Adaptive neural networks finite-time tracking control for non-strict feedback systems via prescribed performance

Adaptive terminal super-twisting prescribed performance controller for near-space vehicle based on data-driven model

A data-driven adaptive terminal super-twisting prescribed performance controller (DASTPC) is designed for near-space vehicle (NSV) to satisfy transient and steady-state performance, and prevent scramjet choking. Firstly, a novel predetermined-time performance function is proposed to guarantee that tracking error can converge to a prescribed bound of small residual sets at the predetermined time. Compared with traditional performance functions, the predetermined-time performance function can achieve faster respond speed, realize more accurate convergence, and avoid overlarge initial value of actuators. Secondly, by combining the predetermined-time performance function with sliding mode control, a novel non-singular fast terminal sliding surface and an improved adaptive super-twisting reaching law are proposed to improve computational efficiency and accelerate convergent rate of system. The adaptive reaching law can avoid excessive gains and attenuate chattering by automatically tuning control gain. Thirdly, a deep recurrent neural network-based long-short term memory (LSTM) is employed to learn time-series historical flight dynamics data offline, so as to construct a data-driven LSTM training model. This data-driven model replaces nominal dynamics model of NSV in DASTPC, effectively suppressing model uncertainties. In addition, a homogeneous high-order sliding mode observer is utilized to compensate for external disturbances, avoiding excessive parameter estimation. Since boundary conditions of the predetermined-time performance function are fully satisfied, the DASTPC can effectively restrict amplitude of angle of attack, thus ensuring the intake condition of scramjet. Ultimately, to illustrate the superiority of DASTPC, several sets of simulations are performed on NSV subject to prescribed performance bound, external disturbances and parameter perturbations.

Prescribed Performance Bounded-H∞ Control for Flexible-Joint Manipulators Without Initial Condition Restriction

Flexible-joint manipulators have a lightweight nature, compact structure, and high flexibility, making them widely applicable in industrial manufacturing, biomedical instruments, and aerospace fields. However, the inherent flexibility of single-link flexible-joint manipulators (SLFJMs) poses substantial control challenges. Compared to traditional control algorithms, prescribed performance control (PPC) algorithms provide superior transient response and steady-state performance by defining a prescribed performance function. However, existing PPC algorithms are limited to a specific range of system initial states, which reduces the joint manipulator's operational workspace and weakens the robustness of the control algorithm. To address this issue, this study proposes a prescribed performance bounded-H∞ fault-tolerant controller for SLFJMs. By designing an improved tangent-type barrier Lyapunov function (BLF), a prescribed performance controller that is independent of the initial state of the SLFJM is developed. An input control function (ICF) is employed to mitigate the impulse response of the control input, ensuring a smooth transition from zero. Furthermore, the improved tangent-type BLF enables the tracking error to rapidly converge to a small neighborhood of zero. Finally, a stabilization control simulation experiment is conducted; the results validate the effectiveness of the proposed prescribed performance bounded-H∞ controller.

Finite-time prescribed performance tracking control for nonlinear time-delay systems with state constraints and actuator hysteresis

In this paper, the problem of adaptive neural network prescribed performance tracking control for a class of non-strict feedback time-delay systems constrained by full-state is studied. Radial basis function (RBF) neural networks (NNs) are integrated into the backstepping medium to deal with the uncertain functions and the barrier Lyapunov function (BLF) technique ensures that the state of the system does not exceed its limits. Subsequently, integrated with the Lyapunov-Krasovskii functional, the proposed control scheme makes the tracking errors converge to the preset region while the state constraint is not violated. Finally, the effectiveness of the scheme is supported by two simulation experiments.

Preassigned Time Adaptive Neural Tracking Control for Stochastic Nonlinear Multiagent Systems With Deferred Constraints

This article studies a preassigned time adaptive tracking control problem for stochastic multiagent systems (MASs) with deferred full state constraints and deferred prescribed performance. A modified nonlinear mapping is designed, which incorporates a class of shift functions, to eliminate the constraints on the initial value conditions. By virtue of this nonlinear mapping, the feasibility conditions of the full state constraints for stochastic MASs can also be circumvented. In addition, the Lyapunov function codesigned by the shift function and the fixed-time prescribed performance function is constructed. The unknown nonlinear terms of the converted systems are handled based on the approximation property of the neural networks. Furthermore, a preassigned time adaptive tracking controller is established, which can achieve deferred prescribed performance for stochastic MASs that provide only local information. Finally, a numerical example is given to demonstrate the effectiveness of the proposed scheme.

Optimization-based adaptive trajectory tracking controller design of self-balanced vehicle with asymptotic prescribed performance

To handle with the nonlinear external disturbances and unmodeled dynamics of self-balanced vehicle (SBV), a novel adaptive trajectory tracking controller based on asymptotic prescribed performance is proposed. First, a velocity planner based on kinematic is constructed to control the velocity signal to improve the motion stability of SBV. Second, the prescribed performance function (PPF) is designed to prescribe transient-state and steady-state performances (TSP). Afterwards, an optimization-based predictive control (OPC) is proposed for accurate trajectory tracking of SBV. Furthermore, a modified radial basis function neural network (RBFNN) approximator is developed to compensate the unmodeled dynamics and the nonlinear external disturbances of the SBV. The overall system stability is proved with the help of Lyapunov theorem. Finally, the tracking performance and anti-interference robustness of the proposed control method are verified by comparative numerical simulations.

Distributed adaptive sliding-mode control for 2-D plane vehicle platoon with prescribed performance and angle constraint

This paper focuses on the distributed adaptive sliding-mode control problem for two-dimensional (2-D) plane vehicle platoon with prescribed performance, angle constraints, and actuator faults. The quadratic spacing policy (QSP) is first adopted for the 2-D plane vehicle platoon to adjust the inter-vehicle spacing. The spacing error can converge within a finite time to the small region predetermined by a new finite-time performance function (FTPF). Meanwhile, a new transformed error function is introduced to convert the FTPF-constrained spacing errors into equivalent unconstrained ones. Besides, the property of the invertible nonlinear mapping function is used for the original system with the angle constraint to get a new unconstrained system. Moreover, a new controller based on hyperbolic tangent function is designed to handle actuator faults occurring multiple times over a period. Furthermore, the stability and string stability of the 2-D plane vehicle platoon are achieved through sliding-mode control. Finally, the simulation results validate the effectiveness of the proposed techniques.

Decentralized adaptive neural safe tracking control for nonlinear systems with conflicted output constraints

The issue of decentralized adaptive safe tracking control for interconnected large-scale nonlinear systems (ILSNSs) with conflicted output constraints is discussed in this paper. By "conflicted output constraints", we mean that the output constraint functions conflict with reference signal, i.e., the reference signal is not completely constrained within the constraint range. In existing methods, it is always assumed that the reference signal is constrained within the constraint region. In practice, the constraints may be detected during system operation and conflict with the reference signal given in advance. In this particular case, the existing methods based on barrier Lyapunov function (BLF) or nonlinear transformation function (NTF) are invalid. From a new point of view, this article designs a new safety reference signal (SRS) which is completely restricted within the constraint range by using the boundary protection approach. Meanwhile, a prescribed performance function which can arbitrarily define the convergence time and tracking accuracy is introduced so that the system output can better track the SRS. Then, combining backstepping technique and radial basis function neural network (RBFNN), a new controller is constructed, under which a desired tracking trajectory can be obtained under the premise of ensuring safety performance. Furthermore, by adding a dynamic event triggering mechanism (DETM) between the actuator and the plant, the communication burden is effectively reduced. Simulation results verify the scheme developed.

Linear-based gain-determining method for adaptive backstepping controller

This paper presents a linear-based gain-determining method for nonlinear adaptive backstepping controllers. Usually, the gains for nonlinear controllers are tuned by the trial and error method. This method becomes more difficult as the number of gains increases. A user-friendly method is proposed in this work to deal with the problem. Firstly, a linear auxiliary system is formed by separating the linear parts from the nonlinear system. Then, linear state-space techniques are used to determine the gains for state-feedback by the linear auxiliary system. After that, by converting the state-feedback gains to backstepping gains, the gains of the nonlinear backstepping controller can be determined. The proficiency of the gain-determining method is proved by simulations with two linear techniques.

Finite-Time-Prescribed Performance-Based Adaptive Fuzzy Control for Strict-Feedback Nonlinear Systems With Dynamic Uncertainty and Actuator Faults

In this article, finite-time-prescribed performance-based adaptive fuzzy control is considered for a class of strict-feedback systems in the presence of actuator faults and dynamic disturbances. To deal with the difficulties associated with the actuator faults and external disturbance, an adaptive fuzzy fault-tolerant control strategy is introduced. Different from the existing controller design methods, a modified performance function, which is called the finite-time performance function (FTPF), is presented. It is proved that the presented controller can ensure all the signals of the closed-loop system are bounded and the tracking error converges to a predetermined region in finite time. The effectiveness of the presented control scheme is verified through the simulation results.

A Novel Prescribed-Performance-Tracking Control System with Finite-Time Convergence Stability for Uncertain Robotic Manipulators

Through this article, we present an advanced prescribed performance-tracking control system with finite-time convergence stability for uncertain robotic manipulators. It is therefore necessary to define a suitable performance function and error transformation to guarantee a prescribed performance within a finite time. Following the definitions mentioned, a modified integral nonlinear sliding-mode hyperplane is constructed from the transformed errors. By using the designed nonlinear sliding-mode surface and the super-twisting reaching control law, an advanced approach to the prescribed performance control was formed for the trajectory tracking control of uncertain robotic manipulators. The proposed controller exhibits improved properties, including estimated convergence speed and a predefined upper and lower limit for maximum overshoot during transient responses. Furthermore, the maximum allowable size of the control errors at the steady-state can be predefined and these errors will inevitably converge to zero within a finite time, while the proposed controller can provide a smooth control torque without the loss of its robustness. It is shown that the proposed control system is globally stable and convergent over a finite time. A comprehensive analysis of the effectiveness of the proposed control algorithm was already conducted via the simulation of an industrial robot manipulator.

Adaptive Sliding Mode Attitude-Tracking Control of Spacecraft with Prescribed Time Performance

In this article, a novel finite-time attitude-tracking control scheme is proposed by using the prescribed performance control (PPC) method for the spacecraft system under the external disturbance and an uncertain inertia matrix. First, a novel polynomial finite-time performance function (FTPF) was used to avoid the complex calculation of exponential function from conventional FTPF. Then, a simpler error transformation was introduced to guarantee that the attitude-tracking error converges to a preselected region in prescribed time. Subsequently, a robust adaptive controller was proposed by using the backstepping method and the sliding mode control (SMC) technique. Unlike the existing attitude-tracking control results, the proposed PPC scheme guarantees the performance of spacecraft system under the static and transient conditions. Meanwhile, the state trajectory of system can be completely drawn into the designed sliding surface. The stability of the control scheme is proven rigorously by the Lyapunov’s theory of stability. Finally, the simulations show that the convergence rate and the convergence accuracy are better for the tracking errors of spacecraft system under the proposed control scheme.

Composite prescribed performance control of small unmanned aerial vehicles using modified nonlinear disturbance observer

An integrated control scheme composed of modified nonlinear disturbance observer and predefined-time prescribed performance control is proposed to address the high-accuracy tracking problem of the unmanned aerial vehicles (UAVs) subjected to external mismatched disturbances. By utilizing the transformation technique that incorporates the desired performance characteristic and the newly predefined-time performance function, the original controlled system can be transformed into a new unconstrained one to achieve the fixed-time convergence of the tracking error. Then, by virtual of the transformed unconstrained system, a modified nonlinear disturbance observer (NDO) which possesses fast convergence speed is established to estimate the external disturbance. With the application of the precise estimation value to compensate the normal control design in each back-stepping step, a novel composite control scheme is constructed. The light spot of the proposed scheme is that it not only has the superior capability to attenuate unknown mismatched disturbances, but also can guarantee that the output tracking errors converge to their prescribed regions within predefined time. Finally, simulation studies verify the effectiveness of the proposed control scheme.

High Performance of an Adaptive Sliding Mode Controller under Varying Loads for Lifting-Type Autonomous Grounded Robot

To work in shared space with humans, autonomous systems must carry unknown loads in predefined missions. With the conventional control scheme, the grounded robot would suffer unstable motion and imprecise tracking performance. To overcome these challenges, in this paper, a novel controller using an adaptive sliding mode for autonomous grounded robots (AGR) is proposed. This control strategy takes into consideration uncertain characteristics, varying loads, and external disturbances. To analyze the tracking performance precisely, the overall error of motion system is decoupled into two subsystems where the second-order system is related to the angular tracking error and the third-order system is associated with the linear one. Initially, the dynamics model of the grounded robot is established containing different elements of nonlinear forces in order to address the technical problems. Then, the system state equation of the autonomous system is mentioned to indicate the theoretical characteristics. Based on the proposed controller, the stability of the system is validated by the Lyapunov theorem. From the results of numerical tests, three practical situations consisting of separately linear and circular trajectories with varying loads and an S-curve trajectory of a working map are suggested. The tracking performance validates that the proposed control scheme is, in various scenarios, robust, effective, and feasible. From these superior outcomes, it can be determined obviously the property of our works in accommodating the variations of cargo from applications in distribution centers, material transportation, or handling equipment.

Prescribed performance adaptive finite-time control for uncertain horizontal platform systems

This paper presents a new approach to the design of prescribed performance adaptive control for uncertain horizontal platform systems with the finite-time convergence. Following an appropriate performance function and error transformation, a new adaptive control law is proposed by using a novel integral non-singular terminal sliding mode surface. The proposed approach simultaneously guarantees that (i) the transient responses of the closed-loop system possess some advanced properties such as the existence of the prespecified lower bound of the convergence rate and of the pre-established upper bound of the maximum overshoot; and (ii) the finite-time convergence of the state trajectories/tracking errors to zero. The global stability and finite-time convergence are strictly analyzed. The proposed method is clarified and verified through two numerical simulation examples.

Direct Adaptive Preassigned Finite-Time Control With Time-Delay and Quantized Input Using Neural Network

This paper investigates an adaptive finite-time control (FTC) problem for a class of strict-feedback nonlinear systems with both time-delays and quantized input from a new point of view. First, a new concept, called preassigned finite-time performance function (PFTF), is defined. Then, another novel notion, called practically preassigned finite-time stability (PPFTS), is introduced. With PFTF and PPFTS in hand, a novel sufficient condition of the FTC is given by using the neural network (NN) control and direct adaptive backstepping technique, which is different from the existing results. In addition, a modified barrier function is first introduced in this work. Moreover, this work is first to focus on the FTC for the situation that the time-delay and quantized input simultaneously exist in the nonlinear systems. Finally, simulation results are carried out to illustrate the effectiveness of the proposed scheme.

A Task Scheduling Algorithm for Phased-Array Radar Based on Dynamic Three-Way Decision

The time resource management of phased-array radars is the key to fulfilling their performance, such as how phased-array radar can efficiently and reasonably schedule tasks under limited resources. Therefore, this paper proposes a task scheduling algorithm for phased-array radar based on dynamic three-way decision. The algorithm introduces three-way decision into the scheduling algorithm and divides the target into three threat areas according to the threat degree (i.e., threat area, nonthreat area, and potential threat area). Different threat domains are assigned different weights and combine the working mode and the task deadline to carry out comprehensive priority planning, so that the radar can reasonably allocate time according to the difference of the target threat level and the threat area in the tracking stage. In addition, an improved adaptive threshold algorithm is proposed to obtain a dynamic three-way decision to achieve the adaptation of the algorithm. A set of performance indicators have been defined to evaluate the algorithm. The relevant experiments have demonstrated that the proposed algorithm can effectively improve the processing capability of phased-array radars when dealing with high-threat targets.