Indirect Adaptive Control Research Articles

Indirect adaptive observer control (I-AOC) design for truck–trailer model based on T–S fuzzy system with unknown nonlinear function

Tracking is a crucial problem for nonlinear systems as it ensures stability and enables the system to accurately follow a desired reference signal. Using Takagi–Sugeno (T–S) fuzzy models, this paper addresses the problem of fuzzy observer and control design for a class of nonlinear systems. The Takagi–Sugeno (T–S) fuzzy models can represent nonlinear systems because it is a universal approximation. Firstly, the T–S fuzzy modeling is applied to get the dynamics of an observational system in order to estimate the unmeasurable states of an unknown nonlinear system. There are various kinds of nonlinear systems that can be modeled using T–S fuzzy systems by combining the input state variables linearly. Secondly, the T–S fuzzy systems can handle unknown states as well as parameters known to the indirect adaptive fuzzy observer. A simple feedback method is used to implement the proposed controller. As a result, the feedback linearization method allows for solving the singularity problem without using any additional algorithms. A fuzzy model representation of the observation system comprises parameters and a feedback gain. The Lyapunov function and Lipschitz conditions are used in constructing the adaptive law. This method is then illustrated by an illustrative example to prove its effectiveness with different kinds of nonlinear functions. A well-designed controller is effective and its performance index minimizes network utilization—this factor is particularly significant when applied to wireless communication systems.

A decision-making approach on control techniques for an inverted pendulum based on, neuro-fuzzy, indirect adaptive and PID controllers

The operation of an inverted pendulum and its respective type of control are affected by the change of the values of its internal parameters. Changes with high uncertainty result in responses with undesirable outputs. In this work, a comparison is presented for the control of an inverted pendulum to determine the operation and characteristics of three types of control systems: Neuro-Fuzzy Control (NFC), Indirect Adaptive Control (IAC) and a Proportional Integral Derivative control (PID). The study considers several indices such as stabilization time, rise time, mean square error, overshoots, convergence, computational load, error, mathematical requirements, and performance indices for control systems. To demonstrate its operation, the controls are implemented in hardware, one for the NFC and another for the IAC under an Arduino UNO platform. The results indicate that the NFC and IAC controls do not generate a transient or impulse response, only a small delay and the rise and stabilization time are minimal. While PID presents a transient response and overshoot, as well as a stabilization time to reach the steady state response.

Nonlinear dynamical system approximation and adaptive control based on hybrid‐feed‐forward recurrent neural network: Simulation and stability analysis

AbstractWe proposed an online identification and adaptive control framework for the nonlinear dynamical systems using a novel hybrid‐feed‐forward recurrent neural network (HFRNN) model. The HFRNN is a combination of a feed‐forward neural network (FFNN) and a local recurrent neural network (LRNN). We aim to leverage the simplicity of FFNN and the effectiveness of RNN to capture changing dynamics accurately and design an indirect adaptive control scheme. To derive the weights update equations, we have applied the gradient‐descent‐based Back‐Propagation (BP) technique, and the stability of the proposed learning strategy is proven using the Lyapunov stability principles. We also compared the proposed method's results with those of the Jordan network‐based controller (JNC) and the local recurrent network‐based controller (LRNC) in the simulation examples. The results demonstrate that our approach performs satisfactorily, even in the presence of disturbance signals.

Artificial neural network‐based adaptive control for nonlinear dynamical systems

SummaryThis research article presents an artificial neural network (ANN)‐based indirect adaptive control method for nonlinear dynamical systems. In this article, a modified Elman recurrent neural network (MERNN) is proposed as an identifier and controller for controlling nonlinear systems. The architecture of the proposed controller is a modified form of the existing Elman recurrent neural network. The parameter training of ANN‐based controllers is obtained by using the most popular optimization algorithm which is known as the back‐propagation algorithm. A comparative study includes Elman, Diagonal, Jordan, feed‐forward neural network (FFNN), and radial basis function network (RBFN)‐based controllers to compare with the proposed MERNN controller. To determine the controller's robustness, parameter variations, and disturbance signals have been considered. The performance analysis of the proposed controller is illustrated by two simulation examples. The simulation results reveal that MERNN can not only identify the unknown dynamics of the plant but also adaptively control it compared to the others.

Indirect Adaptive Control Using Neural Network and Discrete Extended Kalman Filter for Wheeled Mobile Robot

This paper presents a novel approach to address the challenges associated with the trajectory tracking control of wheeled mobile robots (WMRs). The proposed control approach is based on an indirect adaptive control PID using a neural network and discrete extended Kalman filter (IAPIDNN-DEKF). The proposed IAPIDNN-DEKF scheme uses the NN to identify the system Jacobian, which is used for tuning the PID gains using the stochastic gradient descent algorithm (SGD). The DEKF is proposed for state estimation (localization), and the NN adaptation improves the tracking error performance. By augmenting the state vector, the NN captures higher-order dynamics, enabling more accurate estimations, which improves trajectory tracking. Simulation studies in which a WMR is used in different scenarios are conducted to evaluate the effectiveness of the IAPIDNN-DEKF control. In order to demonstrate the effectiveness of the IAPIDNN-DEKF control, its performance is compared with direct adaptive NN (DA-NN) control, backstepping control (BSC) and an adaptive PID. On lemniscate, IAPIDNN-DEKF achieves RMSE values of 0.078769, 0.12086 and 0.1672. On sinusoidal trajectories, the method yields RMSE values of 0.01233, 0.015138 and 0.088707, and on sinusoidal with perturbation, RMSE values are 0.021495, 0.016504 and 0.090142 in x, y and θ, respectively. These results demonstrate the superior performance of IAPIDNN-DEKF for achieving accurate control and state estimation. The proposed IAPIDNN-DEKF offers advantages in terms of accurate estimation, adaptability to dynamic environments and computational efficiency. This research contributes to the advancement of robust control techniques for WMRs and showcases the potential of IAPIDNN-DEKF to enhance trajectory tracking and state estimation capabilities in real-world applications.

An improved indirect adaptive neural control performance based on MOPSO approach: An experimental validation via a transesterification reactor

This paper proposes an indirect adaptive control method based on recurrent neural networks. To achieve satisfactory closed-loop performances, a neural emulator (NE) and a neural controller (NC) adapting rates are established using the multiobjective particle swarm optimization (MOPSO) algorithm. The proposed MOPSO algorithm has been designed to minimize, simultaneously, two separated objective functions: the emulation and the tracking errors. The proposed approach guarantees that the NE tracks the system dynamics within a short time window. Consequently, it provides for the suggested control structure useful information to synthesize optimal adaptive rates of the NE and NC. To validate the effectiveness of the proposed MOPSO algorithm, a numerical example and an experimental validation on a chemical reactor are proposed.

THE METHOD OF CONSTRUCTING THE LAW OF THE SECURITY MANAGEMENT SYSTEM OF CRITICAL INFRASTRUCTURE OBJECTS IN THE CONDITIONS OF EXTERNAL UNCONTROLLED INFLUENCES

The intensive development of information technologies with a high degree of autonomy requires the development of autonomous management systems for optimal management. This issue is especially acute for critical infrastructure objects that have been proven to be affected by extreme external factors and impacts. It is proposed to consider the management process as management in a system with incomplete a priori information about the managed process. The process of managing which changes as information accumulates and is used to improve the operation of the entire security system in general. An identification approach to the synthesis of indirect adaptive control is used, which consists in specifying the model of the object during the control process. On the basis of the refined model, a safety control signal of the object is produced. The model of the object needs clarification because the system is constantly affected by external uncontrolled influences. The difference between the proposed method and the existing ones is that it offers a method of building a robust control system that allows to compensate for unknown disturbances with a certain accuracy in the required time. At the same time, by selecting the parameters of the closed system, it is possible to make the error and time values sufficiently small.

Surrogate indirect adaptive controller tuning based on polynomial response surface method and bioinspired optimization: Application to the brushless direct current motor controller

The increment of autonomous systems has stimulated the research of new controller tuning techniques to face the unpredictable disturbances and parametric uncertainties inherent in any autonomous system that affect its performance. The indirect adaptive controller tuning approach based on the general dynamic model (IACTA-GDM) and bioinspired optimization is one of the most successful elections facing parametric uncertainties and disturbances, which are intricate to handle by other controller tuning techniques. However, this controller tuning approach is limited by the complexity of the dynamic model due to the computational burden, restricting its application to relatively small systems or systems with slow responses where the tuning is updated at large time intervals. The present work proposes a novel surrogate indirect adaptive controller tuning approach based on the response surface method (SIACTA-RSM) to address computational burden limitations. The proposal is tested on the speed regulation controller of a brushless direct current motor, with the aim of reducing the speed regulation error and the control system’s power consumption. The closed-loop system performance and the required computational time obtained by the proposed SIACTA-RSM are compared to the ones of a well-established IACTA-GDM. The descriptive and inferential statistics, as well as graphical comparisons, show that the system performance obtained by the SIACTA-RSM proposal is as competitive as the IACTA-GDM approach, keeping a mean difference among the results by up to 3.18% while reducing the computational burden of IACTA-GDM by up to 90%. These outcomes show that the SIACTA-RSM proposal is a reliable alternative to overcome the computational burden limitations that affect the IACTA-GDM approach while maintaining competitive performance.

Metaheuristic adaptive control based on polynomial regression and differential evolution for robotic manipulators

Adaptive control of systems consists of the online adjustment of their control parameters to deal with parametric uncertainties and disturbances. Indirect Adaptive Control (IAC) of electromechanical systems and robotic manipulators based on metaheuristic optimization has been shown to be competitive compared to other classical and advanced control schemes. In this approach, the identification of system model parameters is stated as an optimization problem and then solved online by metaheuristics. Subsequently, the identified model is used in a second stage of online metaheuristic optimization to predict the system behavior and tune its control parameters. Therefore, this approach can be computationally expensive and unaffordable for systems with more complex dynamics whose models are just as complex. Due to the above, this work proposes an indirect adaptive control method for robotic manipulators based on differential evolution (DE) optimization, which aims to reduce the computational cost of the online identification stage by using Polynomial Regressors (PR) whose adjustment can be performed through a closed-form solution. The proposed method is tested in simulation for a 2 d.o.f. robotic manipulator and the results are compared against the original indirect adaptive control approach based on metaheuristic optimization and also a controller optimized offline. The post-hoc pair-wise Friedman tests confirm that the proposed approach provides competitive results compared to the original approach, with significant savings in computational resources.

An adaptive impedance control method for polishing system of an optical mirror processing robot

AbstractTo solve the constant contact force control problem between the end tool of a 5 degrees of freedom hybrid optical mirror processing robot and a workpiece, an adaptive impedance control method for the pneumatic servo-polishing system of the robot is designed. Firstly, the pneumatic servo-polishing control system at the end of the robot is set up. Secondly, the impedance control method for contact force is investigated based on the mathematical model of the pneumatic servo-polishing control system. Additionally, the causes of steady-state error of impedance control are analyzed theoretically, and the calculation method for steady-state error of impedance control is deduced. Finally, an indirect adaptive impedance controller based on Lyapunov Stability Principle is developed to estimate the environmental stiffness and position online, so as to reduce steady-state error and realize the tracking of polishing contact force. The simulation and experimental results suggest that the adaptive impedance control method not only recognizes that the contact force of the robot is relatively constant during the polishing process but also has high control accuracy for the force, fast-tracking response for the abrupt force, and considerable adaptability to the variable environmental stiffness.

Optimized indirect adaptive robust control for a class of uncertain nonlinear systems with time‐varying parameters

AbstractThis paper concerns high‐accuracy tracking control and parameter estimation for semi‐strict feedback systems with unknown time‐varying parameters. The unsmooth and discontinuous parameters and matrix parameters are all taken into consideration. An optimized indirect adaptive controller based on Z‐swapping and improved least‐square algorithm is developed to handle parametric and unmolded uncertainties simultaneously. Each design index of this adaptive law can be adjusted freely according to the order of the magnitude of unknown parameters and state variables so that the coupling of the estimating process can be avoided. The Lyapunov‐based stability analysis illustrates that the proposed method can obtain a prescribed estimating result as well as transient and final tracking performance. Simulations based on two common systems verify the outstanding performance of the proposed method.

Robust adaptive quadrotor position tracking control for uncertain and fault conditions

The combination of nonlinear indirect and direct adaptive control with an active fault-tolerant framework is proposed in this paper for quadrotor position tracking control under uncertain and fault conditions. An inner loop direct model reference adaptive controller generates the required force while moving along the reference trajectory, while nonlinear indirect adaptive controller maintains the required attitude angles. Furthermore, for uncertain conditions, our proposed framework provides significantly stable characteristics. Otherwise, when a fault occurs at actuators or sensors, the quadrotor vehicle cannot guarantee global asymptotic stability. This study contributes to an active fault-tolerant control strategy for solving the complex position tracking problem using the adaptive two-stage Kalman filter (ATSKF). Based on the fault information, a fault compensation term is added to the control law to improve convergence and system robustness in the presence of uncertain and fault conditions. Finally, simulation results show satisfactory performance for quadrotor vehicle position tracking, even with actuator faults and uncertainties.

Integrated Controller for Divert Thruster Flight Propulsion Control Systems

Future generation missile calls for development of fast response propulsion based control systems to achieve hit-to-kill miss distance against highly manoeuvrable targets covering high-endo to exo-atmosphere regions. Control systems based on solid propulsion are developed connecting multiple pintle nozzles and control thrust vector is generated with distribution hot gasses in pitch/yaw directions. Differential distribution of hot gasses among multiple nozzles is complex due to time varying coupled non-linear chamber pressure dynamics with propellant combustion and nozzles’ flow phenomena and methods based on linear control may not ensure stability in wide operating conditions. There is need to develop "Unified Controller" based on non-linear formulation to ensure control stability in wide operating conditions of various mission applications, with advanced features for new generation missiles to counter various futuristic targets, and achieve hit-to-kill capabilities following integrated approach in order to improve the total system performance. Non-linear algorithms proposed in the recent literature do not exploit synergism between plant and actuation systems dynamics. Present paper presents novel non-linear feedback linearization technique, Dynamic Inversion, a relatively straightforward approach in Integrated Control Framework (ICF) implementing inherent time scale separation between solid motor plant and hot gas flow control valve actuation system. To ensure stability in various operating conditions and assure robust performance in the presence of parameter inaccuracies, controller is augmented with model-following neuro-adaptive design. Prototype systems: Thrust Controlled Motor (TCM) and Divert Thruster Flight Propulsion Control Systems (DTFPCS) are developed to demostrate/validate "Unified Controller" for missile control system. Closed loop control of chamber pressure is demonstrated with single pintle nozzle system and control thrust vector generation with multiple nozzle systems. Model update takes place adaptively online and unlike a typical indirect adaptive control approach, there is no need to update the individual parameters explicitly. Instead the inaccuracy in the system output dynamics is captured directly and then used in modifying the control. Based on present work, missilecontrol systems for various applications can be developed with limited controller changes and thereby minimizing number of developmental tests and cost.

Double Dynamic Linearization-Based Higher Order Indirect Adaptive Iterative Learning Control.

In this article, a higher order indirect adaptive iterative learning control (HO-iAILC) scheme is developed for nonlinear nonaffine systems. The inner loop adopts a P -type controller whose set-point is updated iteratively by learning from the iterations. To this end, an ideal nonlinear learning control law is designed in the outer loop. It is then transferred to a linear parametric-learning controller with a corresponding parameter estimation law by introducing an iterative dynamic linearization (IDL) method. This IDL method is also used to gain an iterative linear data model of the nonlinear system. A parameter iterative updating algorithm is utilized for estimating the unknown parameters of the obtained linear data model. Finally, the HO-iAILC is presented that utilizes additional error information to improve the control performance and employs two iterative adaptive mechanisms to deal with uncertainties. The convergence of the proposed HO-iAILC scheme is proved by using two basic mathematical tools, namely: 1) contraction mapping and 2) mathematical induction. Simulation studies are conducted for the verification of the theoretical results.

A composite adaptive robust control for pneumatic servo systems with time-varying inertia

This article proposes a composite adaptive robust control for a pneumatic servo system with time-varying inertia to achieve a good tracking performance. The proposed design not only preserves the merits of both direct adaptive robust control and indirect adaptive robust control but also obtains a better performance of the working condition with time-varying inertias. Firstly, prescribed tracking performance is guaranteed without knowing the bounds of parametric and non-parametric uncertainties by leveraging a discontinuous projection and deterministic robust control technique. And a nonlinear tracking differentiator is used to obviate differential signal in backstepping. Secondly, the online parametric estimation algorithm is developed by intelligently integrating tracking error dynamics and physical plant dynamics. By effectively using the information of both tracking error and prediction error, a high adaptation gain can be used for achieving a faster parameter convergence and smaller tracking error. Moreover, exponential convergence of both tracking error and prediction error can be obtained under certain conditions. Lastly, comparative experiments on different conditions are carried out for a single-rod pneumatic cylinder driven by a proportional directional valve to demonstrate the superiority of the proposed method.

Enhanced P-Type Control: Indirect Adaptive Learning From Set-Point Updates

In this article, an indirect adaptive iterative learning control (iAILC) scheme is proposed for both linear and nonlinear systems to enhance the P-type controller by learning from set points. An adaptive mechanism is included in the iAILC method to regulate the learning gain using input–output measurements in real time. An iAILC method is first designed for linear systems to improve control performance by fully utilizing model information if such a linear model is known exactly. Then, an iterative dynamic linearization (IDL)-based iAILC is proposed for a nonlinear nonaffine system, whose model is completely unknown. The IDL technique is employed to deal with the strong nonlinearity and nonaffine structure of the systems such that a linear data model can be attained consequently for the algorithm design and performance analysis. The convergence of the developed iAILC schemes is proved rigorously, where contraction mapping, two-dimensional (2-D) Roesser’s system theory, and mathematical induction are employed as the basic analysis tools. Simulation studies are provided to verify the developed theoretical results.

Indirect adaptive neural tracking control of USVs under injection and deception attacks

This paper investigates the tracking control issue of unmanned surface vessels (USVs) under internal/external uncertainties as well as injection and deception attacks, and proposes a novel adaptive neural network tracking control solution. In this work, the backstepping design framework is used to solve the control design issue. To resolve the time-varying gain caused by the injection and deception attacks, indirect adaptive neural and single-parameter learning approaches are employed. In the kinematic channel, internal and external uncertainties as well as the additional terms caused by the injection and deception attacks are regarded as heterogeneous uncertainties. And then, the heterogeneous uncertain term is transformed into a linearly parameterized form with only one parameter with the aid of the neural network technique and single-parameter learning approach. Furthermore, an adaptive law is constructed to update the unknown parameter, and an indirect adaptive neural learning approach is developed. Finally, a novel indirect adaptive neural tracking control solution is proposed to implement the suppression of heterogeneous uncertainties including internal and external uncertainties as well as attacks. The theoretical analysis indicates that all signals in the closed-loop tracking control system of USVs are bounded under the presented scheme. Simulation results demonstrate the effectiveness of the proposed scheme.

Indirect adaptive fuzzy finite time synergetic control for power systems

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

MIMO fuzzy adaptive control systems based on fuzzy semi-tensor product

<abstract><p>Based on fuzzy semi-tensor product (STP) algorithms and fuzzy relation matrix (FRM) models, the design of an adaptive fuzzy controller was proposed in this paper for the multivariable nonlinear systems with uncertainty. The controlled multi-input-and-multi-output (MIMO) plants were expressed and processed first by FRM models and fuzzy STP operations, and then the indirect adaptive fuzzy control laws were designed. The tracking property of the FRM models was proved for the control objective of MIMO systems. The effectiveness of the novel matrix expression was verified by simulations of the tracking control on a two-link rigid robot manipulator. Results in simulation tests show that the proposed design of adaptive FRM control is efficient for nonlinear multivariables. Therefore, the proposed indirect fuzzy adaptive controllers can be extended to general matrix expression for MIMO nonlinear systems with fuzzy STP algorithms and FRM models and online approximate unknown parameters, according to required accuracy.</p></abstract>

A New Indirect Adaptive Neural Control for Nonlinear Systems: A Real Validation on a Chemical Process

In the present work, an indirect adaptive neural control method for nonlinear systems having unknown dynamics is proposed. The proposed control architecture is composed by a neural emulator (NE) and a neural controller (NC) where a new decoupled variable learning rates (VLRs) combined with Taylor development (TD) are used to train the NE and the NC. The developed VLRs mixed with the TD (TDVLRs) ensure a quick adaptation of neural networks parameters guaranteeing a faster output convergence and reducing the tracking error. The effectiveness of the proposed TDVLRs is illustrated by simulation with a nonlinear dynamic system. In order to validate simulation results, an application on a transesterification reactors is, also, presented.