Learning‐based robust control methodologies under information constraints

Special issue on ‘new results on neuro‐fuzzy adaptive control systems’

Analysis and design of control systems via parameter‐based approach

International audience

This paper presents a control strategy for a 4Y octocopter aircraft that is influenced by multiple actuator faults and external disturbances. The approach relies on a disturbance observer, adaptive type-2 fuzzy sliding mode control scheme, and type-1 fuzzy inference system. The proposed control approach is distinct from other tactics for controlling unmanned aerial vehicles because it can simultaneously compensate for actuator faults and external disturbances. The suggested control technique incorporates adaptive control parameters in both continuous and discontinuous control components. This enables the production of appropriate control signals to manage actuator faults and parametric uncertainties without relying only on the robust discontinuous control approach of sliding mode control. Additionally, a type-1 fuzzy logic system is used to build a fuzzy hitting control law to eliminate the occurrence of chattering phenomena on the integral sliding mode control. In addition, in order to keep the discontinuous control gain in sliding mode control at a small value, a nonlinear disturbance observer is constructed and integrated to mitigate the influence of external disturbances. Moreover, stability analysis of the proposed control method using Lyapunov theory showcases its potential to uphold system tracking performance and minimize tracking errors under specified conditions. The simulation results demonstrate that the proposed control strategy can significantly reduce the chattering effect and provide accurate trajectory tracking in the presence of actuator faults. Furthermore, the efficacy of the recommended control strategy is shown by comparative simulation results of 4Y octocopter under different failing and uncertain settings.

Learning systems represent a particularly important class of practical data-driven systems that adapt to their environment based on the environment's response to the system's action. In some settings traditional robust and adaptive control techniques have been verified to be effective when plants are large scale and/or have very complex dynamics (e.g., industrial processes, power grid networks, and transportation systems). However, often in these plants the problems of parameter mismatch and unmodeled dynamics are encountered, and thus the techniques of robust or adaptive control methods in the framework of modern control may no longer be sufficient. As an alternative to robust or adaptive methods, various learning paradigms have been established, for example, reinforcement learning, deep learning, artificial neural networks, and iterative learning, among others. These learning paradigms construct controllers or control signals directly with the data collected and stored when the system operates, typically without the need of identifying system models. However, even such “model-independent” control systems must make assumptions about the system dynamics. One such common assumption is that the system dynamics are linear. Another is that they are time-invariant (autonomous). When the dynamics do not meet these assumptions, traditional learning paradigms may also fail. Despite the success of learning-based methods, finding suitable control frameworks for learning systems when there is uncertainty in the assumptions related to the system dynamics is still an open problem. The aim of this special issue is to collect recent research results that address issues involved in data-driven control methods and related algorithms for learning-based systems that operate under uncertainty. The special issue consists of fourteen papers covering several key areas of activity in which progress has been achieved. Many of the papers also include results of simulation or experimental tests of the presented control methods. Next we give a brief description of each of the fourteen papers. We have organized the papers thematically into four categories: (1) iterative learning control (ILC), (2) neural networks and machine learning, (3) identification and control, and (4) applications. The first five papers describe research on the control method of ILC, which is a process developed especially for applications where the same operation needs to be repeated over a limited period of time. In the first of these papers, Ding and Li give results on an adaptive ILC algorithm for nonlinear continuous nonparameterized systems. This adaptive ILC algorithm, which can adjust the adaptive parameters in both the iteration domain and the time domain, is proposed to track different reference trajectories repetitively over a finite time interval, and also to provide a unified adaptive control strategy for nonlinear continuous nonparameterized systems that have asymmetric control gain matrices for trajectory tracking in different domains. In the next paper, Chen and Chu address the fact that in multiagent collaborative tracking tasks, the system often encounters constraints in practice that cause challenging difficulties in the case of handling large-scale systems. They propose a novel constrained ILC design and further develop a decentralized implementation of the resulting ILC algorithm using the alternating direction method of multipliers, allowing the design to scale up to handle large-scale and varying system dynamics. The next three papers consider the special problem of ILC when there are quantization and intersample behavior effects. In the third paper, Ohnishi et al. develop a framework for a state-tracking ILC that mitigates the oscillatory intersample behavior that is often encountered in output-tracking ILC. The paper addresses stability in the iteration domain, introducing the idea of a robustness filter, designed in the frequency domain. The last two ILC papers both consider quantization effects. Zhang et al. simultaneously tackle the problems of predictive compensation, unknown nonlinearity, and nonaffine structure found in a quantized ILC analysis and design under a data-driven framework. Using the concept of a virtual iterative linear data model, the authors develop control laws that are completely data driven and model free. The established results are also generalized to a class of multi-input multi-output, nonlinear, nonaffine, discrete-time systems. Finally, Huo et al. investigate a quantized ILC method with an encoding–decoding mechanism for networked control systems subject to constrained transmission bandwidths and random data dropouts on both the measurement and the actuator side. Their approach is to utilize an intermittent update principle to develop a control algorithm that can demonstrate enhanced tracking performance. The next three papers use neural network and machine learning (ML) approaches to deal with uncertain systems. First, with the aid of neural networks, Xin et al. develop a neural network-based learning design for a class of high-order nonlinear systems involving full-state constraints in the form of output feedback control. Then, Chen et al. consider non-repetitive time-varying systems. They propose an ML-based nominal model updating mechanism that uses linear regression techniques to update the nominal model in each ILC trial, with only the current trial information, which improves the performance of the ILC. This paper provides information on how ML/ILC parameters can be tuned to achieve desired performance. The ideas are illustrated through experiments on a real gantry robot test platform. Another ML-based problem considers robust approximate optimal control of an air-breathing hypersonic vehicle in the paper by Han et al. The learning-based control framework can guarantee practical finite-time convergence of the tracking error, without chattering. Further, the proposed controller adopts a critic-only network design instead of an actor-critic structure, which simplifies the implementation procedure. The next three papers consider the issue of data-driven identification and control. With the aid of subspace methods and an average consensus algorithm, Cheng et al. develop a data-driven design for distributed fault detection of dynamic systems using measurements in a complex sensor network. Due to advantages such as high reliability and low communication loads, each sensor node has the ability to execute a fault detection scheme using the average consensus algorithm. The correctness and effectiveness of the proposed scheme are demonstrated in a three-phase flow facility. Next, Sun et al. describe a technique whereby a dynamic parameterization is employed to produce linear, time-varying, parameterized models that equivalently describe a general class of nonlinear systems. The parameters in these models can be identified using an iterative learning least squares technique. The third and final paper in this group, by Lan et al., presents a data-driven policy learning strategy. The authors show how to design controllers for automated vehicles based on vehicle-to-vehicle communications. The learned control policy can be implemented using a prescribed vehicle-to-vehicle communication topology, which can establish a safe, robust, and stable mixed platoon. The final three papers in the special issue consider applications of learning systems. Yang et al. propose neural learning-based adaptive impedance control for a lower limb rehabilitation exoskeleton with flexible joints. In order to improve the control performance and enhance the system robustness, periodic dynamics are considered according to the repetitive motion of the rehabilitation process and are handled by a repetitive learning algorithm. The method studied is used for a full model consisting of both the rigid link and the flexible joints. Next, the authors He and Shen investigate the distributed transient control problem for heating ventilation and air condition systems such that each unit of the large-scale building can consistently maintain the desired temperature. For this problem, a distributed ILC algorithm with a decreasing gain is proposed and analyzed. To accelerate the convergence speed, an adaptation mechanism is introduced to generate an adaptive learning gain sequence. The final paper in the special issue, by Li et al., investigates the robust lateral tracking control problem for autonomous vehicles subject to unmodeled system uncertainties, external disturbances, and input constraints that commonly exist in vehicle dynamics. A robust adaptive learning control approach is proposed based on a parametric vehicle model, where an input-dependent auxiliary system is leveraged to compensate for the influence of the input constraints. This approach is proven to be able to achieve impressive path tracking performances under perturbed and constrained scenarios. Furthermore, the proposed robust adaptive learning technique for tackling the under-actuated dynamics is novel and can also be applied to deal with other generic nonsquare systems.

Adaptive neural network control of uncertain robotic manipulators with external disturbance and time-varying output constraints

In this article, an adaptive fault tolerant control strategy is proposed to solve the trajectory tracking problem of a generic hypersonic vehicle subjected to actuator fault, external disturbance, and input saturation. The longitudinal model of generic hypersonic vehicle is divided into velocity subsystem and altitude subsystem, in which dynamic inversion and backstepping are applied, respectively, to track the desired trajectories. For the unknown maximum disturbance upper bound, actuator fault, and input saturation constraint, adaptive laws are proposed to estimate these information online. Finally, numeric simulation is conducted in the cruise phase for generic hypersonic vehicle. Simulation results show that the controllers designed in this article can make generic hypersonic vehicle track the desired trajectories in the presence of actuator fault, external disturbance, and input saturation.

In this paper, the problems of tracking control and finite-time stabilization of a high nonlinear system such as a robotic manipulator in the presence of actuator faults, uncertainties, and external disturbances are explored. In order to improve the performance of the system in the presence of actuator faults, uncertainties and external disturbances a novel fault tolerant control system based on fractional-order backstepping fast terminal sliding mode control is developed in this paper. The control system is developed by employing the results obtained from studies in the fields of fractional-order calculus, backstepping, sliding mode control, Mittag–Leffler stability, and finite-time Lyapunov stability. The performance of the suggested controller is then tested for a PUMA560 robot in which the first three joints are used. The simulation results validate the usefulness of the developed control approach in terms of accuracy of tracking, and convergence speed in the presence of disturbances, uncertainties and actuator faults. The trajectory tracking performance of the developed method is compared with other state of the art approaches such as conventional computed torque control, proportional integral derivative control and nonsingular fast terminal sliding mode control. The simulation results show that the proposed control approach performed better as compared to other control approaches in the presence of actuator faults, uncertainties, and disturbances.

Robust tracking observer-based adaptive fuzzy control design for uncertain nonlinear MIMO systems with time delayed states

Actuator faults and external disturbances, which are inevitable due to material fatigue, operational wear and tear, and unforeseen environmental impacts, cause significant threats to the control reliability and performance of networked systems. Therefore, this paper primarily focuses on the distributed adaptive bipartite consensus tracking control problem of networked Euler–Lagrange systems (ELSs) subject to actuator faults and external disturbances. A robust distributed control scheme is developed by combining the adaptive distributed observer and neural-network-based tracking controller. On the one hand, a new positive definite diagonal matrix associated with an asymmetric Laplacian matrix is constructed in the distributed observer, which can be used to estimate the leader’s information. On the other hand, neural networks are adopted to approximate the lumped uncertainties composed of unknown matrices and external disturbances in the follower model. The adaptive update laws are designed for the unknown parameters in neural networks and the actuator fault factors to ensure the boundedness of estimation errors. Finally, the proposed control scheme’s effectiveness is validated through numerical simulations using two types of typical ELS models: two-link robot manipulators and quadrotor drones. The simulation results demonstrate the robustness and reliability of the proposed control approach in the presence of actuator faults and external disturbances.

This paper is concerned with the fractional-order fault-tolerant tracking control design for unmanned aerial vehicle (UAV) in the presence of external disturbance and actuator fault. Based on the functional decomposition, the dynamics of UAV is divided into velocity subsystem and altitude subsystem. Altitude, flight path angle, pitch angle and pitch rate are involved in the altitude subsystem. By using an adaptive mechanism, the fractional derivative of uncertainty including external disturbance and actuator fault is estimated. Moreover, in order to eliminate the problem of explosion of complexity in back-stepping approach, the high-gain observer is utilized to estimate the derivatives of virtual control signal. Furthermore, by using a fractional-order sliding surface involved with pitch dynamics, an adaptive fractional-order fault-tolerant control scheme is proposed for UAV. It is proved that all signals of the closed-loop system are bounded and the tracking error can converge to a small region containing zero via the Lyapunov analysis. Simulation results show that the proposed controller could achieve good tracking performance in the presence of actuator fault and external disturbance.

Adaptive finite-time sliding mode control design for finite-time fault-tolerant trajectory tracking of marine vehicles with input saturation

Introduction. Budget constraints in a world ravenous for electrical power have led utility companies to operate generating stations with full power and sometimes at the limit of stability. In such drastic conditions the occurrence of any contingency or disturbance may lead to a critical situation starting with poorly damped oscillations followed by loss of synchronism and power system instability. In the past decades, the utilization of supplementary excitation control signals for improving power system stability has received much attention. Power system stabilizers (PSS) are used to generate supplementary control signals for the excitation system in order to damp low-frequency oscillations caused by load disturbances or short-circuit faults. Problem. Adaptive power system stabilizers have been proposed to adequately deal with a wide range of operating conditions, but they suffer from the major drawback of requiring parameter model identification, state observation and on-line feedback gain computation. Power systems are nonlinear systems, with configurations and parameters that fluctuate with time that which require a fully nonlinear model and an adaptive control scheme for a practical operating environment. A new nonlinear adaptive fuzzy approach based on synergetic control theory which has been developed for nonlinear power system stabilizers to overcome above mentioned problems. Aim. Synergetic control theory has been successfully applied in the design of power system stabilizers is a most promising robust control technique relying on the same principle of invariance found in sliding mode control, but without its chattering drawback. In most of its applications, synergetic control law was designed based on an asymptotic stability analysis and the system trajectories evolve to a specified attractor reaching the equilibrium in an infinite time. In this paper an indirect finite time adaptive fuzzy synergetic power system stabilizer for damping local and inter-area modes of oscillations for power systems is presented. Methodology. The proposed controller design is based on an adaptive fuzzy control combining a synergetic control theory with a finite-time attractor and Lyapunov synthesis. Enhancing existing adaptive fuzzy synergetic power system stabilizer, where fuzzy systems are used to approximate unknown system dynamics and robust synergetic control for only providing asymptotic stability of the closed-loop system, the proposed technique procures finite time convergence property in the derivation of the continuous synergetic control law. Analytical proofs for finite time convergence are presented confirming that the proposed adaptive scheme can guarantee that system signals are bounded and finite time stability obtained. Results. The performance of the proposed stabilizer is evaluated for a single machine infinite bus system and for a multi machine power system under different type of disturbances. Simulation results are compared to those obtained with a conventional adaptive fuzzy synergetic controller.

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.

This paper studies the robust adaptive fuzzy control design problem for a class of uncertain multiple-input and multiple-output (MIMO) nonlinear systems in the presence of actuator amplitude and rate saturation. In the control scheme, fuzzy logic systems are used to approximate unknown nonlinear systems. To compensate the effect of input saturations, an auxiliary system is constructed and the actuator saturations then can be augmented into the controller. The modified tracking error is introduced and used in fuzzy parameter update laws. Furthermore, in order to deal with fuzzy approximation errors for unknown nonlinear systems and external disturbances, a robust compensation control is designed. It is proved that the closed-loop system obtainsH∞tracking performance through Lyapunov analysis. Steady and transient modified tracking errors are analyzed and the bound of modified tracking errors can be adjusted by tuning certain design parameters. The proposed control scheme is applicable to uncertain nonlinear systems not only with actuator amplitude saturation, but also with actuator amplitude and rate saturation. Detailed simulation results of a rigid body satellite attitude control system in the presence of parametric uncertainties, external disturbances, and control input constraints have been presented to illustrate the effectiveness of the proposed control scheme.