Parameter Adaptation Law Research Articles

Improved adaptive robust control of direct-drive pump-valve cooperative brake-by-wire unit with disturbance compensation

Abstract Aiming at the needs of high-level autonomous driving, a direct-drive pump-valve coordinated brake-by-wire unit was designed. To improve the response speed, control accuracy, and robustness of the brake-by-wire unit, an improved adaptive integral robust control method (AIRC-AD) based on a novel adaptive law and disturbance compensation was proposed. By integrating pressure tracking error information and overall parameter estimation error information, a novel parameter adaptive law with fast convergence speed was designed. A finite-time disturbance observer was designed to observe the overall system disturbance, improving the system’s anti-disturbance performance. Finally, the stability of the control method was proven using the Lyapunov stability proof method. The results show that under step target signals, sinusoidal target signals and triangular wave target signals, AIRC-AD has better response speed and control accuracy. In conditions such as increased permanent magnet temperature, increased coil resistance, or reduced flow area due to hydraulic pipeline blockage, AIRC-AD has better adaptability. The unit and the method can meet the demand for precise braking force control in high-level autonomous driving.

Adaptive Predefined Time Control for Strict-Feedback Systems with Actuator Quantization

An adaptive predefined-time quantized control issue is considered for strict-feedback systems with actuator quantization. To handle the unknown nonlinearities of a system, the neural networks are first applied to model them. To analyze the predefined-time stability under approximation error, a stability lemma is first introduced. Then, a refreshing predefined-time quantized control strategy is presented. Compared with the existing control studies for actuator quantization, the stability time is not influenced by the initial state and can be set in advance. Furthermore, unlike the available predefined-time control studies, a new parameter adaptive law and virtual controllers are designed. This design not only ensures the predefined-time stability, but overcomes the singularities of system in coventional backstepping control design because of repeating differentiation for virtual controllers.

A Lyapunov-based adaptive control strategy with fault-tolerant objectives for proton exchange membrane fuel cell air supply systems

Proton exchange membrane fuel cells (PEMFCs) are being extensively studied for large-scale applications. The need for reliable and safe system integration processes emphasizes the importance of health-conscious control research. Considering their high-frequency operation and load variations, the reliability of PEMFCs can be compromised by mechanical failures within air supply systems. Therefore, this paper proposes an adaptive control strategy with fault-tolerant objectives for regulating the oxygen excess ratio (OER). Specifically, it addresses two common faults within air compressors, namely compressor overheating and increased mechanical friction. While these faults have been studied in fault diagnosis research, their treatment within the context of fault-tolerant control is relatively uncommon. To ensure reliable OER regulation under multiple fault occurrences, estimator-based parameter adaptation laws are firstly derived using Lyapunov theory during the stability demonstration process. Then, the baseline controller is designed using an extended state observer and feedback linearization, with a clear presentation of the existence and stability of the state transformation. Furthermore, extensive numerical tests are conducted to demonstrate the effectiveness of the proposed control strategy. The proposed adaptive control strategy ensures consistent regulation outcomes without compromising performance under healthy conditions, highlighting its potential for large-scale application as a standard control approach in practical applications.

The research on Adaptive Fuzzy Tracking Supervisory Control in the control system of average coolant temperature of lead–bismuth-cooled reactor under multiple operating conditions in the marine environments

Lead–bismuth-cooled reactor nuclear power equipment is severely affected by marine environments, causing operational attitude changes such as heeling and rolling. Conventional controllers cannot track set points rapidly. Therefore, this paper proposes an Adaptive Fuzzy Tracking Supervisory Control method. Firstly, the reactor mathematical model based on fuzzy basis functions is obtained through fuzzy mathematics. Secondly, a coolant average temperature tracking controller is designed on Lyapunov stability theory. To ensure the system returns to stable domain, supervisory compensatory control algorithm is developed. Next, the parameters of fuzzy model are adjusted using Fuzzy universal approximation theorem. By introducing discrepancies between actual system and fuzzy model outputs, parameter adaptive law is designed through Lyapunov theorem. This enables real-time parameter adjustment for fuzzy model and fuzzy tracking supervisory controller, enhancing load tracking performance of coolant’s average temperature. Finally, simulation experiments demonstrate that adaptive fuzzy tracking supervisory controller has stronger adaptability to multiple operating conditions under marine environments.

Adaptive sliding mode control with disturbance estimation for hydraulic actuator systems and application to rock drilling jumbo

For achieving high-performance control of hydraulic actuator systems, an adaptive sliding mode control with disturbance estimation algorithm is proposed and successfully applied to the hydraulic actuator systems of rock drilling jumbo. A switching extended state observer for hydraulic actuator systems is proposed, which combines the advantages of the linear extended state observer and the nonlinear extended state observer to estimate system disturbances more efficiently. The designed parameter adaptive law provides adaptivity to the system parameters, while the disturbance estimation algorithm can online estimate the unknown disturbances, such integration can enhance the adaptability and robustness of the sliding mode control algorithm. Rigorous simulation tests and practical application experiments are conducted to evaluate the performance of the proposed controller. The reliability of the designed parameter adaptive law and switching extended state observer is validated in detail in simulation studies. In the application experiments of rock drill jumbo, the results indicate the proposed controller is superior to sliding mode control with disturbance estimation and PID control. The average error of the developed controller is reduced by 58 %, 54 %, and 56 % compared to the sliding mode control with disturbance estimation controller, and 82 %, 78 %, and 79 % compared to the PID controller in tracking the low-frequency, high-frequency, and superimposed sinusoidal signals, respectively. Moreover, both the convergence of the uncertain parameters and the estimation of the unknown disturbances in the experiments are satisfactory. These experimental results adequately demonstrate the practical application value of the proposed controller.

Robust multiple fault detection and estimation approaches based on adaptive finite‐time observer for nonlinear systems

AbstractThis article investigates robust multiple fault detection (FD) and fault estimation (FE) schemes, based on adaptive finite‐time observer (AFTO), applied to nonlinear systems with disturbances, actuator faults, and sensor faults. The AFTO is designed, and a parameter adaptive law is constructed to continuously optimize the residual based performance function to improve the AFTO robustness. The schemes are able to simultaneously detect and estimate unknown states, disturbances, and faults. The AFTO's design, finite‐time convergence and stability conditions are guaranteed in terms of linear matrix inequalities. Finally, simulation results, based on two typical practical systems, are given to illustrate the effectiveness of the proposed approaches. The simulation results also show that the designed AFTO can offer faster convergence speed, higher estimation accuracy, and stronger robustness than conventional observer and sliding mode observer.

Active Fault Tolerant Nonsingular Terminal Sliding Mode Control for Electromechanical System Based on Support Vector Machine

Effective fault diagnosis and fault-tolerant control method for aeronautics electromechanical actuator is concerned in this paper. By borrowing the advantages of model-driven and data-driven methods, a fault tolerant nonsingular terminal sliding mode control method based on support vector machine (SVM) is proposed. A SVM is designed to estimate the fault by off-line learning from small sample data with solving convex quadratic programming method and is introduced into a high-gain observer, so as to improve the state estimation and fault detection accuracy when the fault occurs. The state estimation value of the observer is used for state reconfiguration. A novel nonsingular terminal sliding mode surface is designed, and Lyapunov theorem is used to derive a parameter adaptation law and a control law. It is guaranteed that the proposed controller can achieve asymptotical stability which is superior to many advanced fault-tolerant controllers. In addition, the parameter estimation also can help to diagnose the system faults because the faults can be reflected by the parameters variation. Extensive comparative simulation and experimental results illustrate the effectiveness and advancement of the proposed controller compared with several other main-stream controllers.

Discrete-Time Adaptive Control for Three-Phase PWM Rectifier.

This paper proposes a dual-loop discrete-time adaptive control (DDAC) method for three-phase PWM rectifiers, which considers inductance-parameter-mismatched and DC load disturbances. A discrete-time model of the three-phase PWM rectifier is established using the forward Euler discretization method, and a dual-loop discrete-time feedback linearization control (DDFLC) is given. Based on the DDFLC, the DDAC is designed. Firstly, an adaptive inductance disturbance observer (AIDO) based on the gradient descent method is proposed in the current control loop. The AIDO is used to estimate lump disturbances caused by mismatched inductance parameters and then compensate for these disturbances in the current controller, ensuring its strong robustness to inductance parameters. Secondly, a load parameter adaptive law (LPAL) based on the discrete-time Lyapunov theory is proposed for the voltage control loop. The LPAL estimates the DC load parameter in real time and subsequently adjusts it in the voltage controller, achieving DC load adaptability. Finally, simulation and experimental results show that the DDAC exhibits better steady and dynamic performances, less current harmonic content than the DDFLC and the dual-loop discrete-time PI control (DDPIC), and a stronger robustness to inductance parameters and DC load disturbances.

Adaptive backstepping sliding mode compensation control for electro‐hydraulic load simulator with backlash links

AbstractWhen a mechanical backlash between the loading piston rod and the steering gear piston rod connection of the electro‐hydraulic load simulator (EHLS), it will lead to poor loading accuracy. To solve this problem, an adaptive backstepping sliding mode controller based on the backlash feedforward compensation and state observer is proposed. First, the mechanism of surplus force generation in the EHLS is analyzed, and a mathematical model containing position disturbance and backlash nonlinearity is established based on the analysis results. Second, a state observer is designed to estimate the system state and the parameter adaptive law is designed to estimate the system parameters. Third, the adaptive backstepping method is combined with the non‐singular terminal sliding mode control (NTSMC) strategy to deal with the position disturbance and parameter uncertainty and to enhance the system's dynamic performance. Finally, a backlash feed‐forward compensator is engineered to restrain the detrimental effects caused by the backlash nonlinearity, and the simulation experimental results show that the designed new method can realize the precise loading of the EHLS under different working conditions, and the surplus force is suppressed better at the same time.

Adaptive robust control of electromagnetic actuators with friction nonlinearity and uncertainty compensation

Friction nonlinearity and uncertainty are the main factors affecting the highly performance control of electromagnetic actuators. In this paper, a nonlinear adaptive robust control strategy is proposed of electromagnetic actuators with friction nonlinearity and uncertainty compensation. First, the dynamical model of the electromagnetic actuator is established considering nonlinearity and uncertainty. Then, an adaptive robust controller is designed based on the continuously differentiable friction model to ensure that the control input is continuously and bounded. In the design of the controller, the unfavorable effects of unknown parameters in the electromagnetic actuator are eliminated by constructing a parameter adaptive law. Meanwhile, in order to improve the tracking accuracy of the electromagnetic actuator, a nonlinear robust control law is designed to ensure the robustness of the controller. The stability analysis by Lyapunov function shows that the asymptotic tracking effect can be obtained when only parameter uncertainty exists in the closed-loop system of the electromagnetic actuator, and the consistent bounded stability can be ensured when the system also exists uncertain nonlinearity. Extensive comparative results verify the effectiveness of the proposed control method.

Parameter adaptive based neural network sliding mode control for electro‐hydraulic system with application to rock drilling jumbo

SummaryRock drilling jumbo is an important large construction machine used for tunneling construction, and its automation has an urgent demand in engineering. However, the electro‐hydraulic system of the rock drilling jumbo has strong parameters uncertainties and some dynamics that are hard to model accurately, which causes certain challenges for designing model‐based high‐performance control algorithms. To solve these challenges, a parameter adaptive based neural network sliding mode control algorithm is proposed to enhance control performance of the electro‐hydraulic system. The parameter adaptive law is developed to estimate unknown parameters of the system, the neural network is applied for compensating unmodeled dynamics, and then the final control law is designed by sliding mode control method, and the stability demonstration of the closed‐loop system is given. In the simulations, the effectiveness of the designed parameter adaptive law is verified. Extensive comparison experiments are performed on a real rock drilling jumbo driven by proportional valves, the experimental results demonstrate that the developed control algorithm obviously improves the control precision of hydraulic cylinder of the rock drilling jumbo compared with the traditional sliding mode and PID control algorithm, thus the designed control algorithm can be expanded and applied for general hydraulic servo control mechanism.

Data-Driven Indirect Iterative Learning Control.

In this work, a data-driven indirect iterative learning control (DD-iILC) is presented for a repetitive nonlinear system by taking a proportional-integral-derivative (PID) feedback control in the inner loop. A linear parametric iterative tuning algorithm for the set-point is developed from an ideal nonlinear learning function that exists in theory by utilizing an iterative dynamic linearization (IDL) technique. Then, an adaptive iterative updating strategy of the parameter in the linear parametric set-point iterative tuning law is presented by optimizing an objective function for the controlled system. Since the system considered is nonlinear and nonaffine with no available model information, the IDL technique is also used along with a strategy similar to the parameter adaptive iterative learning law. Finally, the entire DD-iILC scheme is completed by incorporating the local PID controller. The convergence is proved by applying contraction mapping and mathematical induction. The theoretical results are verified by simulations on a numerical example and a permanent magnet linear motor example.

Dual cerebella model neural networks based robust adaptive output feedback control for electromechanical actuator with anti‐parameter perturbation and anti‐disturbance

SummaryTo realize a high‐accuracy tracking control of an electromechanical actuator in which only position signal is available, a new robust adaptive output feedback control strategy based on dual CMAC neural networks is proposed in this article. A high‐gain observer and a neural network are combined to estimate the unmeasured system states, in which a neural network is designed to estimate and compensate the system parameter estimation error and other uncertain nonlinearity to improve the performance of the traditional observer. A robust adaptive controller and another neural network are combined to decrease the impact of parameter perturbation and external disturbance and strengthen the system robustness. Lyapunov theorem is used to derive an on‐line weight adaption law for the two neural networks and an on‐line parameter adaptation law for the uncertain parameters to stabilize the system. Thus the parameter uncertainty and disturbance can be compensated with feedforward compensation technique. A nonlinear robust feedback term is designed to suppress the model compensation deviation. Considering the stronger approximation ability of CMAC basis function against RBF basis function, an improved CMAC neural network is introduced in the proposed controller. A large number of simulations and experiments show that the proposed controller has better tracking performance than many main‐stream output feedback controllers in present.

Hierarchical Sliding-Mode Surface-Based Adaptive Actor-Critic Optimal Control for Switched Nonlinear Systems With Unknown Perturbation.

This article studies the hierarchical sliding-mode surface (HSMS)-based adaptive optimal control problem for a class of switched continuous-time (CT) nonlinear systems with unknown perturbation under an actor-critic (AC) neural networks (NNs) architecture. First, a novel perturbation observer with a nested parameter adaptive law is designed to estimate the unknown perturbation. Then, by constructing an especial cost function related to HSMS, the original control issue is further converted into the problem of finding a series of optimal control policies. The solution to the HJB equation is identified by the HSMS-based AC NNs, where the actor and critic updating laws are developed to implement the reinforcement learning (RL) strategy simultaneously. The critic update law is designed via the gradient descent approach and the principle of standardization, such that the persistence of excitation (PE) condition is no longer needed. Based on the Lyapunov stability theory, all the signals of the closed-loop switched nonlinear systems are strictly proved to be bounded in the sense of uniformly ultimate boundedness (UUB). Finally, the simulation results are presented to verify the validity of the proposed adaptive optimal control scheme.

A novel adaptive synchronization algorithm for a general class of fractional-order complex-valued systems with unknown parameters, and applications to circuit realization and color image encryption

This article proposes an adaptive synchronization (AS) algorithm to synchronize a general class of fractional-order complex-valued systems with completely unknown parameters, which may appear in physical and engineering problems. The analytical and theoretical concepts of the algorithm rely on the mathematical framework of the Mittag-Leffler global stability of fractional-order systems. A specific control system is established analytically based on the fractional-order adaptive laws of parameters, and the corresponding numerical results are executed to verify the accuracy of the AS algorithm. The proposed synchronization method is evaluated using the fractional-order complex Rabinovich system as an attractive example. The electronic circuits of the new system with different fractional orders are designed. By utilizing the Multisim electronic workbench software, various chaotic/hyperchaotic behaviors have been observed, and a good agreement is found between the numerical results and experimental simulation. In addition, the approximation of the transfer function for different fractional-order are presented. And the corresponding resistor and capacitor values in the chain ship model (CSM) are estimated, which can be utilized in designing electronic circuits for other fractional-order systems. Furthermore, two strategies for encrypting color images are proposed using the AS algorithm and fractional-order adaptive laws of parameters. In the first strategy, the color image is treated as a single package and divided into two vectors. The first vector is embedded into transmitter parameters, while the second vector is injected into the transmitter state signals. In the second strategy, the primary RGB channel components of the original color image are extracted and separated into two vectors, and the same process is followed as in the first strategy. These strategies complicate the decryption task for intruders. Different scales of white Gaussian noise are added to color images to examine the robustness of the proposed color images encryption strategies.

Exponential Synchronization of Memristor-Based Competitive Neural Networks With Reaction-Diffusions and Infinite Distributed Delays.

Taking into account the infinite distributed delays and reaction-diffusions, this article investigates the global exponential synchronization problem of a class of memristor-based competitive neural networks (MCNNs) with different time scales. Based on the Lyapunov-Krasovskii functional and inequality approach, an adaptive control approach is proposed to ensure the exponential synchronization of the addressed drive-response networks. The closed-loop system is a discontinuous and delayed partial differential system in a cascade form, involving the spatial diffusion, the infinite distributed delays, the parametric adaptive law, the state-dependent switching parameters, and the variable structure controllers. By combining the theories of nonsmooth analysis, partial differential equation (PDE) and adaptive control, we present a new analytical method for rigorously deriving the synchronization of the states of the complex system. The derived m-norm (m ≥ 2)-based synchronization criteria are easily verified and the theoretical results are easily extended to memristor-based neural networks (NNs) without different time scales and reaction-diffusions. Finally, numerical simulations are presented to verify the effectiveness of the theoretical results.

Observer-based dynamic event-triggered adaptive control for uncertain nonlinear strict-feedback systems

This paper proposes an observer-based dynamic event-triggered adaptive control approach for uncertain nonlinear strict-feedback systems. Initially, an observer for unmeasurable states is constructed. Subsequently, employing backstepping technique, the output-feedback adaptive control law and parameter adaptive law are developed. The tuning function method is used to avoid the over-parameterization problem in parameter adaptive law design. To lower the data exchange rate within the network, a dynamic event-triggered scheme is formulated to allow real-time update control signal. Through Lyapunov theory analysis, the resulting closed-loop system is demonstrated to be stabilized, and the occurrence of Zeno behavior is effectively prevented. Finally, the simulation example validates the obtained result of the presented design approach.

Fuzzy Adaptive Nonsingular Terminal Sliding Mode Control of a Miniature Helicopter

This paper presents a fuzzy adaptive terminal sliding mode control for an unmanned miniature helicopter including uncertainties and external disturbances, and a terminal sliding surface is utilized to provide faster convergence. Due to the fact that the presented controlled system is facing a singularity problem, the controlling structure is developed to a nonsingular one. In the proposed controlling structure, a continuous nonsingular terminal sliding mode control is combined with an adaptive learning algorithm and fuzzy logic system to estimate the uncertainties; and the parameter adaptation law is obtained based on the Lyapunov stability theorem. Analytical results show that the proposed approach enables a faster and more accurate tracking performance as compared to recent controlling methods.

High precision control of direct-drive permanent magnet linear motor with adaptive friction compensation

To improve the control precision and stability of Direct-Drive Permanent Magnet Linear Motor (DPMLM) with frictional nonlinearity and uncertain disturbances at a low speed, an adaptive robust integral sign error control algorithm based on friction compensation of dual nonlinear observer is proposed in this study. An improved LuGre friction model is established to describe the friction phenomena of the system. The dual nonlinear observer is designed to observe the internal frictional state of the model. A parameter adaptive law is designed to perform parameter estimation of structured uncertainty. Robust integral of the sign of the error (RISE) term is designed to overcome the frictional nonlinear disturbances. The friction nonlinearity and parameter uncertainty are compensated by feedforward compensation. The bounded stability of the proposed controller is proved by Lyapunov stability theory. As suggested by the experimental results, RMSE index decreased by 10.8% and the control precision was improved effectively by the proposed control algorithm at a low speed.

Model-free adaptive backstepping control for a class of uncertain nonlinear systems

Most nonlinear dynamic systems are characterized by uncertainties in models and parameters. Deterministic models cannot account for these uncertainties; therefore, model-based control using such models cannot provide the required performance. It is crucial to establish a practical concept of model-free control as a powerful alternative to model-based control. This paper develops a model-free adaptive backstepping control (MFABC) based on type 2 fuzzy Petri nets for a class of uncertain nonlinear systems. To provide valuable robustness to the MFABC structure, we have exploited the universal approximation property of type 2 fuzzy Petri nets to approximate the different nonlinear functions of the uncertain nonlinear system. The parameter adaptive laws are designed by the Lyapunov function; the stability and error convergence can be guaranteed. The simulation tests show that the proposed MFABC can provide good performance and high accuracy compared with the backstepping control. Moreover, the stability of this control scheme is affirmed.