Prescribed Performance Control Scheme Research Articles

Robust inertia-free attitude takeover control of postcapture combined spacecraft with guaranteed prescribed performance

In this paper, a robust inertia-free attitude takeover control scheme with guaranteed prescribed performance is investigated for postcapture combined spacecraft with consideration of unmeasurable states, unknown inertial property and external disturbance torque. Firstly, to estimate the unavailable angular velocity of combination accurately, a novel finite-time-convergent tracking differentiator is developed with a quite computationally achievable structure free from the unknown nonlinear dynamics of combined spacecraft. Then, a robust inertia-free prescribed performance control scheme is proposed, wherein, the transient and steady-state performance of combined spacecraft is first quantitatively studied by stabilizing the filtered attitude tracking errors. Compared with the existing works, the prominent advantage is that no parameter identifications and no neural or fuzzy nonlinear approximations are needed, which decreases the complexity of robust controller design dramatically. Moreover, the prescribed performance of combined spacecraft is guaranteed a priori without resorting to repeated regulations of the controller parameters. Finally, four illustrative examples are employed to validate the effectiveness of the proposed control scheme and tracking differentiator.

Prescribed performance control for MDF continuous hot pressing hydraulic system

In this paper, a prescribed performance control scheme is proposed for medium density fibreboard (MDF) continuous hot pressing hydraulic system. The performance issues regarding transient and steady state behaviours are explicitly considered in the control design and analysis. Firstly, a special error transformation is introduced into the first subsystem to transform original system with performance constraints into an equivalent 'output constrained' one to achieve prescribed performance guarantees. Subsequently, based on an appropriately designed barrier Lyapunov function (BLF), a controller is designed to stabilise the transformed system. Meanwhile, the novel error transformation effectively facilitates the control law derivation. It is proven that the proposed controller is capable of guaranteeing the satisfaction of the specific constraints on the transformed error and hence the prescribed performance specifications on the position tracking error, as well as the bounds of all closed-loop signals. In particular, in the second step of controller design, a first-order filter is used in conjunction with the traditional backstepping control to eliminate the differential term expansion caused by the derivative of virtual control input. Finally, numerical simulation results are presented to authenticate and validate the effectiveness of the proposed control scheme.

Robust estimation‐free decentralized prescribed performance control of nonaffine nonlinear large‐scale systems

SummaryIn this paper, a low‐complexity robust estimation‐free decentralized prescribed performance control scheme is proposed and analyzed for nonaffine nonlinear large‐scale systems in the presence of unknown nonlinearity and external disturbance. To tackle the high‐order dynamics of each tracking error subsystem, a time‐varying stable manifold involving the output tracking error and its high‐order derivatives is constructed, which is strictly evolved within the envelope of user‐specialized prescribed performance. Sequentially, a robust decentralized controller is devised for each manifold, under which the output tracking error and its high‐order derivatives are proven to converge asymptotically to a small residual domain with prescribed fast convergence rate. Additionally, no specialized approximation technique, adaptive scheme, and disturbance observer are needed, which alleviates the complexity and difficulty of robust decentralized controller design dramatically. Finally, 3 groups of illustrative examples are used to validate the effectiveness of the proposed low‐complexity robust decentralized control scheme for uncertain nonaffine nonlinear large‐scale systems.

Low-complexity differentiator-based decentralized fault-tolerant control of uncertain large-scale nonlinear systems with unknown dead zone

This paper investigates a low-complexity robust decentralized fault-tolerant prescribed performance control scheme for uncertain larger-scale nonlinear systems with consideration of the unknown nonlinearity, actuator failures, dead-zone input, and external disturbance. Firstly, a new simple finite-time-convergent differentiator is developed to obtain the unmeasurable state variables with arbitrary accuracy. Then, a time-varying sliding manifold involving the output tracking error and its high-order derivatives is constructed to tackle the high-order dynamics of subsystems. Sequentially, a robust decentralized fault-tolerant control scheme is proposed for each sliding manifold with prescribed convergence rate. The prominent advantage of the proposed fault-tolerant control scheme is that any specialized approximation technique, disturbance observer, and recursive procedure of backstepping technique are avoided, which dramatically alleviates the complexity of controller design. Finally, two groups of illustrative examples are employed to demonstrate the effectiveness of the low-complexity decentralized fault-tolerant control scheme under the developed finite-time-convergent differentiator.

Indirect adaptive fuzzy prescribed performance control of feedback linearisable MIMO non‐linear systems with unknown control direction

An indirect adaptive fuzzy prescribed performance control scheme is developed for a class of uncertain multi-input and multi-output feedback linearisable non-linear systems with unknown control direction and external disturbances. The properties of symmetric matrix and hyperbolic tangent function are exploited and the predefined tracking performance boundary is used to design the adaptive fuzzy prescribed performance controller, which can avoid the possible singularity problem and calculating the inverse of matrix. A robust controller equipped with a Nussbaum-type function is constructed to compensate for the lumped errors and solve the unknown control direction problem. The proposed scheme guarantees that all the signals in the resulting closed-loop system are bounded and that the tracking errors converge to a small residual set with the prescribed performance bounds. Simulation results are used to demonstrate the effectiveness of the proposed approach.

Fuzzy Adaptive Prescribed Performance Control for a Class of Uncertain Nonlinear Systems with Unknown Dead‐Zone Inputs

This paper proposes a fuzzy adaptive prescribed performance control scheme for a class of uncertain chaotic systems with unknown control gains and unknown dead‐zone inputs. Firstly, an error transformation is introduced to transform the original constrained system into an equivalent unconstrained one. Then, based on the error transformation technique and the predefined performance technique, a fuzzy adaptive feedback control method is developed. It is shown that all the signals of the resulting closed‐loop system are bounded. Finally, an illustrative example is given to demonstrate the effectiveness and usefulness of the proposed technique.

Robust Adaptive Neural Control of Morphing Aircraft with Prescribed Performance

This study proposes a low‐computational composite adaptive neural control scheme for the longitudinal dynamics of a swept‐back wing aircraft subject to parameter uncertainties. To efficiently release the constraint often existing in conventional neural designs, whose closed‐loop stability analysis always necessitates that neural networks (NNs) be confined in the active regions, a smooth switching function is presented to conquer this issue. By integrating minimal learning parameter (MLP) technique, prescribed performance control, and a kind of smooth switching strategy into back‐stepping design, a new composite switching adaptive neural prescribed performance control scheme is proposed and a new type of adaptive laws is constructed for the altitude subsystem. Compared with previous neural control scheme for flight vehicle, the remarkable feature is that the proposed controller not only achieves the prescribed performance including transient and steady property but also addresses the constraint on NN. Two comparative simulations are presented to verify the effectiveness of the proposed controller.

Guaranteeing preselected tracking quality for air-breathing hypersonic non-affine models with an unknown control direction via concise neural control

A simplified neural controller is addressed for the longitudinal dynamics of an air-breathing hypersonic vehicle (AHV) with a completely unknown control direction by utilizing the prescribed performance control scheme. Unlike the existing literatures, the exploited methodology does not require an affine AHV model or any prior information about the sign of control gains. Moreover, the proposed strategy can provide preselected bounds on the transient and steady performance of velocity and altitude tracking errors. The altitude dynamics is converted into a pure feedback formulation with an unknown control direction, based on which, a novel adaptive neural controller that is quite simpler than the ones derived from back-stepping designs is achieved. For the problem of the unknown control direction, a Nussbaum-type function is introduced to handle it. By employing the minimal-learning parameter (MLP) technique to regulate the norm instead of the elements of the ideal weight vector, only one learning parameter is required for neural approximation. Thus, a low computational burden design is obtained. Finally, simulations are performed to verify the presented control approach.