Finite frequency H∞control design for nonlinear systems

This paper presents a wavelet cerebellar model articulation controller (WCMAC) based adaptive control design for nonlinear systems using a particle swarm optimization (PSO). The WCMAC is the main tracking controller and a robust compensation controller is used to compensate for the residual error. In the WCMAC, the adaptive laws of controller parameters are derived using the gradient descent method. However, the initial values of learning rates of these adaptive laws are very important and they affect much to the performance of control systems. In this paper, the particle swarm optimization algorithm is applied to find the optimal learning rates of the parameter adaptation laws. To show the effectiveness of the proposed approach, numerical simulations of magnetic levitation system and inverted pendulum are provided to confirm the applications of the proposed PSO-WCMAC-based control system. The superiority of the proposed control scheme is also evaluated by quantitative comparison with other control schemes.

This paper studies the linear feedback control strategies for nonlinear systems. Asymptotic stability of the closed-loop nonlinear system is guaranteed by means of a Lyapunov function, which can clearly be seen to be the solution of the Hamilton-Jacobi-Bellman equation thus guaranteeing both stability and optimality. The formulated Theorem expresses explicitly the form of minimized functional and gives the sufficient conditions that allow using the linear feedback control for nonlinear system. The numerical simulations the Duffing oscillator and the nonlinear automotive active suspension system are provided to show the effectiveness of this method.

It has been a common consensus that general techniques for stabilization of nonlinear systems are available only for some special classes of nonlinear systems. Control design for nonlinear systems with uncertain components is usually carried out on a per system basis, especially when physical control constraints, and certain control performance measures such as optimum time control are imposed. Elegant adaptive control techniques are difficult to apply to this type of problems. A new neural network based control design is proposed and presented in this paper to deal with a special class of uncertain nonlinear systems with multiple inputs. The desired system dynamics are analyzed and utilized in the process of the proposed intelligent control design. The theoretical results are provided to justify the design procedures. The simulation study is conducted on a second-order bilinear system with two inputs and uncertainties on its parameters. The simulation results indicate that the proposed design approach is effective.

Direct feedback control design for nonlinear systems

Fuzzy control design of nonlinear systems under unreliable communication links: A systematic homogenous polynomial approach

SUMMARY The development of control strategies for controllable passive dampers — ‘semiactive’ damping devices — is complicated by the nonlinear and dissipative nature of the devices and the nonlinear nature of the closed-loop system with any form of parametric feedback control. Control design for nonlinear systems is often achieved by designing a control for a linearized model since strategies for linear systems are straightforward. One such approach is clipped-optimal control in which the desired damper forces are determined from an optimal controller (e.g., linear quadratic regulator, linear quadratic Gaussian, H2 and H∞), which is designed assuming that the damping devices are fully linear actuators that can exert any forces (dissipative or nondissipative), and a secondary bang–bang controller commands the controllable damper to exert forces as close as possible to the desired forces. However, designs using any linearized model generally result in suboptimal (and sometimes very poor) performance because the linear actuator assumption differs from the actual implementation with a dissipative damping device. Thus, one must generally resort to a large-scale parameter study in which the nonlinear system is simulated many times to determine control strategies that are actually optimal for the nonlinear controlled closed-loop system. This paper demonstrates how an approach developed by the authors, which can reduce the model of a system with local modifications to a lower order set of Volterra integral equations in the modification forces, can significantly decrease the computational burden of a complex control design study for controllable dampers. The approach is first adapted to use a state feedback control with a bang–bang on–off secondary controller. Then, the structural and damping device models are detailed. The optimal semiactive control design is demonstrated via both a parameter study and an optimization algorithm. Numerical examples demonstrate that the approach is effective and, compared with a conventional simulation approach, can reduce computation time by two orders of magnitude. Copyright © 2014 John Wiley & Sons, Ltd.

This paper proposes an adaptive sliding mode fault tolerant control design for lipschitz nonlinear system subject to simultaneous actuator and sensor faults. First, in order to estimate the system states, actuator and sensor faults, only one observer with an adaptive nonlinear gain is required where the minimum phase condition is relaxed to delectability and the matching condition is weakened to become a condition related to the system dimensions. Furthermore, our approach is applicable where there are faults greater than outputs. Next, a virtual sensor technique is developed to replace a failed, missing or corrupted sensor to provide a signal having the same effect of the nominal sensor. Then, this study provides an adaptive sliding mode fault tolerant control to achieve an optimal interaction between observer, virtual sensor and controller models. Thus, using the LMI technique with multiobjective optimization performance, sufficient conditions are derived to ensure the closed-loop Lipschitz nonlinear system stability and guarantee that the Lipschitz set of the adaptive sliding mode observer is a subset of the adaptive sliding mode control Lipschitz set. Two illustrative examples the first is the robotic manipulator model and the second is the longitudinal dynamics of the following vehicle system were performed to verify the effectiveness of the proposed approach.

An H∞ tracking-based adaptive fuzzy sliding mode controller (H∞ AFSMC) is proposed for a class of nonlinear systems involving system uncertainties and external disturbances. The H∞ tracking control design method and the adaptive fuzzy sliding mode control technique are combined in the design of the H∞ AFSMC controller. By the Lyapunov theory, it is shown that the proposed controller has the following advantages: it can inhibit the chattering phenomenon and can guarantee the H∞ tracking performance to attenuate the lumped uncertainties caused by the unmodelled dynamics, the approximation error and the external disturbances. Subsequently, the problem of H∞ AFSMC design is characterised in terms of an eigenvalue problem, which can be efficiently solved by using convex optimisation techniques. Finally, an inverted pendulum system is simulated to demonstrate the validity and effectiveness of the proposed controller.

Control Design for Nonlinear Systems via Controller with Quasi-fixed Parameters

The problem of robust reliable control with multi-objective requirements and static-output-feedback (SOF) form is studied for a class of uncertain nonlinear systems subject to vector-bounded nonlinear dynamics, norm-bounded disturbances and actuator faults or the loss of actuator effectiveness. Based on the Lyapunov stability theory and multi-objective optimization strategy, a sufficient condition for the reliable controller design with SOF-form is developed. It can guarantee the robust stability, the given magnitude constraints on control input and system output, the prescribed level on the disturbance attenuation and the optimized quadratic cost performance of the closed-loop system under all possible faults. Thus, the comprehensive performance of the post-fault system is ensured by the designed controller. Finally, a numerical example is provided to verify the effectiveness of our proposed design algorithm.

Analysis on existence of compact set in neural network control for nonlinear systems

In this paper, a new method is proposed to design a sliding mode controller with variable boundary layer for a nonlinear system. In this method, model predictive control (MPC) is used to predict the future boundary layer thickness. In order to predict the behavior of the nonlinear system, a neural network model is used as an internal model. The simulation results show the supremacy of this method in comparison with the conventional method.

This study presents robust control architecture in the sense of variable structure control via a backstepping design. By using systematic backstepping design techniques, closed-loop behavior of an n-order nonlinear system can be transformed into a stability and convergence problem of a fast switched 2nd order system. There are two main parts contained within the proposed control algorithm; one is a nominal control effort generated according to the Lyapunov stability criterion during recursive backstepping processes, and the other belongs to a smooth robust control law designed to eliminate the effects of unknown lumped perturbations. Finally, a Genesio system is used as an illustrated example to demonstrate the robustness of the control algorithm. The feasibility and properties of the proposed method are given by numerical simulations.

This paper focuses on an input-to-state practical stability (ISpS) problem of nonlinear systems which possess unmodeled dynamics in the presence of unstructured uncertainties and dynamic disturbances. The dynamic disturbances depend on the states and the measured output of the system, and its assumption conditions are relaxed compared with the common restrictions. Based on an input-driven filter, fuzzy logic systems are directly used to approximate the unknown and desired control signals instead of the unknown nonlinear functions, and an integrated backstepping technique is used to design an adaptive output-feedback controller that ensures robustness with respect to unknown parameters and uncertain nonlinearities. This paper, by applying the ISpS theory and the generalized small-gain approach, shows that the proposed adaptive fuzzy controller guarantees the closed-loop system being semi-globally uniformly ultimately bounded. A main advantage of the proposed controller is that it contains only three adaptive parameters that need to be updated online, no matter how many states there are in the systems. Finally, the effectiveness of the proposed approach is illustrated by two simulation examples.

To help along the analysis and design of nonlinear control systems the NONLINCON package, an acronym for nonlinear control, was developed. This note addresses the usefulness of symbolic computation, and of the NONLINCON package in particular, for the symbolic analysis and design of nonlinear control systems. The symbolic computation program MAPLE is used as computing substratum. Textbook problems show that the NONLINCON package can be used successfully. A larger scale problem is too complex, however, to be solved with the current versions of NONLINCON and MAPLE. The conclusion is that symbolic computation is a viable approach for textbook problems, but not yet for more complex ones. >