Chang Tan

Adaptive control schemes for discrete-time T-S fuzzy systems with unknown parameters and actuator failures

This paper develops a new solution framework with detailed system modeling, and control design, analysis and evaluation, for adaptive control of discrete-time input-output multiple-delay T-S fuzzy systems with unknown parameters and uncertain actuator failures. A multiple-delay prediction fuzzy system model is derived and its minimum phase property is clarified. Based on a model-based approach, the design and analysis are presented for an adaptive control scheme for multiple-delay T-S fuzzy systems, and an adaptive actuator failure compensation for systems with redundant actuators subject to uncertain failures, for which new system parametrizations and controller structures are developed. Illustrative examples and simulation results are presented to demonstrate the studied new concepts and to verify the desired performance of the new types of adaptive fuzzy control systems.

Adaptive control of discrete-time state-space T--S fuzzy systems with general relative degree

This paper develops a new solution framework for adaptive control of general discrete-time state-space T-S fuzzy systems with a relative degree ρ (1 ≤ ρ ≤ n). A new procedure is proposed to construct a global T-S fuzzy system model from local state-space models in non-canonical form, which has an explicit relative degree structure and a specific input-output signal causality relationship in the sense that it does not include any future values of fuzzy membership functions. An adaptive feedback control scheme is designed based on the global T-S fuzzy model, to ensure desired stability and tracking. As an illustrative example, a T-S fuzzy system is constructed based on the linearized local models of a transport airplane. Simulation results have demonstrated the developed new concepts and verified the desired performance of the new type of adaptive fuzzy control systems.

A discrete-time parameter estimation based adaptive actuator failure compensation control scheme

This paper studies discrete-time adaptive failure compensation control of systems with uncertain actuator failures, using an indirect adaptive control method. A discrete time model of a continuous-time linear system with actuator failures is derived and its key features are clarified. A new discrete-time adaptive actuator failure compensation control scheme is developed, which consists of a total parametrization of the system with parameter and failure uncertainties, a stable adaptive parameter estimation algorithm, and an on-line design procedure for feedback control. This work represents a new design of direct adaptive compensation of uncertain actuator failures, using an indirect adaptive control method. Such an adaptive design ensures desired closed-loop system stability and asymptotic tracking properties despite uncertain actuator failures. Simulation results are presented to show the desired adaptive actuator failure compensation performance.

A Multiple-Model Based Adaptive Actuator Failure Compensation Scheme for Control of Near-Space Vehicles*

Abstract This paper presents a new adaptive actuator failure compensation control scheme for nonlinear systems motivated from a near space vehicle control application, using a multiple-model failure estimation and control approach. Such a design employs multiple controllers, each of which is designed for each failure pattern in the failure pattern set of interest and is constructed with parameters from adaptive estimates of the failure parameters of a specific actuator failure. Each estimator is based on a complete parametrization of the corresponding actuator failure. A switching mechanism is set up, based on finding the minimal performance cost index from the estimation errors, to select the most appropriate controller to generate the current control signal. Simulation results from the attitude control of a near space vehicle dynamic model in the presence of uncertain actuator failures are presented to show the desired adaptive actuator failure compensation performance.

A multiple-model MRAC scheme for multivariable systems with matching uncertainties

This paper develops a multivariable multiple-model adaptive control scheme for adaptive state feedback state tracking control of systems whose plant-model matching conditions are uncertain and parameters are unknown. To deal with the uncertainty of plant-model matching conditions needed for adaptive control, multiple reference model systems are employed to generate multiple parameter estimation and feedback control signals from which a most suitable control input is selected by a control switching mechanism designed using multiple estimation errors. Such a new multiple-model control design is based on an expanded control system parametrization which has the capacity to cover system structural uncertainties. Stability analysis and simulation results ensure and verify the desired adaptive control system stability and tracking performance.

A direct MRAC based multivariable multiple-model switching control scheme

Abstract In this paper a direct model reference adaptive control (MRAC) based multiple-model switching control scheme is developed for linear multivariable plants. Such a scheme is capable of ensuring desired system performance, avoiding control singularity and possible persistent control switching. A plant signal identity is used to derive a bank of parameter estimators which are initialized from different parameter subregions. A bank of adaptive controllers are constructed, whose parameters are directly updated from the estimators with globally stable adaptive laws for desired parameter adaptation without control gain matrix inversion to avoid control singularity. A control switching mechanism is designed with performance indexes formed from estimation errors which are directly used in the controller parameter update laws and are closely related to the tracking error, together with the use of a lower threshold switching parameter to ensure the eventual settle-down of the control switching. Closed-loop stability and output tracking are analyzed, and some extensions of the multiple-model design are given. Simulation results are presented to show the desired system performance, especially, improved system transient responses.

Adaptive actuator failure compensation using multiple-model switching

A new adaptive actuator failure compensation control scheme is developed, using a multi-layer multiple-model and switching approach, for effective and faster compensation of failure uncertainties, and improvement of system transient performance. A basic-layer multiple-model adaptive compensation control structure is first developed to ensure effective failure compensation, by using a set of basic multiple adaptive controllers, each designed for one failure pattern from a failure pattern set. Then, a multi-layer multiple-model adaptive control scheme is employed to improve system transient performance, using multiple groups of adaptive controllers. Each group is designed by expanding each basic controller structure to a set of subgroups of controllers, with controllers from different subgroups initialized from different parameter subregions, and controllers within each subgroup updated by different adaptation gains. A control switching mechanism is designed based on cost functions formed from tracking error related estimation errors, and selects the applied control input from the multiple control signals, which corresponds to the minimal cost function. Simulation results from an aircraft flight control example are presented to show the desired system performance despite the presence of uncertain actuator failures.

A Lyapunov method based multiple-model adaptive actuator failure compensation scheme for control of near-space vehicles

In a recent paper [7], a multiple-model adaptive actuator failure compensation control scheme is proposed for the control of a near-space vehicle, using the gradient algorithm, to achieve fast and accurate actuator failures compensation. In this paper, a new multiple-model adaptive actuator failure compensation control scheme is developed for nonlinear systems motivated from a near-space vehicle control application. Such a design also employs multiple controllers based on multiple-model failure estimations and a control switching mechanism, based on finding the minimal performance cost index, to select the most appropriate controller. Different from [7], each estimator is designed based on the Lyapunov method, which ensures the system stability and desired tracking properties. Moreover, a smooth control are introduced to the multiple-model control system frame to avoid the discontinuity problem from the control switching, to widen the application of such design. Simulation results for a near-space vehicle dynamic model are presented to show the desired failure compensation performance.

A multiple-model adaptive control scheme for multivariable systems with uncertain actuation signs

A new multiple-model adaptive switching control scheme, using a direct adaptive control approach to avoid control gain estimate singularity, is developed for multivariable systems with uncertain actuation signs. Such an adaptive controller is capable of handling the adaptive control design condition uncertainty caused by actuation sign uncertainty. Multiple direct adaptive controllers are designed for all possible patterns of actuation signs, and each of them is updated by an adaptive law corresponding to one particular actuation sign pattern. A control switching mechanism is designed with multiple modified performance indexes based on normalized estimation errors, which is desirable for selecting the best controller. An aircraft flight control example is presented to show the effectiveness of the proposed adaptive control scheme.

Multiple-model based adaptive control design for parametric and matching uncertainties

This paper develops a new multiple-model adaptive control scheme to expand the capacity of state feedback state tracking adaptive control to handle both the plant-model matching and parameter uncertainties for single-input LTI systems. First, a multiple-model adaptive control design is derived for systems with uncertain parameter matrices in general forms, whose multiple controllers are implemented with multiple estimates of a single parameter vector defined under a matching condition for a single reference model system. Then, the new scheme is developed to relax the matching condition, using multiple reference model systems (only one of which is required to be able to match the controlled plant), and multiple controllers (which are updated from adaptive laws generated from multiple reference model systems based estimation errors), as two key features of the new design to deal with the matching uncertainty. A switching mechanism is constructed using those multiple estimation errors, capable of selecting the suitable control input from the multiple control signals (it is uncertain which of them can lead to a stable closed-loop system), to achieve the desired system performance. Such a new design has the capacity to relax some practical design conditions, as demonstrated by an aircraft flight control example.