Adaptive sliding mode fault-tolerant control for type-2 fuzzy systems with distributed delays

GestureMoRo: an algorithm for autonomous mobile robot teleoperation based on gesture recognition

Gestures are a common way people communicate. Gesture-based teleoperation control systems tend to be simple to operate and suitable for most people's daily use. This paper employed a LeapMotion sensor to develop a mobile robot control system based on gesture recognition, which mainly established connections through a client/server structure. The principles of gesture recognition in the system were studied and the relevant self-investigated algorithms-GestureMoRo, for the association between gestures and mobile robots were designed. Moreover, in order to avoid the unstably fluctuated movement of the mobile robot caused by palm shaking, the Gaussian filter algorithm was used to smooth and denoise the collected gesture data, which effectively improved the robustness and stability of the mobile robot's locomotion. Finally, the teleoperation control strategy of the gesture to the WATER2 mobile robot was realized, and the effectiveness and practicability of the designed system were verified through multiple experiments.

Event-triggered fault-tolerant control for input-constrained nonlinear systems with mismatched disturbances via adaptive dynamic programming

In this paper, the issue of event-triggered optimal fault-tolerant control is investigated for input-constrained nonlinear systems with mismatched disturbances. To eliminate the effect of abrupt faults and ensure the optimal performance of general nonlinear dynamics, an adaptive dynamic programming (ADP) algorithm is employed to develop a sliding mode fault-tolerant control strategy. When the system trajectories converge to the sliding-mode surface, the equivalent sliding mode dynamics is transformed into a reformulated auxiliary system with a modified cost function. Then, a single critic neural network (NN) is adopted to solve the modified Hamilton-Jacobi-Bellman (HJB) equation. In order to overcome the difficulty that arises from the persistence of excitation (PE) condition, the experience replay technique is utilized to update the critic weights. In this study, a novel control method is proposed, which can effectively eliminate the effects of abrupt faults while achieving optimal control with the minimum cost under a single network architecture. Furthermore, the closed-loop nonlinear system is proved to be uniformly ultimate boundedness based on Lyapunov stability theory. Finally, three examples are presented to verify the validity of the control strategy.

Fault Detection for Semi-Markov Switching Systems in the Presence of Positivity Constraints

The fault detection issue is investigated for complex stochastic delayed systems in the presence of positivity constraints and semi-Markov switching parameters. By choosing a mode-dependent fault detection filter (FDF) as a residual generator, the corresponding fault detection is formulated as a positive [Formula: see text] filter problem. Attention is focused on the design of a mode-dependent FDF to minimize the error between the residual signal and the fault signal. The designed FDF features good sensitivity of the faults and robustness against the external disturbances. Subsequently, by means of the linear copositive Lyapunov functional (LCLF), stochastic stability is proposed to satisfy an expected [Formula: see text]-gain performance. Some solvability conditions for the desired mode-dependent FDF are established with the help of a linear programming approach. Finally, an application example of a data communication network model is provided to demonstrate the effectiveness of the theoretical findings.

Dissipativity-Based Fault-Tolerant Control for Stochastic Switched Systems With Time-Varying Delay and Uncertainties

This article investigates the fault-tolerant control problem for stochastic switched interval type-2 (IT2) fuzzy time-delayed uncertain systems based on unknown input observer synthesis, which can avoid uneasy measurement on the time derivative of output, and estimate unavailable or partially measurable states, including sensor and actuator faults accurately. First, a desired fuzzy observer is designed to ensure the observer-based dynamic error system mean-square exponentially stable with sufficient condition of a strict (l,ħ,℘) - [Formula: see text]-dissipative performance, which is a unified framework of passivity, and H_∞ provides results with less conservativeness. Then, we concentrate on stability analyses on dissipativity-based switched IT2 fuzzy systems with stochastic perturbation through linear matrix inequalities, Lyapunov function, free-weighting matrices, and average dwell time, discussing it according to different values of disturbance. Finally, simulation examples are listed to account for availability and effectiveness of the research methodology.

Observer-based output feedback control design for a fractional ODE and a fractional PDE cascaded system

This paper considers a new bi-directional cascaded system of a fractional ordinary differential equation (FODE) and a fractional partial differential equation (FPDE) which interacts at an intermediate point. The space-dependent coefficients, interaction between the FODE and FPDE at an intermediate point and the presence of fractional calculus makes the FODE-FPDE cascaded system, representative. In this note, we first apply an invertible integral transformation to convert the system into a FODE-FPDE coupled system, as the target system, which is Mittag-Leffler stable. Using the backstepping method and under some assumptions of the space-dependent coefficients, we work out the kernel functions in the transformation and we design a boundary controller. Also, by the invertibility of the transformation, we show the Mittag-Leffler stability of the closed-loop system via the Lyapunov approach. Second, we propose an observer for which we prove that it can well estimate the original cascaded system. Then, we design an output feedback boundary control law and show that the closed-loop system is Mittag-Leffler stable under the designed output feedback control law. Finally, we present some numerical illustrations to show the correctness of the theoretical results.

A New Type-3 Fuzzy Logic Approach for Chaotic Systems: Robust Learning Algorithm

The chaotic systems have extensive applications in various branches of engineering problems such as financial problems, image processing, secure communications, and medical problems, among many others. In most applications, a synchronization needs to be made with another favorite chaotic system, or output trajectories track the desired signal. The dynamics of these systems are complicated, they are very sensitive to the initial conditions, and they exhibit a stochastic unpredictable behavior. In this study, a new robust type-3 fuzzy logic control (T3-FLC) is designed that can be applied for a large case of chaotic systems under faulty actuators and unknown perturbed dynamics. The dynamic uncertainties are estimated by the online learned type-3 fuzzy logic systems (T3-FLSs). The rules of T3-FLS are optimized by the Lyapunov theorem. The actuator nonlinearities are identified by a new method. The effects of approximation error (AE), dynamic perturbations and unknown time-varying control gains are tackled by the designed adaptive compensator. The designed compensator is constructed by online estimation of the upper bound of AE. By several simulations and comparison with the new FLS-based controllers, the better performance of the designed T3-FLC is shown. In addition, the performance of the designed controller is examined in a secure communication system.

Design of Linear Matrix Inequality-Based Adaptive Barrier Global Sliding Mode Fault Tolerant Control for Uncertain Systems with Faulty Actuators

This paper proposes a linear matrix inequality (LMI)-based adaptive barrier global sliding mode control (ABGSMC) for uncertain systems with faulty actuators. The proposed approach is derived using a novel global nonlinear sliding surface to guarantee the global dynamic property and to ensure system stability and the occurrence of sliding in the presence of actuator faults. The optimal coefficients of the sliding surface are determined using the LMI method. The system’s asymptotic stability is proven using Lyapunov theory. Additionally, an adaptive barrier function is considered to ensure the convergence of the output variables to a predefined locality of zero in a limited time, even where external disturbances and actuator faults are present. In order to decrease the steepness of the control action and mitigate the chattering phenomenon, the hyperbolic tangent function is employed instead of the signum function in the sliding mode control. The proposed method is validated using a simulation study of the Genesio’s chaotic system.

Non-fragile sliding mode control for one-sided Lipschitz chaotic systems

This paper deals with the sliding mode stabilization for chaotic systems. In the system under consideration, the nonlinear function is one-sided Lipschitz with quadratic inner-boundedness. Specifically, a non-fragile sliding mode surface is constructed, and the sufficient condition for the convergence is derived. Then, a new feedback law is proposed to enable the state trajectories of the closed-loop system to reach the sliding mode surface in finite time. Finally, an example in the background of the unified chaos system is simulated to show the validation of the designed controller.

Design and implementation of finite time sliding mode controller for fuzzy overhead crane system

This paper considers the problem of fuzzy overhead crane system modelling and finite-time stability/boundedness via sliding mode control (SMC) method. Due to the strong coupling of control input, the fuzzy technique is utilized to linearize the overhead crane system and a fuzzy overhead crane model is established with appropriate membership functions. Considering the bad effect, including the swing of hook and plates, the external disturbances of the friction and air resistances, is inevitable during the transportation of copper electrode plates, the SMC method is adopted to stabilize the fuzzy system and robust to these interference signals. Furthermore, taking the time cost of actual industry into account, the finite-time stability/boundedness is introduced to achieve the state of system could be stable in a specified finite time. Moreover, the reaching law of sliding mode dynamics is analysed and the sufficient conditions for finite-time stability/boundedness of system state are formulated, respectively. Finally, the simulation results of the control strategy put forward in this article with the comparisons on some existing algorithms are provided to verify the effectiveness of the control strategy in the copper electrolytic overhead crane system.

Stabilization of Interval Type-2 Fuzzy-Based Reliable Sampled-Data Control Systems

This article investigates the stabilization problem of the Takagi-Sugeno (T-S) fuzzy-based interval type-2 (IT-2) reliable sampled-data control system. Different from the existing studies, the information for the entire sampling interval with a relation of the augmented state vectors is considered for designing the reliable sampled-data control of IT-2 T-S fuzzy systems. In this regard, we construct the suitable looped Lyapunov-Krasovskii functional (LKF) with the information of the fuzzy membership function that feeds back the time derivative of the membership function to derive sufficient conditions. These sufficient conditions are established in the form of linear matrix inequalities (LMIs), which ensure the global asymptotic stability of T-S fuzzy systems under the designed control technique. Finally, the applicability and superiority of developed reliable sampled-data control techniques were validated by three practical systems.

Adaptive Control of Nonlinear MIMO System With Orthogonal Endocrine Intelligent Controller

In this article, a new intelligent hybrid controller is proposed. The controller is based on the combination of the orthogonal endocrine neural network (OENN) and orthogonal endocrine ANFIS (OEANFIS). The orthogonal part of the controller consists of Chebyshev orthogonal functions, which are used because of their recursive property, computational simplicity, and accuracy in nonlinear approximations. Artificial endocrine influence on the controller is achieved by introducing excitatory and inhibitory glands to the OENN part of the structure, in the form of postsynaptic potentials. These potentials provide a network with the capability of additional self-regulation in the presence of external disturbances. The intelligent structure is trained using a developed learning algorithm, which consists of both offline and online learning procedures: online learning for fitting OENN substructure and offline learning for adjusting OEANFIS parameters. The learning process is expanded by introducing the learning rate adaptation algorithm, which bases its calculations on the sign of the error difference. Finally, the proposed intelligent controller was experimentally tested for control of a nonlinear multiple-input-multiple-output two rotor aerodynamical system. During the test phase, an additional four related intelligent control logics and default PID-based controllers were used, and tracking performance comparisons were performed. The proposed controller showed notably better online results in comparison to other control algorithms. The major deficiencies of the structure are complexity and noticeably large training computation time, but these drawbacks can be neglected if tracking performances of a dynamical system are of the highest importance.

Periodic event-triggered targeted shape control of Lagrangian systems with discrete-time delays

We study the problem of periodic event-triggered targeted formation control for multi-agent Lagrangian systems. We assume that the equations of motion of each agent are derived from a Lagrangian function. Besides, each agent has information about a predefined convex set as a targeted set. The goal of the agents is to achieve the desired formation in the targeted sets, while the information is asynchronously shared by a periodic event-triggering specification, and there is also a time-delay in their communication. A novel distributed control law is proposed for the agents to reach the goal while their velocities are driven to zero. Applications and simulations are provided to validate the theoretical results.

Adaptive Global Sliding Mode Controller Design for Perturbed DC-DC Buck Converters

This paper proposes a novel adaptive intelligent global sliding mode control for the tracking control of a DC-DC buck converter with time-varying uncertainties/disturbances. The proposed control law is formulated using a switching surface that eliminates the reaching phase and ensures the existence of the sliding action from the start. The control law is derived based on the Lyapunov stability theory. The effectiveness of the proposed approach is illustrated via high-fidelity simulations by means of Simscape simulation environment in MATLAB. Satisfactory tracking accuracy, efficient suppression of the chattering phenomenon in the control input, and high robustness against uncertainties/disturbances are among the attributes of the proposed control approach.

Adaptive Neural Network Sliding Mode Control for Nonlinear Singular Fractional Order Systems with Mismatched Uncertainties

This paper focuses on the sliding mode control (SMC) problem for a class of uncertain singular fractional order systems (SFOSs). The uncertainties occur in both state and derivative matrices. A radial basis function (RBF) neural network strategy was utilized to estimate the nonlinear terms of SFOSs. Firstly, by expanding the dimension of the SFOS, a novel sliding surface was constructed. A necessary and sufficient condition was given to ensure the admissibility of the SFOS while the system state moves on the sliding surface. The obtained results are linear matrix inequalities (LMIs), which are more general than the existing research. Then, the adaptive control law based on the RBF neural network was organized to guarantee that the SFOS reaches the sliding surface in a finite time. Finally, a simulation example is proposed to verify the validity of the designed procedures.

The stability and accuracy analysis of automatic steering system with time delay

The paper presents a systematic method of stability analysis for automatic steering system involving a time delay and some external disturbances. The analysis focuses on the stability of automatic steering system with increase of the time delay from zero to infinity. Firstly, the structure and dynamic model of automatic steering system are briefly introduced, and PD control algorithm is adopted to further analysis the time delay mechanism of control system of automatic steering system. Then the time delay dynamic models of automatic steering system with PD control algorithm are derived, the time delay stability interval and critical time delay of automatic steering system are calculated by using the generalized Sturm criterion method. Finally, the accuracy of critical time delay calculation by proposed method, and the stability and accuracy of automatic steering system with time delay and external disturbance are illustrated by numerical simulations.

H ∞ Time-Delayed Fractional Order Adaptive Sliding Mode Control for Two-Wheel Self-Balancing Vehicles

In this paper, a time-delayed fractional order adaptive sliding mode control algorithm is proposed for a two-wheel self-balancing vehicle system. The closed-loop system is proved based on the Lyapunov-Razumikhin function. The switching function is designed to make the system robust when facing uncertainties and external disturbances. It is designed to avoid monotonically increasing gains and can handle state-dependent uncertainties without a prior bound. The two-wheel self-balancing vehicle used in the experiment consists of a gyroscope MPU-6050 and accelerometer, a motor driving circuit composed of a motor driving chip TB6612FNG, and STM32F103x8B that is selected as the control core. The experimental results show that the time-delayed fractional order adaptive sliding mode control algorithm can make the vehicle achieve autonomous balance and quickly restore its stable state while appropriate disturbance is introduced.