Dynamic output feedback control of a flexible air-breathing hypersonic vehicle via T–S fuzzy approach

By utilising Takagi–Sugeno (T–S) fuzzy set approach, this paper addresses the robust H_∞ dynamic output feedback control for the non-linear longitudinal model of flexible air-breathing hypersonic vehicles (FAHVs). The flight control of FAHVs is highly challenging due to the unique dynamic characteristics, and the intricate couplings between the engine and fight dynamics and external disturbance. Because of the dynamics’ enormous complexity, currently, only the longitudinal dynamics models of FAHVs have been used for controller design. In this work, T–S fuzzy modelling technique is utilised to approach the non-linear dynamics of FAHVs, then a fuzzy model is developed for the output tracking problem of FAHVs. The fuzzy model contains parameter uncertainties and disturbance, which can approach the non-linear dynamics of FAHVs more exactly. The flexible models of FAHVs are difficult to measure because of the complex dynamics and the strong couplings, thus a full-order dynamic output feedback controller is designed for the fuzzy model. A robust H_∞ controller is designed for the obtained closed-loop system. By utilising the Lyapunov functional approach, sufficient solvability conditions for such controllers are established in terms of linear matrix inequalities. Finally, the effectiveness of the proposed T–S fuzzy dynamic output feedback control method is demonstrated by numerical simulations.     

Robust consensus algorithm for multi-agent systems with exogenous disturbances under convergence conditions

In this paper, a robust consensus algorithm is developed and sufficient conditions for convergence to consensus are proposed for a multi-agent system (MAS) with exogenous disturbances subject to partial information. By utilizing H_∞ robust control, differential game theory and a design-based approach, the consensus problem of the MAS with exogenous bounded interference is resolved and the disturbances are restrained, simultaneously. Attention is focused on designing an H_∞ robust controller (the robust consensus algorithm) based on minimisation of our proposed rational and individual cost functions according to goals of the MAS. Furthermore, sufficient conditions for convergence of the robust consensus algorithm are given. An example is employed to demonstrate that our results are effective and more capable to restrain exogenous disturbances than the existing literature.     

Sliding mode control for Markov jump linear uncertain systems with partly unknown transition rates

In this paper, based on sliding mode control approach, the robust stabilisation problem for a class of continuous-time Markovian jump linear uncertain systems with partly unknown transition rates is investigated. The transition rate matrix under consideration covers completely known, boundary known and completely unknown elements. By making use of linear matrix inequalities technique, sufficient conditions are presented to derive the linear switching surface and guarantee the stochastic stability of sliding mode dynamics. Then a sliding mode control law is designed to drive the state trajectory of the closed-loop system to the specified linear switching surface in finite time in spite of the existing uncertainties and unknown transition rates. Finally, an example is given to verify the validity of the theoretical results.     

Feedback nonlinear discrete-time systems

In this paper, we design an adaptive iterative learning control method for a class of high-order nonlinear output feedback discrete-time systems with random initial conditions and iteration-varying desired trajectories. An n-step ahead predictor approach is employed to estimate future outputs. The discrete Nussbaum gain method is incorporated into the control design to deal with unknown control directions. The proposed control algorithm ensures that the tracking error converges to zero asymptotically along the iterative learning axis except for the beginning outputs affected by random initial conditions. A numerical simulation is carried out to demonstrate the efficacy of the presented control laws.     

Sliding-mode and proportional-derivative-type motion control with radial basis function neural network based estimators for wheeled vehicles

An obstacle avoidance problem of rear-steered wheeled vehicles in consideration of the presence of uncertainties is addressed. Modelling errors and additional uncertainties are taken into consideration. Controller designs for driving and steering motors are designed. A proportional-derivative-type driving motor controller and a sliding-mode steering controller combined with radial basis function neural network (RBFNN) based estimators are proposed. The convergence properties of the RBFNN-based estimators are proven by the Stone–Weierstrass theorem. The stability of the proposed control law is proven using Lyapunov stability analysis. The obstacle avoidance strategy utilising the sliding surface adjustment to an existing navigation method is presented. It is concluded that the driving velocity and steering-angle performances of the proposed control system are satisfactory.     

Mixed H2/H∞ robust model predictive control with saturated inputs

In this paper, we investigate the mixed H₂/H_∞ robust model predictive control (RMPC) for polytopic uncertain systems, which refers to the infinite horizon optimal guaranteed cost control (OGCC). To fully use the capability of actuators, we adopt a saturating feedback control law as the control strategy of RMPC. As the saturating feedback control law can be effectively represented by the convex hull of a group of auxiliary linear feedback laws, the auxiliary feedback laws allow us to design the actual feedback control law without consideration of the input constraints directly to achieve the improved performance. Moreover, we suggest the relative weights on the actual and auxiliary feedback laws to the RMPC, which in turn improves the closed-loop system performance. Furthermore, an off-line design of the proposed RMPC is also developed to make it more practical. Numerical studies demonstrate the effectiveness of the proposed algorithm.     

Processing study of a high temperature adhesive

An adhesive-bonding process cycle study was performed for a polyimidesulphone. The high molecular weight, linear aromatic system possesses properties which make it attractive as a processable, low-cost material for elevated temperature applications. The results of a study to better understand the parameters that affect the adhesive properties of the polymer for titanium alloy adherends are presented. These include the tape preparation, the use of a primer and press and simulated autoclave processing conditions. The polymer was characterized using Fourier transform infrared spectroscopy, glass transition temperature determination, flow measurements, and weight loss measurements. The lap shear strength of the adhesive was used to evaluate the effects of the bonding process variations.     

Underactuated fingers controlled by robust and adaptive trajectory following methods

Choosing an appropriate control scheme to alleviate nonlinearities and uncertainties is not a trivial task, especially when models are not easily available and practical evaluation provides the only means for actual performance assessment. Various factors can contribute to these nonlinearities and uncertainties, such as friction and stiction. Thus, this article investigates four different control schemes, namely PID, adaptive, conventional sliding mode control (SMC) and integral sliding mode control (ISMC) which are implemented in the Bristol Elumotion Robot Hand (BERUL) to analyse and overcome the aforementioned problems. The hand has five fingers with 16 joints and all fingers are underactuated. The implementation of the proposed control schemes are challenging since the BERUL fingers have significant friction, stiction and unknown parameters. The fingers are light in weight and fragile. Comparative performance characteristics have shown that the ISMC is the most suitable candidate to provide good experimental trajectory following and positioning control for underactuated BERUL fingers.     

Adaptive recurrent neural network control of uncertain constrained nonholonomic mobile manipulators

In this article, motion/force control problem of a class of constrained mobile manipulators with unknown dynamics is considered. The system is subject to both holonomic and nonholonomic constraints. An adaptive recurrent neural network controller is proposed to deal with the unmodelled system dynamics. The proposed control strategy guarantees that the system motion asymptotically converges to the desired manifold while the constraint force remains bounded. In addition, an adaptive method is proposed to identify the contact surface. Simulation studies are carried out to verify the validation of the proposed approach.     

On constrained infinite-time nonlinear optimal control

In this work, we consider the infinite-time optimal control of input affine nonlinear systems subject to point-wise in time inequality constraints on both the process inputs and outputs. Fundamental to solving this constrained infinite-time nonlinear optimal control (CITNOC) problem is the ability to calculate the value function of it's unconstrained counterpart, the infinite-time nonlinear optimal control (ITNOC) problem. Unfortunately, the traditional ITNOC solution procedure of specifying an objective function and then solving for the optimal control policy and corresponding value function is computationally intractable in all but the simplest of examples. However, in many cases one can easily identify a stabilizing feedback for near operating point regulation. Building from this local policy, the proposed method is to construct a meaningful optimal control objective function as well as its corresponding value function. These functions are then used to analyze the closed-loop stability of the proposed policy. Upon return to the constrained case the constructed objective and value functions are again used to develop a self-consistent constrained finite-time scheme that will, for the first time, provide an exact solution to the CITNOC problem. The mechanics of the proposed method are then illustrated by an example from chemical reactor control.