Conditions under which suboptimal nonlinear MPC is inherently robust

We address the inherent robustness properties of nonlinear systems controlled by suboptimal model predictive control (MPC), i.e., when a suboptimal solution of the (generally nonconvex) optimization problem, rather than an element of the optimal solution set, is used for the control. The suboptimal control law is then a set-valued map, and consequently, the closed-loop system is described by a difference inclusion. Under mild assumptions on the system and cost functions, we establish nominal exponential stability of the equilibrium, and with a continuity assumption on the feasible input set, we prove robust exponential stability with respect to small, but otherwise arbitrary, additive process disturbances and state measurement/estimation errors. These results are obtained by showing that the suboptimal cost is a continuous exponential Lyapunov function for an appropriately augmented closed-loop system, written as a difference inclusion, and that recursive feasibility is implied by such (nominal) exponential cost decay. These novel robustness properties for suboptimal MPC are inherited also by optimal nonlinear MPC. We conclude the paper by showing that, in the absence of state constraints, we can replace the terminal constraint with an appropriate terminal cost, and the robustness properties are established on a set that approaches the nominal feasibility set for small disturbances. The somewhat surprising and satisfying conclusion of this study is that suboptimal MPC has the same inherent robustness properties as optimal MPC.     

Adaptive backstepping control of uncertain systems with unknown input time-delay

In this paper, we establish the robustness of adaptive controllers designed using the standard backstepping technique with respect to unmodeled dynamics involving unknown input time delay. While noting that some results on robust stabilization of non-minimum phase systems using the backstepping technique are available, we realize that the standard adaptive backstepping technique has only been shown applicable to unknown minimum phase systems. Another significance of our result is to enable the class of systems stablizable by adaptive backstepping controllers to cross the boundary of minimum phase systems, since systems with input time delay belong to non-minimum phase systems. Moreover, the L₂ and L_∞ norms of the system output are also established as functions of design parameters. This implies that the transient system performance can be adjusted by choosing suitable design parameters.     

Type-2 fuzzy sliding mode control without reaching phase for nonlinear system

A new sliding mode control (SMC) algorithm for the nth order nonlinear system suffering from parameters uncertainty and subjected to an external perturbation is proposed. The algorithm employs a time-varying switching plane. At the initial time t=t₀, the plane passes through the point determined by the system initial conditions in the error state space. Afterwards, the plane moves to the origin of the state space. Since the nonlinear system is sensible to the perturbations and uncertainties during the reaching phase, the elimination of such phase yields in a considerable amelioration of system robustness. Switching plane is chosen such that: (1) the reaching phase is eliminated, (2) the nonlinear system is insensitive to the external disturbance and the model uncertainty from the initial time (3) the convergence of the tracking error to zero. Furthermore, a Type-2 fuzzy system is used to approximate system dynamics (assumed to be unknown) and to construct the equivalent controller such that: (1) all signals of closed-loop system are uniformly ultimately bounded, (2) the problems related to adaptive fuzzy controllers like singularity and constraints on the control gain are resolved. To ensure the robustness of the overall closed-loop system, analytical demonstration using Lyapunov theorem is considered. Finally, a robot manipulator is considered as a real time system in order to confirm the efficiency of the proposed approach. The experimentation is done for both regulation and tracking control problems.     

Affine and predictive control policies for a class of nonlinear systems

The input-state linear horizon (ISLH) for a nonlinear discrete-time system is defined as the smallest number of time steps it takes the system input to appear nonlinearly in the state variable. In this paper, we employ the latter concept and show that the class of constraint admissible N-step affine state-feedback policies is equivalent to the associated class of constraint admissible disturbance-feedback policies, provided that N is less than the system’s ISLH. The result generalizes a recent result in [Goulart, P. J., Kerrigan, E. C., Maciejowski, J. M. (2006). Optimization over state feedback policies for robust control with constraints. Automatica, 42(4), 523-533] and is significant because it enables one: (i) to determine a constraint admissible state-feedback policy by employing well-known convex optimization techniques; and (ii) to guarantee robust recursive feasibility of a class of model predictive control (MPC) policies by imposing a suitable terminal constraint. In particular, we propose an input-to-state stabilizing MPC policy for a class of nonlinear systems with bounded disturbance inputs and mixed polytopic constraints on the state and the control input. At each time step, the proposed MPC policy requires the solution of a single convex quadratic programme parameterized by the current system state.     

Nonlinear stabilizability via encoded feedback: The case of integral ISS systems

In this note we observe how, using the same arguments of Liberzon and Hespanha [(2005). Stabilization of nonlinear systems with limited information feedback, IEEE Transactions on Automatic Control, 50, 910-915] , integral input-to-state stabilizability with respect to measurement errors is enough to prove stabilizability of nonlinear systems with limited information. The utility of the remark lies on the fact that, under a requirement on the data rate analogous to the one in the paper by Liberzon and Hespanha, the stabilizability result is proven for a larger class of systems.     

The zero divisor problem of multivariable stochastic adaptive control

Stochastic adaptive minimum variance control algorithms require a division by a function of a recursively computed parameter estimate at each instant of time. In order that the analysis of these algorithms is valid, zero divisions must be events of probability zero. This property is established for the stochastic gradient adaptive control algorithm under the condition that the initial state of the system and all finite segments of its random disturbance process have a joint distribution which is absolutely continuous with respect to Lebesgue measure. This result is deduced from the following general result established in this paper: a non-constant rational function of a finite set of random variables {x₁},x_n} is absolutely continuous with respect to Lebesgue measure if the joint distribution function of {x₁,…,x_n} has this property.     

带正弦干扰的线性时滞系统的次优控制

研究状态变量含有时滞的线性系统在正弦干扰下的前馈反馈次优减振控制问题，首先构造其解收敛于原时滞系统的无时滞系统序列；然后将时滞系统的最优控制问题化为无时滞系统的最优控制序列问题；最后通过截取最优控制序列解的有限项，得到系统的前馈反馈次优控制律，仿真结果表明，该方法抑制正弦干扰的鲁棒性优于经典反馈最优控制。     

Bounds for uncertain matrix root-clustering in a union of subregions

The research for robustness bounds for systems whose behaviour is described by a linear state-space model is addressed. The paper lays stress on the location of the eigenvalues of the state matrix when this matrix is subject either to an unstructured additive uncertainty or to a structured additive uncertainty. In the first case, upper bounds on the spectral norm of the uncertainty matrix are determined whereas in the second case, upper bounds on the maximal real perturbation in the state matrix are derived. In both cases, the fact that these bounds are not exceeded ensures that the eigenvalues of the uncertain state matrix lie in a specified region 𝒟 of the complex plane in which those of the nominal state matrix already lie. These bounds are obtained through a linear matrix inequalities approach. This approach allows to specify 𝒟, not only as a simple convex region, symmetric with respect to the real axis, but also as a non-convex (but symmetric with respect to the real axis) region defined itself as a union of convex subregions, each of them being not necessarily symmetric with respect to the real axis. Copyright © 1999 John Wiley & Sons, Ltd.     

Visibility maintenance via controlled invariance for leader–follower vehicle formations

This paper studies the visibility maintenance problem (VMP) for a leader–follower pair of Dubins-like vehicles with input constraints and proposes an original solution based on the notion of controlled invariance. The nonlinear model describing the relative dynamics of the vehicles is interpreted as a linear uncertain system, with the leader robot acting as an external disturbance. The VMP is then reformulated as a linear constrained regulation problem with additive disturbances (DLCRP). Positive D-invariance conditions for linear uncertain systems with parametric disturbance matrix are introduced and used to solve the VMP when box bounds on the state, control input and disturbance are considered. The proposed design procedure can be easily adapted to more general scenarios. Simulation results illustrate the theory and show the effectiveness of our approach.     

SMOOTH SLIDING CONTROL OF UNCERTAIN SYSTEMS BASED ON A PREDICTION ERROR

This paper addresses the problem of designing sliding control of uncertain systems which are free of chattering and result in smooth control signals. Both state and output feedback are considered. The proposed approach relies on some appropriate prediction error and can be easily applied to modify the usual state feedback VSC for chattering avoidance. It can also be applied for devising new output feedback VSC laws. The robustness improvement, with respect to unmodelled dynamics, even allows one to reconsider the so far rejected utilization of approximate differentiators for VSC output feedback. The theoretical results are illustrated by simulations. An experimental evaluation is briefly discussed. © 1997 by John Wiley & Sons, Ltd.     

Robust dynamic state feedback for underactuated systems with linearly parameterized disturbances

This article investigates the control problem for underactuated port‐controlled Hamiltonian systems with multiple linearly parameterized additive disturbances including matched, unmatched, constant, and state‐dependent components. The notion of algebraic solution of the matching equations is employed to design an extension of the interconnection and damping assignment passivity‐based control methodology that does not rely on the solution of partial differential equations. The result is a dynamic state‐feedback that includes a disturbance compensation term, where the unknown parameters are estimated adaptively. A simplified implementation of the proposed approach for underactuated mechanical systems is detailed. The effectiveness of the controller is demonstrated with numerical simulations for the magnetic‐levitated‐ball system and for the ball‐on‐beam system.     

Robust adaptive compensation of biased sinusoidal disturbances with unknown frequency

Asymptotically stable, observable linear systems of order n which are not required to be minimum phase and are affected by an additive noisy biased sinusoidal disturbance with unknown bias, magnitude, phase and frequency are considered. The problem of designing an output feedback compensator which regulates the output to zero for any initial condition and for any biased sinusoidal disturbance with no noise is addressed, under the assumption that the system parameters are known. This problem is solved by a (2n+6)-order compensator which generates asymptotically convergent estimates of the biased sinusoidal disturbance and of its parameters, including frequency. The robustness of the closed loop system with respect to sufficiently small additive unmodelled noise is characterized in terms of input-to-state stability.     

ISS properties of nonholonomic vehicles

The paper presents the first result on nonholonomic systems enjoying input to state stability (ISS) properties. Although it is known that smooth stabilizability implies ISS, the converse is not generally true. This leaves the possibility of non-smoothly stabilizable systems being ISS with respect to a particular input, after an appropriate feedback transformation. This is shown to be true for the case of the unicycle with a dynamic extension, in a particular topology induced by a metric appropriate for this type of systems. A feedback control law renders the closed-loop system locally ISS in the particular topology. Potential applications include stability and robustness analysis of formations of mobile robots.     

Performance of Parametric and Covariance Based STAP Tests in Compound-Gaussian Clutter

Michels, J. H., Rangaswamy, M., and Himed, B., Performance of Parametric and Covariance Based STAP Tests in Compound-Gaussian Clutter, Digital Signal Processing12 (2002) 307–328The performance of a parametric space–time adaptive processing method is presented here. Specifically, we consider signal detection in additive disturbance containing compound-Gaussian clutter plus additive Gaussian thermal white noise. Performance is compared to the normalized adaptive matched filter and the Kelly GLRT receiver using simulated and measured data. We focus on the issues of detection and false alarm probabilities, constant false alarm rate, robustness with respect to clutter texture power variations, and reduced training data support.     

Uniting local and global controllers for uncertain nonlinear systems: beyond global inverse optimality

This paper provides a solution to a new problem of global robust control for uncertain nonlinear systems. A new recursive design of stabilizing feedback control is proposed in which inverse optimality is achieved globally through the selection of generalized state-dependent scaling. The inverse optimal control law can always be designed such that its linearization is identical to linear optimal control, i.e. optimal control, for the linearized system with respect to a prescribed quadratic cost functional. Like other backstepping methods, this design is always successful for systems in strict-feedback form. The significance of the result stems from the fact that our controllers achieve desired level of ‘global’ robustness which is prescribed a priori. By uniting locally optimal robust control and global robust control with global inverse optimality, one can obtain global control laws with reasonable robustness without solving Hamilton–Jacobi equations directly.     

含正弦扰动的离散时滞系统的次优减振控制

研究状态变量含有时滞的线性离散系统在正弦扰动下的前馈-反馈最优减振控制问题．将系统的最优控制问题转化为非齐次线性两点边值问题族,利用逐次逼近法得到了系统的最优控制律．该控制律由解析的前馈。反馈控制律和补偿序列的极限组成．通过截取补偿序列的有限项,得到了系统的前馈一反馈次优减振控制律．仿真结果表明,该方法对抑制正弦扰动的鲁棒性优于经典反馈最优控制．     

Adaptive-gain second-order sliding mode observer design for switching power converters

In this paper, an adaptive-gain, Second Order Sliding Mode (SOSM) observer for multi-cell converters is designed by considering it as a type of hybrid system. The objective is to reduce the number of voltage sensors by estimating the capacitor voltages from measurement of the load current. The proposed observer is proven to be robust in the presence of perturbations with unknown boundaries. As the states of the system are only partially observable, a recent concept known as Z(T_N)-observability is used to address the switching behavior. Multi-rate simulation results demonstrate the effectiveness and the robustness of the proposed observer with respect to output measurement noise and system uncertainty (load variations).     

A robust distributed model predictive control algorithm

Although distributed model predictive control (DMPC) has received significant attention in the literature, the robustness of DMPC with respect to model errors has not been explicitly addressed. In this paper, a novel online algorithm that deals explicitly with model errors for DMPC is proposed. The algorithm requires decomposing the entire system into N subsystems and solving N convex optimization problems to minimize an upper bound on a robust performance objective by using a time-varying state-feedback controller for each subsystem. Simulations examples were considered to illustrate the application of the proposed method.     

Robust H∞ output‐feedback control of retarded state‐multiplicative stochastic systems

Linear, state‐delayed, continuous‐time systems are considered with both stochastic and norm‐bounded deterministic uncertainties in the state–space model. The problem of robust dynamic H_∞ output‐feedback control is solved, for the stationary case, via the input–output approach where the system is replaced by a nonretarded system with additional deterministic norm‐bounded uncertainties. A delay‐dependent result is obtained which involves the solution of a simple linear matrix inequality. In this problem, a cost function is defined which is the expected value of the standard H_∞ performance cost with respect to the stochastic parameters. A practical example taken from the field of guidance control is given that demonstrates the applicability of the theory. Copyright © 2010 John Wiley & Sons, Ltd.