Observer design for a class of MIMO nonlinear systems

A simple observer is proposed for a large class of MIMO nonlinear systems which includes many physical models. The main characteristic of the proposed observer lies in the easiness of its implementation and calibration. Indeed, the gain of this observer does not necessitate the resolution of any dynamical system and its expression is given. Moreover, its calibration is achieved through the choice of a single parameter. A simulation example is given in order to illustrate the performance of the proposed observer.     

Reduction of stable differential–algebraic equation systems via projections and system identification

Most large-scale process models derived from first principles are represented by nonlinear differential–algebraic equation (DAE) systems. Since such models are often computationally too expensive for real-time control, techniques for model reduction of these systems need to be investigated. However, models of DAE type have received little attention in the literature on nonlinear model reduction. In order to address this, a new technique for reducing nonlinear DAE systems is presented in this work. This method reduces the order of the differential equations as well as the number and complexity of the algebraic equations. Additionally, the algebraic equations of the resulting system can be replaced by an explicit expression for the algebraic variables such as a feedforward neural network. This last property is important insofar as the reduced model does not require a DAE solver for its solution but system trajectories can instead be computed with regular ODE solvers. This technique is illustrated with a case study where responses of several different reduced-order models of a distillation column with 32 differential equations and 32 algebraic equations are compared.     

A note to output feedback adaptive control for uncertain system with static nonlinearity

The problem of control design for a system represented as linear stationary and static nonlinear parts is considered. It is assumed that the linear part is unknown and strictly minimum phase. The nonlinear part is known inaccurately, it is irreducible to an input of the linear block, and generally does not satisfy sector restrictions. An adaptive regulator ensuring asymptotic stability is synthesized. The output of a control system, but not its derivatives, is used as a measured variable.     

An optimal adaptive H-infinity tracking control design via wavelet network

In this paper, an optimal adaptive H-infinity tracking control design method via wavelet network for a class of uncertain nonlinear systems with external disturbances is proposed to achieve H-infinity tracking performance. First, an alternate tracking error and a performance index with respect to the tracking error and the control effort are introduced in order to obtain better performance, especially, in reducing the cost of the control effort in the case of small attenuation levels. Next, H-infinity tracking performance, which attenuates the influence of both wavelet network approximation error and external disturbances on the modified tracking error, is formulated. Our results indicate that a small attenuation level does not lead to a large control signal. The proposed method insures an optimal trade-off between the amplitude of control signals and the performance of tracking errors. An example is given to illustrate the design efficiency.     

Overcoming the detectability obstacle in certainty equivalence adaptive control

In this paper, we are interested in global adaptive stabilization of nonlinear systems where we know a linearly parametrized stabilizer and a Lyapunov function for the ideal closed-loop system such that the standard estimator is implementable, but this Lyapunov function is not strict, i.e., its derivative is only negative semi-definite. Our motivation to consider this situation stems from the fact that, for many physical systems, the natural Lyapunov function candidate is the total energy, which is always not strict. To complete the stability proof in this case it is necessary to add a—rather restrictive—detectability assumption. Our main contribution is to show that it is possible to overcome this obstacle by adding to the parameter estimator an identification error coming from an indirect identifier. We thus replace the detectability assumption by a new condition that essentially requires that the negative-definite terms appearing in the Lyapunov function derivative dominate, the uncertain terms that cannot be matched by the controller. No matching, or persistency of excitation assumptions are needed.     

Asymptotic controllability and observability imply semiglobal practical asymptotic stabilizability by sampled-data output feedback

It is shown, for general nonlinear systems, that asymptotic controllability and observability are sufficient for semiglobal practical asymptotic stabilization by output feedback. Indeed, as previously shown in the literature, asymptotic controllability implies the existence of a (discontinuous in general) state feedback that, when implemented by sample and hold, is semiglobally practically stabilizing and robust to measurement disturbance; moreover, a weak form of observability allows reconstruction of the state with arbitrary precision in an arbitrarily short amount of time. So, we can build an output feedback that operates periodically in two modes: an initial, small fraction of a sampling period is used to estimate the state, and the remainder of the sampling period is used to implement the state feedback control using the state estimate. Our stabilization results are presented not only for compact target sets (e.g., the origin) but also for noncompact target sets.     

Global partial-state feedback and output-feedback tracking controllers for underactuated ships

New global partial-state feedback and output-feedback control schemes for tracking control of an underactuated surface ship without sway force available are presented. For the case of partial-state feedback, we do not require measurements of the ship sway and surge velocities, while, for the case of output-feedback, none of the ship velocities are required for feedback. The reference trajectory to be tracked can be a curve including a straight-line. Global nonlinear coordinate changes are introduced to transform the ship dynamics to a system affine in the ship velocities to design observers to globally exponentially estimate unmeasured velocities. The controllers’ development is based on Lyapunov's direct method and backstepping technique, and utilizes the passive property of ship dynamics and their interconnected structure. Numerical simulations are provided to illustrate the effectiveness of the proposed controllers.     

On the non-existence of stationary diffusions which satisfy the Bene condition

The one-dimensional diffusion x_t satisfying dx_t = f(x_t)dt + dw_t, where w_t is a standard Brownian motion and f(x) satisfies the Bene condition f′(x) + f²(x) = ax² + bx + c for all real x, is considered. It is shown that this diffusion does not admit a stationary probability measure except for the linear case f(x) = αx + β, α < 0.     

On state observers for nonlinear systems

For linear systems, it is known that there exists a state observer if and only if the system is detectable, or in other words, asymptotically stabilizable by output injection. In this paper, it is shown that asymptotic stabilizability by output injections is neither necessary nor sufficient for the construction of a nonlinear observer which may not have an exponential error decay rate. The concept of uniform observers is introduced and necessary conditions and sufficient conditions are given for the construction of uniform observers.     

Global robust stabilization of cascaded polynomial systems

Global robust stabilization of nonlinear cascaded systems is a challenging problem when the zero-dynamics is not exponentially stable. Recently, some recursive procedure has been developed for handling this problem utilizing the small gain theorem. However, the success of the procedure depends on the satisfaction of some conditions which arise at each step of the recursion. In this paper, we will show that, for the important class of cascaded polynomial systems, the solvability conditions can be made satisfied by appropriately implementing the recursive procedure. This result leads to an explicit construction of the control law.     

Robust tracking control of uncertain nonlinear systems with unknown control directions

A global robust tracking control design procedure is proposed for a class of uncertain nonlinear systems. The key point is that the signs of multiplicative uncertainties, the so-called control directions, are not assumed to be known a priori. The class of systems can be of arbitrary dynamic order and the unmatched additive uncertainties need not satisfy the global Lipschitz condition. It is proved that under the proposed control, all closed-loop states are bounded and the tracking error converges to any prescribed small neighborhood of the origin. The results of this paper enlarge the class of uncertain nonlinear systems for which global robust tracking control can be designed.     

Switching adaptive output-feedback control of nonlinearly parametrized systems

In this paper, we consider global adaptive output-feedback control of nonlinear systems in output-feedback form, with unknown parameters entering nonlinearly. Such unknown parameters are not required to lie in a known compact set. Our proposed adaptive output-feedback controller is a switching-type controller, in which the controller parameter is tuned in a switching manner via a switching logic. Global stability results of the closed-loop system have been proved.     

Lyapunov conditions for input-to-state stability of impulsive systems

This paper introduces appropriate concepts of input-to-state stability (ISS) and integral-ISS for impulsive systems, i.e., dynamical systems that evolve according to ordinary differential equations most of the time, but occasionally exhibit discontinuities (or impulses). We provide a set of Lyapunov-based sufficient conditions for establishing these ISS properties. When the continuous dynamics are ISS, but the discrete dynamics that govern the impulses are not, the impulses should not occur too frequently, which is formalized in terms of an average dwell-time (ADT) condition. Conversely, when the impulse dynamics are ISS, but the continuous dynamics are not, there must not be overly long intervals between impulses, which is formalized in terms of a novel reverse ADT condition. We also investigate the cases where (i) both the continuous and discrete dynamics are ISS, and (ii) one of these is ISS and the other only marginally stable for the zero input, while sharing a common Lyapunov function. In the former case, we obtain a stronger notion of ISS, for which a necessary and sufficient Lyapunov characterization is available. The use of the tools developed herein is illustrated through examples from a Micro-Electro-Mechanical System (MEMS) oscillator and a problem of remote estimation over a communication network.     

On self-synchronization and controlled synchronization

An attempt is made to give a general formalism for synchronization in dynamical systems encompassing most of the known definitions and applications. The proposed set-up describes synchronization of interconnected systems with respect to a set of functionals and captures peculiarities of both self-synchronization and controlled synchronization. Various illustrative examples are given.     

An algorithm for bounded-error identification of nonlinear systems based on DC functions

This paper presents a guaranteed method for the parameter estimation of nonlinear models in a bounded-error context. This method is based on functions which consists of the difference of two convex functions, called DC functions. The method considers DC representations of the functional form of the dynamic system to obtain an outer bound of the set of parameters that are consistent with the measurements, the system and the considered bounded error. At each iteration, the proposed algorithm solves several convex optimization problems to discard from the initial search region subregions that are proved not consistent. This operation is repeated while the obtained solution is improved. Four examples are provided to clarify the proposed identification algorithm.     

一类非线性系统的直接自适应控制器

针对带一类非线性参数系统的状态反馈自适应跟踪控制问题,通过设计一种新的李亚普诺夫函数--加权控制李亚普诺夫函数,由它作用于控制器和参数调整律,使之达到全局渐近跟踪从而满足控制指标。     

Feedback tuning of bifurcations

The present paper studies a feedback regulation problem that arises in at least two different biological applications. The feedback regulation problem under consideration may be interpreted as an adaptive control problem for tuning bifurcation parameters, and it has not been studied in the control literature. The goal of the paper is to formulate this problem and to present some preliminary results.     

Introducing the bounded derivative network—superceding the application of neural networks in control

The Bounded Derivative Network (BDN), the analytical integral of a neural network, is a natural and elegant evolution of universal approximating technology for use in automatic control schemes. This modeling approach circumvents the many real problems associated with standard neural networks in control such as model saturation (zero gain), arbitrary model gain inversion, ‘black box’ representation and inability to interpolate sensibly in regions of sparse excitation. Although extrapolation is typically not an advantage unless the understanding of the process is complete, the BDN can incorporate process knowledge in order that its extrapolation capability is inherently sensible in areas of data sparsity. This ability to impart process knowledge on the BDN model enables it to be safely incorporated into a model based control scheme.     

Output-feedback stabilization of a class of uncertain non-minimum-phase nonlinear systems

The problem of global output-feedback stabilization for a class of nonlinear systems whose zero dynamics are not necessarily stable is addressed in this paper. It is shown that, using a novel observer design tool together with standard backstepping and small-gain techniques, it is possible to design a stabilizing output feedback controller which ensures robustness with respect to dynamic uncertainties. The proposed stabilization method generalizes existing tools in several directions. As an example, the method is applied to the stabilization of the benchmark translational oscillator/rotational actuator (TORA) using only measurements of the rotational position.     

Two nonlinear optimization methods for black box identification compared

In this paper, two nonlinear optimization methods for the identification of nonlinear systems are compared. Both methods estimate the parameters of e.g. a polynomial nonlinear state-space model by means of a nonlinear least-squares optimization of the same cost function. While the first method does not estimate the states explicitly, the second method estimates both states and parameters adding an extra constraint equation. Both methods are introduced and their similarities and differences are discussed utilizing simulation data. The unconstrained method appears to be faster and more memory efficient, but the constrained method has a significant advantage as well: it is robust for unstable systems of which bounded input-output data can be measured (e.g. a system captured in a stabilizing feedback loop). Both methods have successfully been applied on real-life measurement data.