State feedback design for input-saturating quadratic systems

This paper proposes a method to design stabilizing state feedback control laws for nonlinear quadratic systems subject to input saturation. Based on a quadratic Lyapunov function, a modified sector condition and a particular representation for the quadratic terms, synthesis conditions in a “quasi”-LMI form are stated in a regional (local) context. An LMI-based optimization problem is then derived for computing the state feedback gains maximizing the estimate of the stability region of the closed-loop system.     

A convex optimization approach to filtering in jump linear systems with state dependent transitions

We introduce a new methodology to construct a Gaussian mixture approximation to the true filter density in hybrid Markovian switching systems. We relax the assumption that the mode transition process is a Markov chain and allow it to depend on the actual and unobservable state of the system. The main feature of the method is that the Gaussian densities used in the approximation are selected as the solution of a convex programming problem which trades off sparsity of the solution with goodness of fit. A meaningful example shows that the proposed method can outperform the widely used interacting multiple model (IMM) filter and GPB2 in terms of accuracy at the expenses of an increase in computational time.     

Arbitrary decay rate for two connected strings with joint anti-damping by boundary output feedback

In this paper, we are concerned with the boundary stabilization of two connected strings with middle joint anti-damping for which all eigenvalues of the (control) free system are located on the right complex plane. We first design an explicit state feedback controller to achieve exponential stability for the closed-loop system. Consequently, we design the output feedback by using infinite-dimensional observer. The backstepping approach is adopted in investigation. It is shown that by using one boundary stabilizer only, the output feedback can make the closed-loop system exponentially stable with arbitrary decay rate.     

Stability analysis for multi-agent systems using the incidence matrix: Quantized communication and formation control

The spectral properties of the incidence matrix of the communication graph are exploited to provide solutions to two multi-agent control problems. In particular, we consider the problem of state agreement with quantized communication and the problem of distance-based formation control. In both cases, stabilizing control laws are provided when the communication graph is a tree. It is shown how the relation between tree graphs and the null space of the corresponding incidence matrix encode fundamental properties for these two multi-agent control problems.     

Fault tolerant control for singular systems with actuator saturation and nonlinear perturbation

In this paper, the problem of robust fault tolerant control for a class of singular systems subject to both time-varying state-dependent nonlinear perturbation and actuator saturation is investigated. A sufficient condition for the existence of a fixed-gain controller is first proposed which guarantees the regularity, impulse-free and stability of the closed-loop system under all possible faults. An optimization problem with LMI constraints is formulated to determine the largest contractively invariant ellipsoid. An adaptive fault tolerant controller is then developed to compensate for the failure effects on the system by estimating the fault and updating the design parameter matrices online. Both of these two controllers are in the form of a saturation avoidance feedback with the advantage of relatively small actuator capacities compared with the high gain counterpart. An example is included to illustrate the proposed procedures and their effectiveness.     

Markov Jump Linear Systems with switching transition rates: Mean square stability with dwell-time

The stability of a class of Markov Jump Linear Systems characterized by piecewise-constant transition rates and system dynamics is investigated. For these Switching Markov Jump Linear Systems, mean square stability is analyzed through the time evolution of the second-order moment of the state. The main result is a sufficient condition that guarantees mean square stability under constraints on the dwell-time between switching instants. An alternative condition based on Kronecker calculus is worked out. It is shown that both the stability criteria admit an LMI implementation.     

Fault-tolerant control for a class of nonlinear sampled-data systems via a Euler approximate observer

In this paper, a fault-tolerant control (FTC) scheme is developed for a class of nonlinear sampled-data systems. First, a Euler approximate discrete model is used to describe the plant under the sampling. Under this model, an observer-based fault estimation method is proposed. To guarantee the accuracy of both the state and fault estimation values, the conditions to make the approximate model consistent with the exact model are presented. Then, an active fault-tolerant controller, which has a constraint condition for the sampling time, is designed to make the faulty system stable. Finally, an inverted pendulum is used to show the efficiency of the proposed method.     

Stochastic source seeking for nonholonomic unicycle

We apply the recently introduced method of stochastic extremum seeking to navigate a nonholonomic unicycle towards the maximum of an unknown, spatially distributed signal field, using only the measurement of the signal at the vehicle’s location but without the measurement of the vehicle’s position. Keeping the forward velocity constant and controlling only the angular velocity, we design a stochastic source seeking control law which employs excitation based on filtered white noise, rather than sinusoidal perturbations used in the existing work. We study stability with the help of stochastic averaging theorems that we recently developed for general nonlinear continuous-time systems with stochastic perturbations. We prove local exponential convergence, both almost surely and in probability, to a small neighborhood near the source. We characterize the convergence speed explicitly and provide design guidelines for maximizing it, as well as for minimizing the residual set near the source. We present a detailed simulation study, including a study of the effect of saturation on the steering input.     

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.