Ltd.

Linear programming predictive control with actuator saturation: Experimental robustness and performance results

Linear programming optimization is widely used in several application areas. One possible application of this optimization technique, which has not received sufficient attention, is process control. In particular, the standard structure of linear programming optimization problems allows formulating optimal predictive control schemes with the additional advantage of facilitating the handling of actuator saturation. Actuator saturation appears in many engineering applications and there are several control approaches dealing with this problem. In this article, a very simple predictive control scheme based on linear programming optimization for actuator saturation control is proposed. The main characteristics of the presented scheme are simplicity and practical robustness/performance tuning parameters. First, the simplest possible output-feedback linear programming predictive controller is proposed as a starting point. Next, through several practical observations inspired in classical control engineering, a series of modifications to the basic controller structure are proposed. These modifications aim at effectively increasing the performance and robustness of the control algorithm. The experimental results confirm that the proposed control algorithm can operate effectively under the presence of perturbations, something which is currently not widely reported in the literature for linear programming actuator saturation predictive control.