Control Problem For Nonlinear Systems Research Articles

Adaptive Fuzzy Fixed-Time Tracking Control for Nonlinear Systems With Time-Varying Full-State Constraints and Actuator Hysteresis

This paper investigates the problem of adaptive fuzzy fixed-time control for a class of nonlinear systems subject to time-varying full-state constraints and actuator hysteresis. The fuzzy logic systems (FLSs) are employed to approximate the unknown nonlinear dynamics. A tan-type nonlinear mapping (NM) approach is used to solve the control problem of nonlinear systems with full-state constraints. The actuator hysteresis nonlinearities are expressed by differential equations with unknown parameters. And combining the adaptive backstepping control technique with the fixed-time stability theory, an adaptive fuzzy fixed-time control method is presented. The proposed control scheme can guarantee the boundedness of all signals in the closed-loop system without violating the state constraints, and the output can accurately track the desired signals in a fixed time. Finally, the feasibility of the presented algorithm is verified by simulation results.

Further Results on Optimal Tracking Control for Nonlinear Systems With Nonzero Equilibrium via Adaptive Dynamic Programming.

This article develops a novel cost function (performance index function) to overcome the obstacles in solving the optimal tracking control problem for a class of nonlinear systems with known system dynamics via adaptive dynamic programming (ADP) technique. For the traditional optimal control problems, the assumption that the controlled system has zero equilibrium is generally required to guarantee the finiteness of an infinite horizon cost function and a unique solution. In order to solve the optimal tracking control problem of nonlinear systems with nonzero equilibrium, a specific cost function related to tracking errors and their derivatives is designed in this article, in which the aforementioned assumption and related obstacles are removed and the controller design process is simplified. Finally, comparative simulations are conducted on an inverted pendulum system to illustrate the effectiveness and advantages of the proposed optimal tracking control strategy.

New Approach to the Synthesis of Optimal Terminal Control of Nonlinear Dynamic Systems

The problem of constructing common solutions to terminal control problems of nonlinear systems is considered here. Previously proven positions are used that the optimal trajectory is an envelope of a parametric family of surfaces (a parametric family of singular curves), and that optimal control can be found on this family. The fact that at each point of the optimal trajectory the vector-function of Lagrange factors is tangent to it, but also tangent to the singular curve, is played out here. A constructive method of constructing singular curves based on conditional separation of variables in the Hamilton-Jacobi equation is given. The " free" parameters of singular curves are based on the condition of minimizing the terminal functionality, which avoids an explicit solution to the boundary problem for a class of nonlinear dynamic systems, and simplifies computational algorithms. Singular curves are described by a reduced (abbreviated) mathematical model. Thus, to synthesize the law of optimal control, we must use the complete (original) mathematical model of the dynamic system, but to calculate it at one time or another, it is enough reduced model. This consideration defines the principle of informational dualism. An illustrative example is given. It has been shown that this approach can be used to solve some classes of differential games.

Disturbance Rejection Extremum Seeking: A Sliding Mode Control Approach

This paper investigates the sliding mode extremum seeking control problem for nonlinear systems with disturbances to improve the disturbance rejection capability, delivering promising searching accuracy and efficiency under disturbances. The existing results in sliding mode extremum seeking control suffer from the drawback of unsatisfactory transient performance and slow convergence speed when disturbances, especially unmatched disturbances, are injected into the system. To this end, a disturbance rejection sliding mode extremum seeking control approach is proposed in this paper. A nonlinear disturbance observer is developed for disturbance estimation, and then a disturbance rejection sliding mode extremum seeking control algorithm is proposed by exploiting the disturbance estimation. Under the proposed approach, the disturbances can be online estimated and compensated. As a result, the cost function is less disturbed by unknown disturbances, and faster convergence speed as well as shorter recovery time can be obtained. The proposed method is applied for directional antenna automatic tracking system. Simulation results show that the newly proposed searching control approach outperforms the baseline extremum searching approaches in terms of much more reliable and high efficiency searching performance under disturbances.

Prescribed Performance Adaptive Fuzzy Control for Affine Nonlinear Systems With State Constraints

This article considers the problem of prescribed performance control for a class of affine nonlinear systems with state constraints. Fuzzy logic systems (FLSs) are employed to cope with unknown nonlinear functions. In addition, an additive transformation and one-to-one mapping method are introduced to handle the control problem of nonlinear systems with full-state constraints. Then, based on adaptive backstepping control, we develop a new prescribed performance adaptive fuzzy control method, which not only makes the tracking error be constrained within the prescribed range but also ensures the other state variables do not violate the predefined constraints. The simulation example verify the feasibility of the control method.

A new event-triggered adaptive tracking controller for nonlinear systems with unknown virtual control coefficients

This paper investigates the adaptive event-triggered tracking control problem for nonlinear systems with virtual control coefficients being unknown nonlinear functions. Firstly, different from approximators using fuzzy logics/neural networks or Nussbaum gain technique, a new transformation with less computation is introduced based on a monotone decreasing positive continuous function to handle uncertain virtual control coefficients (UVCCs). Furthermore, a novel event-triggered threshold scheme is firstly developed, which can switch between the fixed and relative threshold strategies smoothly. Finally, an adaptive event-triggered tracking controller is constructed, whose effectiveness is verified by both theoretical analysis and simulations.

Adaptive Control and Application for Nonlinear Systems With Input Nonlinearities and Unknown Virtual Control Coefficients.

This article is devoted to an adaptive tracking control problem for nonlinear systems with input deadzone and saturation, whose virtual control coefficients include the known and unknown terms. A novel smooth function is first introduced to approximate the input nonlinearities. By utilizing an auxiliary variable and the Nussbaum gain technique, an improved real control signal is constructed to handle the uncertainties of the virtual control coefficients and input nonlinearities. Furthermore, an adaptive tracking controller is constructed and applied to the attitude control of a quadrotor, which guarantees the boundedness of all the signals in the resulting closed-loop system. Finally, both stability analysis and simulation results validate the effectiveness of the developed control strategy.

Event-Triggered Adaptive Fuzzy Tracking Control for Nonlinear Systems With Unknown Control Directions

This article addresses an event-triggered adaptive fuzzy tracking control problem for nonlinear systems with unknown control directions. To achieve the practical tracking, fuzzy logic systems are employed to approximate unknown nonlinear function. Nussbaum functions are adopted to address the problem of the unknown control directions. The newly designed controller not only guarantees the tracking error that converges to an arbitrary small neighborhood near the zero but also reduces the communication burden from the controller to the actuator. Moreover, the feasibility of the proposed event-triggered mechanism is verified by excluding Zeno behavior. Finally, the simulation result proves the effectiveness of the designed control scheme.

IBLF‐based event‐triggered adaptive learning control of nonlinear systems with full state constraints

SummaryThis article focuses on the adaptive asymptotic learning tracking control problem of nonlinear systems with full state constraints. First, an adaptive neural network tracking algorithm is proposed which combines a time‐varying feedback element and robust compensation feedback element in the form of smooth function to guarantee that output signal tracks the reference signal asymptotically. Apart from this, a time‐varying integral barrier Lyapunov function is utilized to ensure that the system states are always kept in the constraint region. Furthermore, the event‐triggered mechanism is designed to efficiently reduce unnecessary transmissions. Compared with the other literatures, the original state constraint problem is directly solved by the proposed adaptive control algorithm. Finally, simulation is included to validate the built theoretical results.

Composite learning sliding mode control of uncertain nonlinear systems with prescribed performance

This paper explores the prescribed performance tracking control problem of nonlinear systems with triangular structure. To obtain the desired transient performance and precise estimations of uncertain terms, the techniques of neural network control, sliding mode control and composite learning control are incorporated into the proposed control method. The presented control strategy can ensure the tracking error converges to a prescribed small residual set. Compared with the persistent excitation condition required in the conventional adaptive control, the interval excitation condition needed in the proposed control approach is weak, which guarantees that the radial basis function neural networks approximate the unknown nonlinear terms more accurately. Finally, two simulation examples are exploited to manifest the effectiveness of the proposed approach.

The Design of an Anti-Synchronization Control Algorithm for a 4D Laser System

When studying the control problems of nonlinear systems, there are always uncertainties and disturbances. The existence of this phenomenon will increase the error in production engineering and reduce work efficiency. In order to reduce the nonlinear asymmetric control, the control method of a laser hyperchaotic system is designed in this paper. The system is a complex number system, with remarkable nonlinear characteristics. The system is divided into two parts by calculating the state transformation matrix, which shows that the system can realize simultaneous synchronization and anti-synchronization. Firstly, in the ideal case, the stabilization, synchronization, and anti-synchronization of the system are studied by using the dynamic gain feedback method, and a dynamic feedback controller is designed. Secondly, in the case of uncertainty and disturbance, a dynamic feedback control strategy based on uncertainty and disturbance estimator (UDE) is proposed. With the aim to solve the control problem of the system, the corresponding controller is designed to modify the system. Finally, through simulation and comparison, it is verified that the effect of this method is remarkable.

Adaptive fixed-time control for nonlinear systems against time-varying actuator faults

The adaptive fixed-time control problem for nonlinear systems with time-varying actuator faults is investigated in this paper. A novel adaptive fixed-time controller is designed via combining the Lyapunov stability theory with the backstepping method. It can be adapted to both system uncertainties and unknown actuator faults. Compared with the existing fault-tolerant control schemes subject to actuator faults, the adaptive fixed-time neural networks control scheme can make sure that the tracking error is convergent in a small neighborhood of the origin within a fixed-time interval, and it does not depend on the original states of the system and actuator faults. In light of the control scheme proposed in this paper, the fixed-time stability of the closed-loop system can be guaranteed by theoretical analysis, and a numerical example is provided to verify the effectiveness of obtained theoretical results.

Control Problems of Nonlinear Systems Using Feedback: Review and Analysis

Control Problems of Nonlinear Systems Using Feedback: Review and Analysis

A New Adaptive Controller for Nonlinear Systems with Uncertain Virtual Control Gains

This paper addresses the adaptive asymptotic tracking control problem for nonlinear systems whose virtual control gains are unknown nonlinear functions of system states. Only in the first step, the Nussbaum gain technique is utilized to handle the uncertain virtual control gain. In the remaining steps, virtual control gains are dealt with by constructing novel control laws without the approximation of the uncertain nonlinear functions and external disturbances by neural networks or fuzzy logic. New adaptive laws are defined to compensate for unknown virtual control gains, uncertain parameters, and external disturbances. Finally, an adaptive tracking controller is designed and applied to the control of a 3‐order robot system, which guarantees the boundedness of all the signals in the closed‐loop system and asymptotic stability of the tracking error.

An approach to solving optimal control problems of nonlinear systems by introducing detail-reward mechanism in deep reinforcement learning

In recent years, dynamic programming and reinforcement learning theory have been widely used to solve the nonlinear control system (NCS). Among them, many achievements have been made in the construction of network model and system stability analysis, but there is little research on establishing control strategy based on the detailed requirements of control process. Spurred by this trend, this paper proposes a detail-reward mechanism (DRM) by constructing the reward function composed of the individual detail evaluation functions in order to replace the utility function in the Hamilton-Jacobi-Bellman (HJB) equation. And this method is introduced into a wider range of deep reinforcement learning algorithms to solve optimization problems in NCS. After the mathematical description of the relevant characteristics of NCS, the stability of iterative control law is proved by Lyapunov function. With the inverted pendulum system as the experiment object, the dynamic environment is designed and the reward function is established by using the DRM. Finally, three deep reinforcement learning algorithm models are designed in the dynamic environment, which are based on Deep Q-Networks, policy gradient and actor-critic. The effects of different reward functions on the experimental accuracy are compared. The experimental results show that in NCS, using the DRM to replace the utility function in the HJB equation is more in line with the detailed requirements of the designer for the whole control process. By observing the characteristics of the system, designing the reward function and selecting the appropriate deep reinforcement learning algorithm model, the optimization problem of NCS can be solved.

Data-Driven Optimal Control of Nonlinear Dynamics Under Safety Constraints

This letter considers the optimal control problem of nonlinear systems under safety constraints with unknown dynamics. Departing from the standard optimal control framework based on dynamic programming, we study its dual formulation over the space of occupancy measures. For control-affine dynamics, with proper reparametrization, the problem can be formulated as an infinite-dimensional convex optimization over occupancy measures. Moreover, the safety constraints can be naturally captured by linear constraints in this formulation. Furthermore, this dual formulation can still be approximately obtained by utilizing the Koopman theory when the underlying dynamics are unknown. Finally, to develop a practical method to solve the resulting convex optimization, we choose a polynomial basis and then relax the problem into a semi-definite program (SDP) using sum-of-square (SOS) techniques. Simulation results are presented to demonstrate the efficacy of the developed framework.

Choosing the number of time intervals for solving a model predictive control problem of nonlinear systems

Model predictive control (MPC) is a control strategy that can handle state and control multi-variables at same time. To use the MPC using direct methods for solving the a dynamic optimization problem, one needs, for example, to transform the optimization problem into a nonlinear programming (NLP) problem by dividing the prediction horizon into equal time intervals. In this work, we suggest a tool and procedures for helping to choose a ‘compromise’ number of time intervals with a needed accuracy, objective cost, number of turned NLP iterations and computational time. On the other hand, we offer a simplified nonlinear program to ensure the convergence of a class of finite optimal control problem by modifying the state box constraints. In particular, a special type of box constraints were used to the constrained optimal control problem to enforce the state trajectories to reach the desired stationary point. These box constraints are characterized by some parameters that are easily optimized by our proposed nonlinear program. Our proposed tools are tested using two case studies; nonlinear continuous stirred tank reactor (CSTR) and nonlinear batch reactor.

Interval Type-2 Fuzzy Sampled-Data H∞ Control Under Stochastic Communication

The manuscript focuses on the interval type-2 (IT2) fuzzy sampled-data H∞ control problem for nonlinear systems with parameter uncertainties and random communication. Nonlinear systems are constructed as Takagi–Sugeno (T–S) fuzzy form, and the parameter uncertainties are represented by employing the membership functions. A reliable controller is designed, based on which the fuzzy closed-loop system is stable and the H∞index is satisfied. In the analysis, the free-weighting matrices and the slack ones are introduced, and the mathematical expectation and the linear matrix inequalities (LMIs) are used. Furthermore, by taking the inverted pendulum system as an example, the superiority and applicability of the control strategy are demonstrated.

Switching event‐triggering mechanisms for integral input‐to‐state stable nonlinear systems

AbstractThis article studies the event‐triggered control problem for nonlinear systems that are merely integral input‐to‐state stable (iISS) with respect to measurement errors and external inputs. By using the iISS Lyapunov function, which comes from a necessary and sufficient condition on iISS, an integral‐based event‐triggering mechanism is introduced to compensate the effects of measurement errors. Under some assumptions on the gains of the iISS Lyapunov function, both the closed‐loop iISS and Zeno‐freeness are proved when the external inputs are measurable. In the presence of unknown disturbances, a novel switching event‐triggering mechanism is designed based on the relationship between the transmitted state and some threshold constant. Subsequently, the corresponding lower bound of inter‐event times is given explicitly to ensure Zeno‐freeness. It is shown that the proposed switching mechanism can be applicable to the nonlinear systems where some existing event‐triggering mechanisms are invalid in avoiding Zeno behavior. Finally, numerical simulations are provided to illustrate the efficiency and feasibility of the obtained results.

Adaptive Optimal Robust Control for Uncertain Nonlinear Systems Using Neural Network Approximation in Policy Iteration

In this study, based on the policy iteration (PI) in reinforcement learning (RL), an optimal adaptive control approach is established to solve robust control problems of nonlinear systems with internal and input uncertainties. First, the robust control is converted into solving an optimal control containing a nominal or auxiliary system with a predefined performance index. It is demonstrated that the optimal control law enables the considered system globally asymptotically stable for all admissible uncertainties. Second, based on the Bellman optimality principle, the online PI algorithms are proposed to calculate robust controllers for the matched and the mismatched uncertain systems. The approximate structure of the robust control law is obtained by approximating the optimal cost function with neural network in PI algorithms. Finally, in order to illustrate the availability of the proposed algorithm and theoretical results, some numerical examples are provided.