A framework for design of scheduled output feedback model predictive control

We present a stabilizing scheduled output feedback Model Predictive Control (MPC) algorithm for constrained nonlinear systems with large operating regions. We design a set of local output feedback predictive controllers with their estimated regions of stability covering the desired operating region, and implement them as a single scheduled output feedback MPC which on-line switches between the set of local controllers and achieves nonlinear transitions with guaranteed stability. This algorithm provides a general framework for scheduled output feedback MPC design.     

Non-linear output feedback tracking control for AUVs in shallow wave disturbance condition

This article presents a non-linear output feedback tracking controller deisgn for autonomous underwater vehicles (AUVs) operating in shallow water area. In a shallow water environment, significant disturbances due to shallow water waves affect the motion of marine vehicles greatly. Since it is not energy efficient to counteract the oscillatory disturbances due to waves, it is critical to obtain the wave information or wave induced disturbance information and design an energy efficient controller to reduce the action of actuators to counteract wave disturbances to avoid wear and tear on actuators. In this article, a non-linear observer is first designed to estimate the low frequency (LF) motion of AUVs and to filter out wave-frequency (WF) motion of AUVs due to shallow water wave by using position and attitude measurements. Based on the designed observer, a non-linear output feedback controller is subsequently derived by using the observer backstepping technique. By using this approach, the AUV achieves global exponential tracking without excessive energy consumption to counteract the wave disturbance and also avoids excessive wear and tear on thrusters. Global exponential stability (GES) of overall observer-controller system is proved through Lyapunov stability theory. A set of simulations is carried out by using the KAMBARA (Silpa-Anan 2001 Silpa-Anan, C. 2001. “Autonomous Underwater Robot: Vision and Control”. In Master's thesis, The Australian National University.  [Google Scholar]) AUV model to demonstrate the performance of the proposed observer and output feedback controller.     

Constrained output feedback model predictive control for nonlinear systems

A constrained output feedback model predictive control approach for nonlinear systems is presented in this paper. The state variables are observed using an unscented Kalman filter, which offers some advantages over an extended Kalman filter. A nonlinear dynamic model of the system, considered in this investigation, is developed considering all possible effective elements. The model is then adaptively linearized along the prediction horizon using a state-dependent state space representation. In order to improve the performance of the control system as many linearized models as the number of prediction horizons are obtained at each sample time. The optimum results of the previous sample time are utilized for linearization at the current sample time. Subsequently, a linear quadratic objective function with constraints is formulated using the developed governing equations of the plant. The performance and effectiveness of the proposed control approach is validated both in simulation and through real-time experimentation using a constrained highly nonlinear aerodynamic test rig, a twin rotor MIMO system (TRMS).     

Dynamic output feedback robust model predictive control

The synthesis approach for dynamic output feedback robust model predictive control is considered. The notion of quadratic boundedness is utilised to characterise the stability properties of the augmented closed-loop system. A finite horizon performance cost, which corresponds to the worst case of both the polytopic uncertainty and the bounded disturbance/noise, is utilised. It is not required to specify the horizon length. A numerical example is given to illustrate the effectiveness of the proposed controller.     

Transfer function conditions for output feedback disturbance rejection

A recent paper [1] has derived state space conditions under which the disturbance transfer function in a linear multivariable system can be zeroed by a dynamic compensator forced by a prescribed set of measurements. The present note derives necessary conditions for this problem in terms of the orders of the open-loop control, disturbance, and measurement transfer functions. These necessary conditions are shown to be generically sufficient for solvability. Moreover, they provide additional insight into the geometric solvability conditions, are simple to check, and extend the corresponding results obtained for the state feedback case [2].     

基于混沌粒子群优化的约束状态反馈预测控制算法

提出一种基于混沌粒子群优化的约束状态反馈预测控制算法,用于解决带有输入约束和状态约束的控制问题.将混沌粒子群优化引入到约束状态反馈预测控制的滚动优化过程中,增强了算法在约束范围内的局部搜索和全局搜索能力.通过对一个实际的带有约束的线性离散系统控制优化问题的解决,验证了基于混沌粒子群优化的状态反馈预测控制算法的可行性和有效性,与传统的二次规划算法的比较结果说明了此算法的优越性,证明了状态反馈预测控制系统良好的鲁棒性.     

Universal disturbance rejection for nonlinear systems in output feedback form

This note deals with global disturbance rejection via output feedback of a class of uncertain nonlinear systems subject to a class of unknown disturbances. Both the uncertainty in the system model and the uncertainty in the exosystem are tackled concurrently. The disturbances generated from an unknown linear exosystem are completely rejected. The order of the exosystem is assumed known, and the eigenvalues are distinct. The system is assumed in the format of the minimum-phase output feedback form, with no knowledge of the values of any system parameters, including the high-frequency gain. No other assumptions are needed in the control design. A new set of filters are introduced for state estimation. The stability of the internal model is exploited to design a new auxiliary error, involving both the unknown parameters of the reformatted exosystem and those of the system, which makes it possible to group all the unknown parameters in a format suitable to adaptive control design. A Nussbaum gain is introduced in adaptive control design to tackle the unknown high-frequency gain and a number of control coefficients are also made adaptive so that the disturbance rejection is global with respect to unknown frequencies in the disturbances.     

Quantized output feedback control for networked control systems

This paper addresses the problem of output feedback control for networked control systems (NCSs) with limited communication capacity. Firstly, we propose a new model to describe the non-ideal network conditions and the input/output state quantization of the NCSs in a unified framework. Secondly, based on our newly proposed model and an improved separation lemma, the observer-based controller is developed for the asymptotical stabilization of the NCSs, which are shown in terms of nonlinear matrices inequalities. The nonlinear problems can be computed through solving a convex optimization problems, and the observed and controller gains could be derived by solving a set of linear matrix inequalities. Thirdly, two simulation examples are given to demonstrate the effectiveness of the proposed method.     

Parametric output feedback control with response insensitivity

A recent parameterization of the class of linear output-feedback controllers that assign a set of desired self-conjugate eigenvalues to the closed-loop system is used to formulate and solve a fundamental response insensitivity problem. It is established to what extent output-feedback control can be used to render the closed-loop system response insensitive to possibly many not necessarily small parameter variations in the open-loop state space model. A non-conservative sequential design procedure is developed for making as many of the closed-loop system eigenmodes as possible totally insensitive while retaining arbitrary assignment of the maximum number of closed-loop eigenvalues. The main result is a class of desensitizing fixed-gain output-feedback controllers explicitly specified by a set of free parameters which may be chosen to satisfy additional design requirements. Conditions are given for total modal decoupling insensitivity to possibly many not necessarily small parameter variations in the open-loop state model. The mechanism of modal decoupling insensitivity for a given mode is interpreted in terms of the insensitivity of the corresponding left eigenmode. A parametric approach to output feedback design for modal decoupling insensitivity is discussed.     

Efficient scheduled stabilizing output feedback model predictive control for constrained nonlinear systems

In this note, we present a computationally efficient scheduled output feedback model predictive control (MPC) algorithm for constrained nonlinear systems with large operating regions. We design a set of local output feedback predictive controllers with their estimated regions of stability covering the desired operating region, and implement them as a single scheduled output feedback MPC which on-line switches between the set of local controllers and achieves nonlinear transitions with guaranteed stability. The algorithm is illustrated with a highly nonlinear continuous stirred tank reactor process.     

Output feedback networked control with persistent disturbance attenuation

This paper presents a model-based control approach for output feedback stabilization and disturbance attenuation of continuous time systems that transmit measurements over a limited bandwidth communication network. Necessary and sufficient conditions for asymptotic stability of the networked system in the presence of persistent external disturbances are given. The results in this paper provide a significant improvement in the performance of the system and provide a considerably reduction of the necessary network bandwidth with respect to similar approaches in the literature.     

Variable structure model following controller using non-dynamic multirate output feedback

The paper presents a new algorithm for a variable structure model following controller (VSMFC) design for discrete time systems using a fast output sampling technique. The reaching law approach is used to guarantee the sliding mode motion. This methodology is easy to implement since the method is based on output feedback. It is shown that the proposed fast output sampling variable structure model following controller (FOS VSMFC) gives the same results as obtained by state feedback VSMFC. To demonstrate the design technique a VSMFC is designed for the tip position control of a single flexible link. The simulation results show the effectiveness of the proposed method.     

Robust H∞ output feedback control for nonlinearsystems

In this paper we present a new approach to the solution of the output feedback robust H_∞ control problem. We employ the recently developed concept of information state for output feedback dynamic games and obtain necessary and sufficient conditions for the solution to the robust control problem expressed in terms of the information state. The resulting controller is an information state feedback controller and is intrinsically infinite dimensional. Stability results are obtained using the theory of dissipative systems, and our results are expressed in terms of dissipation inequalities     

Off-line approach to dynamic output feedback robust model predictive control

This paper presents an off-line approach to the dynamic output feedback robust model predictive control (OFRMPC) for a system with both polytopic uncertainty and bounded disturbance. For the off-line optimization, a sequence of controller parameters and the corresponding regions of attraction are calculated for all combinations of the pre-specified estimated states and estimation error sets (EESs). These controller parameters and the corresponding regions of attraction are stored in a look-up table. On-line, the controller parameters are searched in this look-up table corresponding to real-time EES, and to the region of attraction with the closest containment of real-time estimated state. This method considerably reduces the on-line computational burden. Two numerical examples are given to illustrate the effectiveness of the approach.     

Robust output feedback model predictive control of constrained linear systems

This paper provides a solution to the problem of robust output feedback model predictive control of constrained, linear, discrete-time systems in the presence of bounded state and output disturbances. The proposed output feedback controller consists of a simple, stable Luenberger state estimator and a recently developed, robustly stabilizing, tube-based, model predictive controller. The state estimation error is bounded by an invariant set. The tube-based controller ensures that all possible realizations of the state trajectory lie in a simple uncertainty tube the ‘center’ of which is the solution of a nominal (disturbance-free) system and the ‘cross-section’ of which is also invariant. Satisfaction of the state and input constraints for the original system is guaranteed by employing tighter constraint sets for the nominal system. The complexity of the resultant controller is similar to that required for nominal model predictive control.     

Constrained robust model predictive control via parameter-dependent dynamic output feedback

This paper considers output feedback robust model predictive control for the quasi-linear parameter varying (quasi-LPV) system with bounded disturbance. The so-called quasi-LPV means that the varying parameters of the linear system are known at the current time, but unknown in the future. The control law is parameterized as a parameter-dependent dynamic output feedback, and the closed-loop stability is specified by the notion of quadratic boundedness. An iterative algorithm is proposed for the on-line synthesis of the control law via convex optimization. A numerical example is given to illustrate the effectiveness of the controller.