Abstract: Hybrid dynamical systems exhibit continuous and instantaneous changes, having features of continuous-time and discrete-time dynamical systems. Filled with a wealth of examples to illustrate concepts, this book presents a complete theory of robust asymptotic stability for hybrid dynamical systems that is applicable to the design of hybrid control algorithms--algorithms that feature logic, timers, or combinations of digital and analog components. With the tools of modern mathematical analysis, Hybrid Dynamical Systems unifies and generalizes earlier developments in continuous-time and discrete-time nonlinear systems. It presents hybrid system versions of the necessary and sufficient Lyapunov conditions for asymptotic stability, invariance principles, and approximation techniques, and examines the robustness of asymptotic stability, motivated by the goal of designing robust hybrid control algorithms. This self-contained and classroom-tested book requires standard background in mathematical analysis and differential equations or nonlinear systems. It will interest graduate students in engineering as well as students and researchers in control, computer science, and mathematics.

Abstract: Implementation of adaptive backstepping controllers requires analytic calculation of the partial derivatives of certain stabilizing functions. It is well documented that, as the order of a nonlinear system increases, analytic calculation of these derivatives becomes prohibitive. Therefore, in practice, either alternative control approaches are used or the derivatives are neglected in the implementation. Neglecting the derivatives results in the loss of all guarantees proven by Lyapunov methods for the adaptive backstepping approach and may result in instability. This paper presents a new implementation approach for adaptive backstepping control. The main objectives are to facilitate the derivation and implementation of the adaptive backstepping approach, with performance guarantees proven by Lyapunov methods, for applications that were prohibitively difficult using the standard analytic implementation approach. The new approach uses filtering methods to produce certain command signals and their derivatives which eliminates the requirement of analytic differentiation. The approach also introduces filters to generate certain compensating signals necessary to compute compensated tracking errors suitable for adaptive parameter estimation. We present a set of Lemmas and Theorems to analyze the performance both during the initialization and the operating phases. We show that the initialization phase is of finite duration that can be controlled by selection of a design parameter. We also show that all signals within the system are bounded during this short initialization phase. During the operating phase, we show that the command filtered implementation approach has theoretical properties identical to those of the conventional approach. The general approach is presented and analyzed for systems in generalized parameter strict feedback form. Extensions of the approach are presented to demonstrate the application of the method to a land vehicle trajectory following application. Application and effectiveness of the proposed method is shown by simulation results.

Abstract: A EROSPACE engineering applications greatly stimulated the development of optimal control theory during the 1950s and 1960s, where the objective was to drive the system states in such a way that some defined cost was minimized. This turned out to have very useful applications in the design of regulators (where some steady state is to be maintained) and in tracking control strategies (where some predetermined state trajectory is to be followed). Among such applications was the problem of optimal flight trajectories for aircraft and space vehicles. Linear optimal control theory in particular has been very well documented and widely applied, where the plant that is controlled is assumed linear and the feedback controller is constrained to be linear with respect to its input. However, the availability of powerful low-cost microprocessors has spurred great advantages in the theory and applications of nonlinear control. The competitive era of rapid technological change, particularly in aerospace exploration, now demands stringent accuracy and cost requirements in nonlinear control systems. This has motivated the rapid development of nonlinear control theory for application to challenging, complex, dynamical real-world problems, particularly those that bear major practical significance in aerospace, marine, and defense industries. Infinite-time horizon nonlinear optimal control (ITHNOC) presents a viable option for synthesizing stabilizing controllers for nonlinear systems by making a state-input tradeoff, where the objective is to minimize the cost given by a performance index. The original theory of nonlinear optimal control dates from the 1960s. Various theoretical and practical aspects of the problem have been addressed in the literature over the decades since. In particular, the continuous-time nonlinear deterministic optimal control problem associated with autonomous (time-invariant) nonlinear regulator systems that are affine (linear) in the controls has been studied by many authors. The long-established theory of optimal control offers quite mature and well-documented techniques for solving this control-affine nonlinear optimization problem, based on dynamic programming or calculus of variations, but their application is generally a very tedious task. Bellman’s dynamic programming approach reduces to solving a nonlinear first-order partial differential equation (PDE), expressed by the Hamilton–Jacobi–Bellman (HJB) equation. The solution to the HJB equation gives the optimal performance/cost value (or storage) function and determines an optimal control in feedback form under some smoothness assumptions. Alternatively, in the classical calculus of variations, optimal control problems can be characterized locally in terms of the Hamiltonian dynamics arising from Pontryagin’s minimum principle. These are the characteristic equations of the HJB PDE, which result in a nonlinear, constrained two-point boundary value problem (TPBVP) that, in general, can only be solved by successive approximation of the optimal control input using iterative numerical techniques for each set of initial conditions. Numerically, even though the nonlinear TPBVP is somewhat easier to solve than the HJBPDE, control signals can only be determined offline and are thus best suited for feedforward control of plants for which the state trajectories are known a priori. Therefore, contrary to the dynamic programming approach, the resultant control law is not generally in feedback form. Open-loop control, however, is sensitive to random disturbances and requires that the initial state be on the optimal trajectory. In contrast, nonlinear optimal feedback has inherent robustness properties (inherent in the sense that it is obtained by ignoring uncertainty and disturbances). The potential difficulty with the HJB approach is that no efficient algorithm is available to solve the PDE when it is nonlinear and the problem dimension is high, making it impossible to derive exact expressions for optimal controls for most nontrivial problems of interest. The optimal can only be computed in special cases, such as linear dynamics and quadratic cost, or very low-dimensional systems. In particular, if the plant is linear time invariant (LTI) and the (infinite-time) performance index is quadratic, then the corresponding HJB equation for this infamous linear-quadratic regulator (LQR) problem reduces to an algebraic Riccati equation (ARE). Contrary to the well-developed and widely applied theory and computational tools for theRiccati equation (for example, see [1]), theHJB equation is difficult, if not impossible, to solve for most practical applications. The exact solution for the optimal control policies is very complex

Abstract: Finite-state models of control systems were proposed by several researchers as a convenient mechanism to synthesize controllers enforcing complex specifications. Most techniques for the construction of such symbolic models have two main drawbacks: either they can only be applied to restrictive classes of systems, or they require the exact computation of reachable sets. In this paper, we propose a new abstraction technique that is applicable to any nonlinear sampled-data control system as long as we are only interested in its behavior in a compact set. Moreover, the exact computation of reachable sets is not required. The effectiveness of the proposed results is illustrated by synthesizing a controller to steer a vehicle.

Abstract: Sampling arises simultaneously with input and output delays in networked control systems. When the delay is left uncompensated, the sampling period is generally required to be sufficiently small, the delay sufficiently short, and, for nonlinear systems, only semiglobal practical stability is generally achieved. For example, global stabilization of strict-feedforward systems under sampled measurements, sampled-data stabilization of the nonholonomic unicycle with arbitrarily sparse sampling, and sampled-data stabilization of LTI systems over networks with long delays, are open problems. In this paper, we present two general results that address these example problems as special cases. First, we present global asymptotic stabilizers for forward complete systems under arbitrarily long input and output delays, with arbitrarily long sampling periods, and with continuous application of the control input. Second, we consider systems with sampled measurements and with control applied through a zero-order hold, under the assumption that the system is stabilizable under sampled-data feedback for some sampling period, and then construct sampled-data feedback laws that achieve global asymptotic stabilization under arbitrarily long input and measurement delays. All the results employ “nominal” feedback laws designed for the continuous-time systems in the absence of delays, combined with “predictor-based” compensation of delays and the effect of sampling.

Abstract: In the literature, it has been proved that under a lower-triangular linear growth condition, a class of uncertain nonlinear systems can be globally stabilized by a linear state feedback controller (Tsinias) and later by a linear output feedback controller (Qian and Lin), both in the continuous-time form. This technical note shows that the same continuous-time system under the same assumption can be globally stabilized by a sampled-data output feedback controller whose observer and control law are discrete-time and linear, and hence can be easily implemented by computers.

Abstract: This paper presents a control design for nonlinear systems with state constraints, based on the use of our newly introduced Integral Barrier Lyapunov Functionals (iBLF). The integral functional allow the mixing of the original state constraints with the errors in a form amenable to stable backstepping control design. This reduces some of the conservatism associated with the use of purely error-based functions with transformed error constraints. We show that, under the proposed iBLF-based control, output tracking error is bounded by an exponentially decreasing function of time, all states always remain in the constrained state space, and that the stabilizing functions and control input are bounded, subject to significantly relaxed feasibility conditions. A numerical example illustrates the performance of the proposed control.

Abstract: In this note, we consider a class of uncertain dynamic nonlinear systems preceded by Bouc-Wen type of hysteresis nonlinearity. A new perfect inverse function of the hysteresis is constructed and used to cancel the hysteresis effects in controller design with backstepping technique. For the design and implementation of the controller, no knowledge is assumed on system parameters. It is shown that the proposed controller not only guarantees asymptotic stability, but also transient performance.

Abstract: SUMMARY This paper addresses the problem of using output feedback to globally control a class of nonlinear systems whose output functions are not precisely known. First, for the nominal linear system, we design a homogeneous state compensator without requiring precise information of the output function, and construct a nonlinear stabilizer with adjustable coefficients by using the generalized adding a power integrator technique. Then based on the homogeneous domination approach, a scaling gain is introduced into the proposed output feedback controller, which can be used by tuning the scaling gain to solve: (i) the problem of global output feedback stabilization for a class of upper-triangular systems; and (ii) the problem of global practical output tracking for a class of lower-triangular systems. Copyright © 2011 John Wiley & Sons, Ltd.

Abstract: In this paper, we propose a decentralized adaptive robust controller for trajectory tracking of robot manipulators. In each local controller, a disturbance observer (DOB) is introduced to compensate for the low-passed coupled uncertainties, and an adaptive sliding mode control term is employed to handle the fast-changing components of the uncertainties beyond the pass-band of the DOB. In contrast to most of the local controllers using DOB for robot manipulators that are based on linear control theory, in this study, by some special nonlinear damping terms, the boundedness of the signals of the overall nonlinear system is first ensured. This paves the way to analyze how the DOB and adaptive sliding mode control play in a cooperative way in each local subsystem to achieve an excellent control performance. Simulation results are provided to support the theoretical results.

Abstract: Sliding mode control is an important method used in nonlinear control systems. In robust control systems, the sliding mode control is often adopted due to its inherent advantages of easy realization, fast response and good transient performance as well as its insensitivity to parameter uncertainties and disturbances. In this paper, we derive new results based on the sliding mode control for the anti-synchronization of identical Qi three-dimensional (3D) four-wing chaotic systems (2008) and identical Liu 3D four-wing chaotic systems (2009). The stability results for the anti-synchronization schemes derived in this paper using sliding mode control (SMC) are established using Lyapunov stability theory. Since the Lyapunov exponents are not required for these calculations, the SMC method is very effective and convenient to achieve global chaos anti-synchronization of the identical Qi four-wing chaotic systems and identical Liu four-wing chaotic systems. Numerical simulations are shown to illustrate and validate the synchronization schemes derived in this paper.

Abstract: The paper addresses the problem of row straightening of agents via local interactions. A nonlinear control protocol that ensures finite-time equidistant allocation on a segment is proposed. With the designed protocol, any settling time can be guaranteed regardless of the initial conditions. A robust modification of the control algorithm based on sliding mode control technique is presented. The case of multidimensional agents is also considered. The theoretical results are illustrated via numerical simulations.

Abstract: An innovative approach to adaptive fuzzy sliding mode control for a class of SISO continuous nonlinear systems with unknown dynamics and bounded disturbances is introduced in this paper. The main idea of the presented method consists in the introduction of the fuzzy self-tuning mechanism for adaptation of the sliding mode control parameters - extended feedback and switching gains. Such modification reduces the well-known chattering problem in classical sliding mode control. In comparison with the other algorithms eliminating this problem the proposed method results in faster convergence and more transparent and interpretable design of self-tuning mechanism. Moreover, the proposed method guaranteing the asymptotic reference signal tracking with bounded system signals can be easily implemented to high order systems. The performance of the presented control design is demonstrated on control of a nonlinear electro-hydraulic servo mechanism.

Abstract: Closed-loop control of skeletal muscle is complicated by the nonlinear muscle force to length and velocity relationships and the inherent unstructured and time-varying uncertainties in available models. Some pure feedback methods have been developed with some success, but the most promising and popular control methods for neuromuscular electrical stimulation (NMES) are neural network (NN)-based methods. Efforts in this paper focus on the use of a NN feedforward controller that is augmented with a continuous robust feedback term to yield an asymptotic result (in lieu of typical uniformly ultimately bounded stability). Specifically, an NN-based controller and Lyapunov-based stability analysis are provided to enable semi-global asymptotic tracking of a desired limb time-varying trajectory (i.e., non-isometric contractions). The developed controller is applied as an amplitude modulated voltage to external electrodes attached to the distal-medial and proximal-lateral portion of the quadriceps femoris muscle group in non-impaired volunteers. The added value of incorporating a NN feedforward term is illustrated through experiments that compare the developed controller with and without the NN feedforward component.

Abstract: This paper presents a nonlinear optimal speed controller based on a state-dependent Riccati equation (SDRE) for permanent magnet synchronous motor (PMSM). An SDRE-based near optimal load torque observer is also proposed to provide the load torque information for the controller. In both designs, the stability is analytically proven, and the Taylor series method is used to find an approximate solution because the SDRE cannot be directly solved. The SDRE-based optimal controller and the observer can ensure better control performance such as no overshoot and fast transient response in speed tracking than the linear conventional controllers such as linear quadratic regulator and proportional-integral controller even under the variations of the model parameters and load torque. The proposed SDRE-based control strategy is implemented on a PMSM testbed using TMS320F28335 DSP. The simulation and experimental results are given to prove the feasibility of the proposed control scheme.

Abstract: A helicopter maneuvers naturally in an environment where the execution of the task can easily be affected by atmospheric turbulence, which leads to variations of its model parameters. This paper discusses the nature of the disturbances acting on the helicopter and proposes an approach to counter the effects. The disturbance consists of vertical and lateral wind gusts. A 7-degrees-of-freedom (DOF) nonlinear Lagrangian model with unknown disturbances is used. The model presents quite interesting control challenges due to nonlinearities, aerodynamic forces, under actuation, and its non-minimum phase dynamics. Two approaches of robust control are compared via simulations with a Tiny CP3 helicopter model: an approximate feedback linearization and an active disturbance rejection control using the approximate feedback linearization procedure. Several simulations show that adding an observer can compensate the effect of disturbances. The proposed controller has been tested in a real-time application to control the yaw angular displacement of a Tiny CP3 mini-helicopter mounted on an experiment platform.

Abstract: In this paper, we study the lag synchronization (LS) of n-dimensional hyperchaotic complex nonlinear systems. The idea of the nonlinear control technique based on the complex Lyapunov function with lag in time is used to propose a scheme to investigate LS of hyperchaotic attractors of these systems. Both complex Lyapunov and control functions are introduced. For illustration, the scheme is applied to two hyperchaotic complex Lorenz systems. The real and complex control functions are derived analytically to achieve LS and to show that the complex error dynamical systems are globally stable. Numerical results are calculated to test the validity of the analytical expressions of control functions to achieve LS of two identical hyperchaotic attractors.

Abstract: This paper deals with the benefits of using a nonlinear model-based approach for controlling magnetically guided therapeutic microrobots in the cardiovascular system. Such robots used for minimally invasive interventions consist of a polymer binded aggregate of nanosized ferromagnetic particles functionalized by drug-conjugated micelles. The proposed modeling addresses wall effects (blood velocity in minor and major vessels' bifurcations, pulsatile blood flow and vessel walls, and effect of robot-to-vessel diameter ratio), wall interactions (contact, van der Waals, electrostatic, and steric forces), non-Newtonian behavior of blood, and different driving designs as well. Despite nonlinear and thorough, the resulting model can both be exploited to improve the targeting ability and be controlled in closed-loop using nonlinear control theory tools. In particular, we infer from the model an optimization of both the designs and the reference trajectory to minimize the control efforts. Efficiency and robustness to noise and model parameter's uncertainties are then illustrated through simulations results for a bead pulled robot of radius in a small artery.

Abstract: The aim of this work was twofold: on the one hand, to describe a comparative study of two intelligent control techniques-fuzzy and intelligent proportional-integral (PI) control, and on the other, to try to provide an answer to an as yet unsolved topic in the automotive sector-stop-and-go control in urban environments at very low speeds. Commercial vehicles exhibit nonlinear behavior and therefore constitute an excellent platform on which to check the controllers. This paper describes the design, tuning, and evaluation of the controllers performing actions on the longitudinal control of a car-the throttle and brake pedals-to accomplish stop-and-go manoeuvres. They are tested in two steps. First, a simulation model is used to design and tune the controllers, and second, these controllers are implemented in the commercial vehicle-which has automatic driving capabilities-to check their behavior. A stop-and-go manoeuvre is implemented with the two control techniques using two cooperating vehicles.

Abstract: This paper addresses the problem of synchronized path following of multiple homogenous underactuated autonomous underwater vehicles (AUVs). The dedicated control laws are categorized into two envelopes: One is steering individual underwater vehicle to track along predefined path, and the other is ensuring tracked paths of multiple vehicles to be synchronized, by means of decentralized speed adaption under the constraints of multi-vehicle communication topology. With these two tasks formulation, geometric path following is built on Lyapunov theory and backstepping techniques, while injecting helmsman behavior into classic individual path following control. Synchronization of path parameters are reached by using a mixture of tools from linear algebra, graph theory and nonlinear control theory. A simple but effective control design on direct inter-vehicle speed adaption with minimized communication variables, enables the multi-AUV systems to be synchronized and stabilized into an invariant manifold, and all speeds converge to desired assignments as a byproduct. Simulation results illustrate the performance of the synchronized path following control laws proposed.