Adaptive Tracking Control Scheme Research Articles

Observer-Based Adaptive Fuzzy Control of Nonstrict Feedback Nonlinear Systems With Function Constraints

In this paper, an adaptive fuzzy tracking control scheme based on fuzzy state observer is proposed for a class of uncertain non-strict feedback nonlinear systems with function constraints. In the first place, based on the approximation characteristic of fuzzy logic systems (FLSs), a fuzzy state observer is designed to estimate the immeasurable state variables in the controlled system. Next, under the framework of adaptive backstepping control technology, FLSs are selected not only to approximate unknown nonlinear functions, but also to avoid algebraic loop problem caused by non-strict feedback structure. At the same time, asymmetric Barrier Lyapunov functions (BLFs) are selected to solve the problem that system states are subject to function constraints, which are related to states and time. Then, Lyapunov stability theory is utilized to prove the stability of the controlled system, the realizability of function constraints and the convergence of output tracking errors. Finally, a simulation is given to check the effectiveness of the proposed control scheme.

Adaptive Neural Consensus Tracking Control for Nonlinear Multiagent Systems Using Integral Barrier Lyapunov Functionals.

This article presents the adaptive tracking control scheme of nonlinear multiagent systems under a directed graph and state constraints. In this article, the integral barrier Lyapunov functionals (iBLFs) are introduced to overcome the conservative limitation of the barrier Lyapunov function with error variables, relax the feasibility conditions, and simultaneously solve state constrained and coupling terms of the communication errors between agents. An adaptive distributed controller was designed based on iBLF and backstepping method, and iBLF was differentiated by means of the integral mean value theorem. At the same time, the properties of neural network are used to approximate the unknown terms, and the stability of the systems is proven by the Lyapunov stability theory. This scheme can not only ensure that the output of all the followers meets the output trajectory of the leader but also make the state variables not violate the constraint bounds, and all the closed-loop signals are bounded. Finally, the efficiency of the proposed controller is revealed.

Distributed Event-Triggered Control of Networked Strict-Feedback Systems via Intermittent State Feedback

It poses technical difficulty to achieve stable tracking even for single mismatched nonlinear strict-feedback systems when intermittent state feedback is utilized. The underlying problem becomes even more complicated if such systems are networked with directed communication and state-triggering setting. In this work, we present a fully distributed adaptive tracking control scheme for multiple agent systems in strict-feedback form using triggered state from the agent itself and the triggered states from the neighbor agents. To circumvent the non-differentiability of virtual controllers stemming from state-triggering, we first develop a distributed continuous control scheme under regular state feedback, upon which we construct the distributed event-triggered control scheme by replacing the states in the preceding scheme with the triggered ones. Several useful lemmas are introduced to allow the stability condition to be established with such replacement, ensuring that all the closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB), with the output tracking error converging to a residual set around zero. Besides, with proper choices of the design parameters, the tracking performance in the mean square sense can be improved. Numerical simulation verifies the benefits and efficiency of the proposed method.

Adaptive fault-tolerant trajectory tracking control of twin-propeller non-rudder unmanned surface vehicles

This paper studies the fault-tolerant trajectory tracking control problem of twin-propeller non-rudder unmanned surface vehicles (TPNR USVs) subject to propeller faults. Firstly, a propeller model of TPNR USVs is constructed by decomposing propeller thrusts on the body-fixed reference frame. A propeller fault model is also established by taking into account floating and loss-of-effectiveness faults. Secondly, to ensure tracking errors stay in reasonable ranges, a novel guaranteed transient performance method is proposed. Meanwhile, corresponding error transformation functions are constructed. Thirdly, by utilizing the excellent nonlinearity approximation performance of neural networks (NNs), an adaptive fault-tolerant trajectory tracking control scheme, which can guarantee TPNR USVs track the desired trajectory quickly and accurately even in the event of propeller faults, is proposed. Finally, the fault-tolerant trajectory tracking performance analysis demonstrates the efficiency of the proposed control scheme.

Robust adaptive three-dimensional trajectory tracking control scheme design for small fixed-wing UAVs

This paper aims to tackle the three-dimensional trajectory tracking problems of small fixed-wing unmanned aerial vehicles subject to nonlinearities, uncertainties and wind disturbances via adaptive techniques. The control objective is to efficiently control the thrust and the deflections of control surfaces to ensure the unmanned aerial vehicle arrives at a specified location within a given time frame. However, achieving this goal for small fixed-wing unmanned aerial vehicles can be challenging because the precise dynamic model and several parameters are not accessible, making most existing control strategies unworkable. Motivated by these facts, based on feedback linearization techniques, we derive linear models with equivalent disturbances to describe the translational dynamics without requiring precise aerodynamic force model information. To deal with the dilemmas where the norm bounds of equivalent disturbances depend on control inputs, system states, and unknown disturbances, a novel robust adaptive control strategy is designed for position control. Based on the assumption of two-time separation, the control scheme incorporates two parts, namely, a position controller containing the horizontal-plane and altitude parts and a robust filter-based attitude regulator. Also, to prevent chattering issues, we design a practical and robust adaptive position controller under which the tracking error is ultimately bounded The overall closed-loop stability is theoretically investigated based on the Lyapunov arguments. Hardware-in-loop simulation experiments are performed to testify our developed control scheme.

Adaptive Neural Trajectory Tracking Control for n-DOF Robotic Manipulators With State Constraints

This paper proposes an adaptive neural trajectory tracking control scheme for n-DOF robotic manipulators subjected to parameter variations, unknown functions, and time-varying external disturbances. First, the computed torque control (CTC) method is designed to reduce the system's nonlinearity. Second, radial basis function neural networks (RBFNNs) are constructed to approximate the uncertainties due to parameter variations and unknown functions. It's also important to note that the RBFNN's centers and widths are defined by state constraints. As a result of the nonlinear disturbance observer (NDO), the RBFNNs' approximation errors and disturbances are estimated to further improve tracking performance. The barrier Lyapunov function (BLF) ensures the closed-loop system's stability, guaranteeing tracking performance while preventing state constraint violation. Furthermore, sensitivity analysis provides a ranking of the importance of design parameters in influencing dynamic responses. Finally, simulations on a 7-DOF robotic manipulator are performed to validate the effectiveness of the proposed method.

Adaptive tracking control for a class of stochastic nonlinear systems with input delay using multidimensional Taylor network

AbstractIn this paper, an adaptive tracking control strategy based on multidimensional Taylor network (MTN) is designed for a class of stochastic nonlinear systems with input delay, in which MTN is mainly introduced to approximate uncertain nonlinear functions. Moreover, the effect of input delay is compensated by exploiting an appropriate auxiliary system with the same order as the considered system. By utilizing the backstepping technique and classical adaptive approach, an MTN‐based adaptive tracking control method is proposed, which guarantees that all the signals in the closed‐loop system remain bounded in probability. Furthermore, unlike most existing approaches, the tracking error can be bounded by an explicit expression of design parameters in the mean quartic sense. Finally, two examples are given to verify the validity of the presented scheme.

Low-Computation Adaptive Saturated Self-Triggered Tracking Control of Uncertain Networked Systems

In this paper, a low-computation adaptive self-triggered tracking control scheme is proposed for a class of strict-feedback nonlinear systems with input saturation. By introducing two novel error transformation functions, the designed low-computation adaptive control scheme can overcome the problem of complexity explosion in the absence of any filters, such that the developed control scheme is more applicable. In addition, to save communication resources in networked systems, a self-triggered communication strategy is proposed which can predict the next trigger point based on the current information. Compared with traditional event-triggered mechanisms, the computational burden arising from continuous monitoring of threshold conditions was successfully avoided. Furthermore, the input saturation problem considered in this paper prevents the overload phenomenon caused by signal jumps, and the adverse effects are compensated by introducing an auxiliary system. The effectiveness of the developed control scheme is verified through a simulation example.

Barrier function-based prescribed-performance adaptive attitude tracking control for spacecraft with uncertainties

This paper addresses the problem of robust adaptive attitude tracking control for spacecraft with mismatched and matched uncertainties. The idea of disturbance estimation and compensation is introduced into the control design. First, finite-time disturbance observers are developed for different channels of spacecraft based on barrier functions for achieving finite-time asymptotic estimates of unknown bounded uncertainties in the system. Second, a class of prescribed performance functions is considered in the design of the barrier function. The spacecraft attitude adaptive tracking control strategy with finite-time convergence capability and prescribed performance is proposed based on the designed finite-time disturbance observers and barrier function. Finally, the theoretical findings are verified by numerical simulations and compared with the simulation results of existing methods.

Event-triggered adaptive finite time trajectory tracking control for Underactuated Vessel considering unknown time-varying disturbances

Abstract This paper aims to solve the finite-time trajectory tracking problem of underactuated surface ships under the influence of dynamic uncertainty, unknown external time-varying interference and limited communication resources, an event-triggered robust adaptive finite-time trajectory tracking control method for underactuated ships is designed by combing the existing trajectory tracking control methods and engineering needs in actual navigation. It can obviously improve the tracking accuracy of the ship, and complete the tracking task safely and efficiently. This scheme transforms the composite uncertain vector synthesized by uncertain parameters and external disturbances into a linear parameterized form. Next, considering the limitation of communication resources, a relative threshold event triggering mechanism is introduced to further extend the triggering time interval on the basis of the fixed threshold triggering scheme. Then, an event-triggered robust adaptive finite-time trajectory tracking control scheme is designed for underactuated ships, and a rigorous stability proof is provided for the finite-time trajectory tracking control scheme designed above through Lyapunov stability theory. Simulation experiment is carried out in MATLAB and the results show that the finite-time trajectory tracking control scheme proposed in this paper can effectively solve the problems of dynamic uncertainty, unknown time-varying interference from the outside world, and limitation of communication resources. This paper can provide theoretical support for the autonomous navigation of intelligent ships.

An adaptive control system for path tracking of crawler combine harvester based on paddy ground conditions identification

The navigation technology of crawler combine harvester has a pivotal role in unmanned rice harvesting and has attracted much interest in recent years due to its labor-saving and effective operation. However, the complex and variable paddy ground conditions (PGCs) significantly impact the harvester's navigation tracking accuracy, and it is not easy to obtain stable and excellent performance. In this study, the cone index (CI) was used to classify the PGC and constructed a feature vector for characterizing the PGC using the vibration acceleration of the harvester. The Particle Swarm Optimization Support Vector Machine (PSO-SVM) algorithm was used to identify the PGC classes, and the identification accuracy was 91.73% and 91.67% for the training and the test sets, respectively. The field tests showed that the identification accuracy of the PSO-SVM algorithm for PGCs of each class is 85.0%, 83.0% and 90.0%, respectively. An orthogonal test method developed a steering control model adapted to PGC of three classes to enhance the path tracking accuracy, and the target path was tracked using a novel adaptive tracking control strategy. The field path tracking tests showed that the standard deviations of the lateral deviation of the harvester in the paddy fields of each PGC class are 0.053 m, 0.039 m and 0.045 m, respectively, and the standard deviations of the heading deviation are 1.120°, 0.895° and 0.877°, respectively. The proposed algorithm improves the performance of straight-line path tracking over the conventional pure tracking algorithm. Accordingly, the method proposed in this study can enable the crawler combine harvester to operate stably with high path tracking accuracy in paddy fields with various ground conditions.

Nonsingular Fixed-Time Control of Nonstrict Feedback MIMO Nonlinear System With Asymptotically Convergent Tracking Error

In this paper, the research on fixed-time tracking control problem of multiple-input multiple-output (MIMO) nonlinear systems with non-strict feedback control structure is carried out. By means of the fuzzy logic systems (FLSs), the unknown system nonlinear dynamics are identified, and the “algebraic loop problem” is solved. Then, through the combination of adaptive back-stepping recursive technology and adding power integration technology, a non-singular fixed-time adaptive tracking control scheme is presented. Under the action of the presented control scheme, the system can track the specified reference signal within a fixed time independent of the initial state of the system, and the tracking control error can converge to zero asymptotically. Next, on the basis of the fixed-time Lyapunov stability theory, the practical fixed-time stability of the closed-loop system is theoretically demonstrated. Finally, two simulations verify the validity and practicability of the proposed control scheme.

Singularity-free adaptive control of MIMO discrete-time nonlinear systems with general vector relative degrees

This paper develops a singularity-free adaptive tracking control scheme for a general class of multi-input and multi-output uncertain discrete-time nonlinear systems with non-canonical control gain matrices. The estimation of the control gain matrices, especially in some non-canonical forms, may be singular during parameter adaptation, which leads to the singularity problems of the adaptive control laws. This paper employs the matrix decomposition technique to solve the problem under a linearly parameterized adaptive control framework. The state and output feedback cases are addressed, respectively, to ensure closed-loop stability and asymptotic output tracking. Compared with the existing results, the features of the proposed adaptive control scheme include: (i) the proposed control laws do not involve the high-gain issue commonly encountered in robust control methods; (ii) two different filtered tracking error signals are introduced for the state and output feedback cases, respectively. These filters are crucial to avoid causality contradiction of the adaptive control laws commonly encountered in adaptive control of discrete-time systems; and (iii) a future time signal estimation-based adaptive control law is developed to ensure asymptotic output tracking for the output feedback case without requiring the high-gain observer. Finally, an illustrative example is given to verify the validity of the proposed control scheme.

Distributed Adaptive Formation Tracking Control of Mobile Robots With Event-Triggered Communication and Denial-of-Service Attacks

This paper focuses on distributed adaptive formation tracking control of mobile robots under event-triggered communication (ETC) and Denial-of-Service (DoS) attacks. Dynamic level of model for mobile robots and directed communication graph condition are considered. To handle the constraint that only part of the robots can access full knowledge of the desired trajectories, distributed event-triggered based estimators are designed. Then, an adaptive tracking control scheme is designed for each robot by utilizing backstepping tech- nique. By analyzing the effects of DoS attacks on ETC, a stability condition constraining the active and suspended durations for each attack period is provided, based on which all closed-loop signals are ensured to be locally uniformly bounded. Moreover, Zeno behaviors are excluded. Experimental results are presented to validate theoretical findings.

Adaptive fuzzy finite-time tracking control of nonlinear systems with unmodeled dynamics

This paper considers an adaptive fuzzy finite-time tracking control issue for uncertain nonlinear systems in the presence of unmodeled dynamics via the backstepping technique. In the design procedure, the traditional dynamic signal structure is improved firstly such that it is suitable for the controller design within finite-time interval universally for the controlled systems and it can be utilized to dominate the unmodeled dynamics. The dynamic disturbances are compensated by nonlinear damping terms. The fuzzy logic systems (FLSs) are used to package the unknown nonlinearities. By constructing the appropriate Lyapunov functions in recursive step and employing the finite-time stability theorem, the developed FLS-based adaptive tracking control strategy within finite-time interval, which ensures the boundedness of all the closed-loop signals and the convergence of tracking error. In the end, simulation results are provided to test the availability of the developed strategy.

A prescribed-performance-based adaptive finite-time tracking control scheme circumventing the dependence on the system initial condition

This paper is concerned with the prescribed finite-time tracking control problem for a class of nonlinear systems with unknown initial condition, state time-varying delays and external disturbances. A new adaptive prescribed finite-time tracking control scheme bypassing system initial condition is proposed via the prescribed performance method. Several nonlinear mapping functions (NMFs) and a new prescribed finite-time performance function (PFTPF) combining finite-time control with prescribed performance control are constructed to obtain the scheme. In this paper, the assumptions on the time-delay terms are removed by means of the designed Lyapunov–Krasovskii functions. In addition, to avoid step impact effect arisen by an overlarge initial control input, a control input with zero initial value is obtained. Aiming at the design of zero initial control input, a new Lyapunov stability criterion form is put forward. Two practical examples are presented to confirm the effectiveness and superiority of the proposed scheme.

Adaptive Neural Control of Nonlinear Nonstrict Feedback Systems With Full-State Constraints: A Novel Nonlinear Mapping Method.

In this work, a neural-networks (NNs)-based adaptive asymptotic tracking control scheme is presented for a class of uncertain nonstrict feedback nonlinear systems with time-varying full-state constraints. First, we construct a novel exponentially decaying nonlinear mapping to map the constrained system states to new system states without constraints. Instead of the traditional barrier Lyapunov function methods, the feasible conditions which require the virtual control signals satisfying the constraint requirements are removed. By employing the Nussbaum design method to eliminate the effect of unknown control gains, the general assumption about the signs of the unknown control gains is relaxed. Then, the nonstrict feedback form of the system can be pulled back to the strict feedback form through the basic properties of radial basis function NNs. Simultaneously, the intermediate control signals and the desired controller are constructed by the backstepping process and the Nussbaum design method. The designed controller can ensure that all signals in the whole closed-loop system are bounded without the violation of the constraints and hold the asymptotic tracking performance. In the end, a practical example about a brush dc motor driving a one-link robot manipulator is given to illustrate the effectiveness of the proposed design scheme.

Adaptive asymptotic tracking control design for high-order uncertain nonlinear systems

This paper investigates the asymptotic tracking control of uncertain high-order nonlinear systems with time-varying desired signals.A new universal adaptive tracking control strategy is introduced, which combines the technique of adding a power integrator, adaptive technology and a series of algebraic transformations to achieve asymptotic tracking performance successfully. Compared to the traditional tracking control strategies, the proposed control scheme guarantees that the output tracking error of the high-order nonlinear system asymptotically converges to zero and is no longer limited to a time-varying compact domain. While ensuring that all signals in the closed-loop system are bounded, the controller also has sufficient ability to compensate for the unknown quantity, virtual control coefficient and parameter uncertainty of the system. Finally, the feasibility and effectiveness of the theoretical results are verified by a simulation example.

Adaptive Event-Triggered Consensus Tracking Control Schemes for Uncertain Constrained Nonlinear Multi-Agent Systems

Adaptive Event-Triggered Consensus Tracking Control Schemes for Uncertain Constrained Nonlinear Multi-Agent Systems

Disturbance-Observer-Based Adaptive Fuzzy Tracking Control for Unmanned Autonomous Helicopter With Flight Boundary Constraints

In this article, a disturbance-observer-based adaptive fuzzy tracking control scheme is proposed for a medium-scale unmanned helicopter of six degrees of freedom in the presence of system uncertainties, flight boundary constraints, and external disturbances. A flight boundary protection algorithm is proposed to ensure its flight trajectory within the given safety range. A fuzzy logic system is utilized to estimate the system uncertainties and a nonlinear disturbance observer is adopted to handle the unknown compound terms of the external disturbances and the estimation errors resulting from the fuzzy logic system. An inverse optimal control approach is then used to avoid solving the Hamilton–Jacobi–Bellman equation in minimizing a cost function in the attitude loop. It is shown via the Lyapunov method that the desired safe tracking performance of the position loop and attitude loop of the controlled unmanned helicopter can be achieved. Simulations are provided to illustrate the effectiveness of the proposed control scheme.