Adaptive neural network control for nonstrict-feedback uncertain nonlinear systems with input delay and asymmetric time-varying state constraints

This paper is devoted to adaptive neural network control issue for a class of nonstrict-feedback uncertain systems with input delay and asymmetric time-varying state constraints. State-related external disturbances are involved into the system, and the upper bounds of disturbances are assumed as functions of state variables instead of constants. Additionally, during the approximations of unknown functions by neural networks, the online computation burdens are declined sharply, since the norms of neural network weight vectors are only estimated. In the process of dealing with input delay, an auxiliary function is applied such that the conditions for time delay are more general than the ones in existing literature. A novel adaptive neural network controller is designed by constructing the asymmetric barrier Lyapunov function, which guarantees that the output of system has a good tracking performance and the state variables never violate the asymmetric time-varying constraints. Finally, numerical simulations are presented to verify the proposed adaptive control scheme.     

Fixed-time neuroadaptive practical tracking control based on extended state/disturbance observer for a QUAV with external disturbances and time-varying parameters

This paper investigates the fixed-time neural network adaptive (FNNA) tracking control of a quadrotor unmanned aerial vehicle (QUAV) to achieve flight safety and high efficiency. By combining radial basis function neural network (RBFNN) with fixed time adaptive sliding mode algorithm, a novel radial basis function neural network adaptive law is proposed. In addition, an extended state/disturbance observer (ESDO) is proposed to solve the problem of unmeasurable state and external interference, which can obtain reliable state feedback and interference input. Unlike most other ESO applications, this paper does not set the uncertainty model and external disturbances as total disturbances. Instead, the external disturbances are observed by extending the states and the observed states are fed back to the controller to cancel the disturbances. In view of the time-varying resistance coefficient and inertia torque in the QUAV model, the neural network is introduced so that the controller does not need to consider these nonlinear uncertainties. Finally, a numerical example is given to verify the effectiveness of the coupled non-simplified QUAV model.     

Sliding mode attitude tracking of rigid spacecraft with disturbances

The attitude tracking control problem of a spacecraft nonlinear model with external disturbances and inertia uncertainties is addressed in this paper. First, a new sliding mode controller is designed to ensure the asymptotic convergence of the attitude and angular velocity tracking errors against external disturbances and inertia uncertainties by using a modified differentiator to estimate the total disturbances. Second, an adaptive algorithm is applied to compensating the disturbances, by which another sliding mode controller is successfully designed to achieve a high performance on the attitude tracking in the presence of the inertia uncertainties, external disturbances and actuator saturations. Finally, simulation results are presented to illustrate effectiveness of the control strategies.     

An adaptive fast super-twisting disturbance observer-based dual closed-loop attitude control with fixed-time convergence for UAV

In this paper, a fixed-time dual closed-loop attitude control method is investigated for a quadrotor unmanned aerial vehicle. Firstly, a fixed-time adaptive fast super-twisting disturbance observer is presented for estimating the unknown external disturbance. A modified adaptive law is employed based on an equivalent control method to obtain proper observer gains. Secondly, a fixed-time controller is designed by using a universal barrier Lyapunov function to satisfy asymmetric tracking error constraints. Then, a tracking differentiator is utilised to arrange the transition process. Finally, the implementation of the developed method in a quadrotor unmanned aerial vehicle is performed. Through stability analysis and simulation results, the effectiveness and superiority of the proposed fixed-time control method are validated.     

Fixed-time stabilization of periodic linear systems and its application to the elliptical spacecraft rendezvous

A smooth periodic delayed feedback (SPDF) control scheme is proposed for the fixed-time stabilization problem of linear periodic systems subject to input delay. By investigating the monodromy matrix of the periodic system, it is proved that the SPDF controller can achieve the fixed-time stabilization of linear periodic systems with arbitrarily long yet bounded input delays under the condition that the original system is uniformly completely controllable. The proposed controller is continuously differentiable and smooth. The SPDF control scheme is then applied to the elliptical spacecraft rendezvous problem. The effectiveness of the established method is verified on numerical simulations.     

Model predictive tracking control with disturbance compensation for wheeled mobile robots in an environment with obstacles

This paper explores the trajectory tracking control problem for a wheeled mobile robot (WMR) in an environment with obstacles and unknown disturbances. A fixed-time extended state observer is presented, which is utilized to estimate unknown disturbances and improve the convergence speed of estimation errors. By introducing the obstacle avoidance cost, a model predictive controller with disturbance compensation is proposed to guarantee desired tracking performance in the presence of obstacles. The proposed method is analyzed for recursive feasibility and closed-loop system stability subject to unknown disturbances and obstacles. Finally, both simulation and experiment are conducted to express the satisfactory tracking effect of the developed approach.     

Robust H∞ dynamic output-feedback control for four-wheel independently actuated electric ground vehicles through integrated AFS/DYC

This paper simultaneously addresses the parameter/state uncertainties, external disturbances, input saturations, and actuator faults in the handling and stability control for four-wheel independently actuated (FWIA) electric ground vehicles (EGVs). Considering the high cost of the available sensors for vehicle lateral velocity measurement, a robust H_∞ dynamic output-feedback controller is designed to control the vehicle motion without using the lateral velocity information. The investigated parameter/state uncertainties include the tire cornering stiffness, vehicle mass, and vehicle longitudinal velocity. The unmodeled terms in the vehicle lateral dynamics model are dealt as the external disturbances. Faults of the active steering system and in-wheel motors can cause dangerous consequences for driving, and are considered in the control design. Input saturation issues for the tire forces can deteriorate the control effects, and are handled by the proposed strategy. Integrated control with active front steering (AFS) and direct yaw moment (DYC) is adopted to control the vehicle yaw rate and sideslip angle simultaneously. Simulation results based on a high-fidelity and full-car model via CarSim-Simulink show the effectiveness of the proposed control approach.     

Fixed-time nonlinear homogeneous sliding mode approach for robust tracking control of multirotor aircraft: Experimental validation

This paper presents a robust scheme for fixed-time tracking control of a multirotor system. The aircraft is subjected to matched lumped disturbances, i.e., unmodeled dynamics, parameters uncertainties, and external perturbations besides measurement noise. Firstly, a novel Nonlinear Homogeneous Continuous Terminal Sliding Manifold (NHCTSM) based on the weighted homogeneity theory is presented. The sliding manifold is designed with prescribed dynamics featuring Global Asymptotic Stability (GAS) and fixed-time convergence. Then, a novel Fixed-time Non-switching Homogeneous Nonsingular Terminal Sliding Mode Control (FNHNTSMC) is proposed for the position and attitude loops by employing the developed NHCTSM and an appropriate reaching law. Moreover, the control framework incorporates a disturbance observer to feedforward and compensate for the disturbances. The designed control scheme can drive the states of the system to the desired references in fixed-time irrespective of the values of the Initial Conditions (ICs). Since the existing works on homogeneous controllers rely on the bi-limit homogeneity concept in the convergence proofs, the estimate of the settling-time or its upper-bound cannot be given explicitly. In contrast, this study employs Lyapunov Quadratic Function (LQF) and Algebraic Lyapunov Equation (ALE) in the stability analysis of both controller and observer. Following this method, an expression of the upper-bound of the settling-time is explicitly derived. Furthermore, to assure the Uniform Ultimate Boundedness (UUB) of all signals in the feedback system, the dynamics of the observer and controller are jointly analyzed. Simulations and experiments are conducted to quantify the control performance. The proposed approach achieves superior performance compared with recent literature on fixed-time/finite-time control and a commercially available PID controller. The comparative results witness that the developed control scheme improves the convergence-time, accuracy, and robustness while overcoming the singularity issue and mitigating the chattering effect of conventional SMC.     

Fixed-time stabilization of general linear systems with input delay

In this paper, the fixed-time stabilization control problem for general linear systems with input delay is addressed. In addition to the Artstein–Kwon–Pearson reduction transformation, a pre-compensation control structure is established first to convert the original system into a single input delay-free linear system. Then, we show that the origin of the transformed system is fixed-time stabilizable by an additional homogeneous control design if the original system is controllable. Finally, an example is used to validate the proposed method via simulation results.     

Adaptive fuzzy output feedback controller design for a HAGC system with input saturation and output error constraints

The comprehensive effect of external disturbance, measurement delay, unmeasurable states and input saturation makes the difficulties and challenges for a HAGC system. In this paper, an adaptive fuzzy output feedback control scheme is designed for a HAGC system under the simultaneous consideration of those factors. At the first place, by state transformation technique, the dynamic model of a HAGC system is simply expressed as a strict feedback form, where measurement delay is converted into input delay. Then, an auxiliary system is employed to compensate for the effect of input delay. Furthermore, an asymmetric barrier Lyapunov function (BLF) is constructed to ensure the output error constraint requirement of thickness error and the fuzzy observer is established to solve unmeasurable states, unknown nonlinear functions at the same time. With the aid of backstepping method, adaptive fuzzy controller is developed to assure that the closed-loop system is semi-globally boundedness and the output error of thickness error doesn’t violate its constraint. At the end, compared simulations are carried out to verify the efficiency of the proposed control scheme.     

Fixed-time composite neural learning control of state-constrained nonlinear uncertain systems

This paper presents a fixed-time composite neural learning control scheme for nonlinear strict-feedback systems subject to unknown dynamics and state constraints. To address the problem of state constraints, a new unified universal barrier Lyapunov function is proposed to convert the constrained system into an unconstrained one. Taking the unconstrained system, a modified fixed-time convergence state predictor is explored, enabling the prediction error for compensating the neural adaptive law to be obtained and improving the learning ability of online neural networks (NNs). Without employing fractional power terms or a complicated switching strategy to build the control law, a new method of constructing a smooth fixed-time dynamic surface control scheme is proposed. This overcomes the potential singularity problem and the explosion of complexity often encountered in fixed-time back-stepping designs. The representative features of our design are threefold. First, it is free of the fractional power terms, yet offers fixed-time convergence. Second, it addresses the state constraint problem without requiring a feasibility check. Third, it constructs a new state-predictor and enhances the approximation accuracy of NNs. The stability of the proposed control scheme is analyzed using the Lyapunov technique. Simulation results are presented to illustrate the effectiveness of the proposed controller.     

Memory-based controller design for neutral time-delay systems with input saturations: A novel delay-dependent polytopic approach

In this paper, a new memory-based control problem is addressed for neutral systems with time-varying delay, input saturations and energy bounded disturbances. Attention is focused on the design of a memory-based state feedback controller such that the closed-loop system achieves the desirable performance indices including the boundedness of the state trajectories, the H_∞ disturbance rejection/attenuation level as well as the asymptotic stability. By using the combination of a novel delay-dependent polytopic approach, augmented Lyapunov–Krasovskii functionals and some integral inequalities, delay-dependent sufficient conditions are first proposed in terms of linear matrix inequalities. Then, three convex optimization problems are formulated whose aims are to, respectively, maximize the disturbance tolerance level, minimize the disturbance attenuation level and maximize the initial condition set. Finally, simulation examples demonstrate the effectiveness and benefits of the obtained results.     

Distributed observer-based consensus tracking for nonlinear MASs with nonuniform input delays and disturbances

In this paper, we investigate the consensus tracking problem of nonlinear MASs with nonuniform time-varying input delays and external disturbances. For each follower, the composited disturbance observer and the state observer are employed to estimate bounded composited disturbances and unmeasured states, and a distributed observer based on output-feedback is proposed to approximate the leader’s states approachably. Sequentially, the consensus tracking control is converted into a stability control problem for the nonlinear MASs with nonuniform time-varying input delays. Subsequently, a distributed controller based on the truncated prediction approach is presented, which only depends on the boundary value of time-varying input delays. The distributed controller can render each follower synchronically stable via the Lyapunov stability theory. Finally, a group of single-link manipulators is used as an example to verify the effectiveness of the theoretical results.     

Constrained model predictive fault-tolerant control for multi-time-delayed batch processes with disturbances: A Lyapunov-Razumikhin function method

This paper mainly studies the design of iterative learning constrained model predictive fault–tolerant control for batch processes accompanied by multi–delays, interference and actuator failures. Firstly, an equivalent 2D–Roesser model with multi–delays is established. The definition of invariant set is proposed. The sufficient conditions with invariant set characteristics are established. After that the predictive fault-tolerant controller is designed with terminal constraints against external disturbances. In this paper, Lyapunov–Razumikhin Function (LRF) is used to form Lyapunov–Krasovskii Function (LKF) to construct the sufficient condition for the predictive control system that satisfies the terminal constraint condition. Moreover, the system state still remains invariant set characteristics. This method has certain advantages in controller design and calculation. In addition, it has the characteristics of simple design and small computation, and is especially suitable for small delay systems. Finally, a simulation experiment in the nonlinear batch reactor is carried out. Compared with the traditional one-dimensional (1D) method, the presented strategy has better performance through simulation experiment.     

Robust mixed H2/H∞ model predictive control for Markov jump systems with partially uncertain transition probabilities

This paper addresses the control problem for a class of discrete-time Markov jump linear systems with partially unknown transition probabilities using model predictive controller subject to external disturbances and input constraints. Our focus is on the design of a model predictive controller to stabilize the system with a given mixed H₂/H_∞ performance index. Sufficient conditions are derived in terms of a set of linear matrix inequalities. Examples are presented to demonstrate the effectiveness of the proposed controller design method.     

Resilient control based on disturbance observer for nonlinear singular stochastic hybrid system with partly unknown Markovian jump parameters

In this paper, the composite anti-disturbance resilient control is considered for nonlinear singular stochastic hybrid system with partly unknown Markovian jump parameters under multiple disturbances. Three kinds of disturbances are included in the studied system. One is generated by an external system and it enters the hybrid system from the channel of the control input. The other one is stochastic white noise. And the third one is the external unknown time-varying disturbance and it is supposed to be H₂ norm bounded. By combining the disturbance-observer-based-control scheme, H_∞ control technique and resilient control method, a composite anti-disturbance resilient controller is constructed to attenuate and eliminate the affection of these disturbances, and ensures the whole closed-loop system regular, impulse free and stochastically stable with the corresponding control performance. Then, some sufficient conditions and the gains of the controller and observer are obtained by using Lyapunov function method and the linear matrix inequalities (LMIs) technique. Finally, two numerical examples are given to show the effectiveness of presented method.     

Observer-based terminal sliding mode control of non-affine nonlinear systems: Finite-time approach

This paper studies the problem of observer based fast nonsingular terminal sliding mode control schemes for nonlinear non-affine systems with actuator faults, unknown states, and external disturbances. A hyperbolic tangent function based extended state observer is considered to estimate unknown states, which enhances robustness by estimating external disturbance. Then, Taylor series expansion is employed for the non-affine nonlinear system with actuator faults, which transforms it to an affine form system to simplify disturbance observer and controller design. A finite time disturbance observer is designed to address unknown compound disturbances, which includes external disturbances and system uncertainties. A fast nonsingular terminal sliding mode with exponential function sliding mode is proposed to address output tracking. Simulation results show the proposed scheme is effective.     

Optimization-based adaptive neural sliding mode control for nonlinear systems with fast and accurate response under state and input constraints

The high-performance control requires the system to be stable, fast and accurate simultaneously. However, various systems (e.g., motors, industrial robots) generally face technical challenges such as nonlinearities, uncertainties, external disturbances and physical constraints, which make it difficult to reach the hardware potential of the systems to track the desired trajectories when satisfying the high-performance control requirements. Therefore, take a two-order nonlinear system for example, an optimization-based adaptive neural sliding mode control based on a two-loop control structure is proposed in this paper, where the outer and inner loops are designed separately to achieve different control requirements. Namely, the outer loop is designed as a model predictive control (MPC)-based optimization problem, which can optimize the desired trajectories to meet the state and input constraints, and maximize the converging speed of transient response as fast as possible, and the inner loop is designed with a recurrent neural network (RNN)-based adaptive neural sliding mode controller, which can guarantee the tracking of the replanned desired trajectories from outer loop as accurate as possible. The stability of the system is guaranteed by Lyapunov theorem, the optimal tracking performance is achieved under nonlinearities, uncertainties, external disturbances and physical constraints, and comparative simulation with a motor system is carried out to verify the effectiveness and superiority of the proposed approach.     

Trajectory tracking control for unmanned surface vessel with input saturation and disturbances via robust state error IDA-PBC approach

A novel robust state error Interconnection and Damping assignment Passivity-based Control (IDA-PBC) controller for Unmanned Surface Vessels (USVs) with input saturation and disturbances is proposed. A reduced-order extended state observer, the state error IDA-PBC technique, and an auxiliary dynamic system are used in the controller design. Firstly, a reduced-order extended state observer is constructed to estimate the external disturbances. Then, the state error IDA-PBC approach reduces system energy consumption and is easy to implement. We construct an auxiliary dynamic system to handle input saturation. All signals of the whole system can guarantee uniformly ultimate boundedness. Simulations demonstrate the robustness and effectiveness of the proposed approach.     

Finite time position and heading tracking control of coaxial octorotor based on extended inverse multi-quadratic radial basis function network and external disturbance observer

In this work, finite time position and heading control based on backstepping based fast terminal sliding mode control is proposed for coaxial octorotor subjected to external wind disturbances. First, mathematical model of the coaxial octorotor is developed and then a new learning-based technique, an extended inverse multi-quadratic radial basis function network (EIMRBFN) is proposed to estimate the unmodeled dynamics of the octorotor. The external disturbance observer is also designed to encompass the realistic disturbance effect in the dynamical model and to allow the controller handle external disturbances, effectively. Backstepping controller based on fast terminal sliding model control is then proposed and also applied on the resultant dynamical model that provides finite time convergence of system's states. The stability of the proposed controller and complete system is analyzed using Lyapunov stability theory. Finite time convergence analysis of the desired trajectory is also provided. Simulations are carried out to validate the effectiveness of the proposed control scheme. Comparison with traditional PID and LQR controllers also verifies that the proposed controller achieves improved performance.