Online Adaptive Identification Research Articles

Online adaptive identification of clutch torque transmissibility for the drivability consistency of high-performance production vehicles

Drivability in vehicles with a wet-type dual-clutch transmission (DCT) relies heavily on the precise torque transfer control of the clutch to efficiently distribute the engine torque to the wheels. However, the large variation in the clutch torque transmissibility caused by temperature and wear and the lack of torque sensors for the driveline in production vehicles are the two significant challenges that hinder accurate clutch control in wet DCT vehicles. Therefore, knowledge regarding clutch torque transmissibility is crucial for realizing the demanded clutch torque because it can help determine the required clutch pressure. Thus, in this study, an online adaptive identification algorithm for clutch torque characteristics is developed for high-performance vehicles with wet-type DCTs. The proposed algorithm estimates each clutch torque characteristic using the extended Kalman filter (EKF) based on a parameterizable model for the torque transmissibility curve, making it applicable to production vehicles. Further, the algorithm incorporates an adaptation law based on the Lyapunov stability theorem to compensate for uncertainties in the engine torque, thereby ensuring a robust identification performance regardless of the installed engine type. The estimation performance and effectiveness of the adaptive identification algorithm are experimentally validated using test vehicles with wet DCT.

Adaptive predictive control of a fan-ventilated tunnel greenhouse with evaporative cooling

In the present work, micro-climate control of a fan-ventilated tunnel greenhouse with evaporative cooling is investigated via simulation study. For this purpose, the adaptive generalized predictive control (AGPC) strategy is adopted to control temperature, relative humidity, and CO2 concentration in the greenhouse on extremely hot summer days. A dynamic model which integrates a distributed model of greenhouse micro-climate with a crop growth model is used for numerical simulation of the tunnel greenhouse. The controller performance is analyzed via numerical simulation. The relative gain array analysis confirms that greenhouse micro-climatic parameters are highly interactive. However, simulation results showed that AGPC can efficiently handle variables interaction and follow set-points despite actuator limitations and high outside temperature. Besides, by considering output constraints in applying AGPC, the control system can keep the humidity of the greenhouse in a specific range instead of in a particular set point. The calculation burden for the AGPC strategy is kept low without increasing prediction error by employing a linear model and on-line adaptive identification. The applied strategy can handle the uncertainty of the design model due to the model mismatch by on-line identification of model parameters using the recursive least square method.

Adaptive Identification of Nonlinear Friction and Load Torque for PMSM Drives via a Parallel-Observer-Based Network With Model Compensation

Acquiring accurate knowledge of nonlinear friction and load torque is of great interest for optimizing the control behavior of permanent-magnet synchronous motor (PMSM) drives. In this work, a friction-and-load adaptive identification scheme based on a parallel-observer-based network with model compensation (POBN-MC) is presented. The developed network possesses a parallel structure consisting of the designed two novel observers, which involve a gain-adaptation super-twisting load torque observer and a variable-learning-rate Adaline inertia observer. A non-empirical friction model is proposed to capture friction, forming the model compensation part that is exploited for correcting the torque input of the network. With a two-step mechanism derived from the POBN-MC, the proposed scheme attains the online adaptive identification of the friction and load torque in a manner that integrates both accuracy and simplicity. In the first step, an explicit mapping relationship between the nonlinear friction torque and the rotor speed is determined with the speed response triggered by the natural deceleration. The second step accomplishes the online observation with regard to friction-and-load information matching the real-time operating conditions. Sufficient theoretical analyses, as well as the validations of numerous simulations and experiments, are presented to support the suggested scheme.

Inferring Moore Machine for adaptive online Hybrid Automaton Identification

In the context of CPS modeling, this paper proposes a new method for online adaptive identification of hybrid systems. The method relies on a Moore machine identification process; A mapping between a hybrid automaton and a discrete event Moore machine is proposed, and a new online adaptive Moore machine identification method is proposed. A case study illustrates the proposed method.

Active Fault Tolerant Control System Design for Hydraulic Manipulator With Internal Leakage Faults Based on Disturbance Observer and Online Adaptive Identification

In this paper, an active fault-tolerant control (FTC) system design is proposed for an n-degree-of-freedom (n-DOF) hydraulic manipulator with internal leakage faults and mismatched/matched lumped disturbances. A pair of matched and mismatched disturbance observers (DOBs) is proposed to simultaneously estimate and compensate for the effects of matched/mismatched disturbances on the control system in healthy conditions. The fault detection is achieved when the estimated matched disturbance is larger than a threshold. After that, a novel control reconfiguration law is designed to switch from a normal controller to a fault-tolerant controller with an online identification algorithm based on an adaptive mechanism. The proposed active FTC guarantees the position tracking performance in not only single-fault but also simultaneous-faults conditions. Moreover, the problem of uniting disturbance-observer-based control for external disturbance and adaptive control for parametric uncertainty is solved in a novel approach. Simulation results are conducted in a two-degree-of-freedom hydraulic leg prototype, which verifies the effectiveness of the proposed method.

Adaptive Nonlinear Tire-Road Friction Force Estimation for Vehicular Systems Based on a Novel Differentiable Friction Model

A novel adaptive nonlinear observer-based parameter estimation scheme using a newly continuously differentiable friction model has been developed to estimate the tire-road friction force. The differentiable friction model is more flexible and suitable for online adaptive identification and control with the advantage of more explicit parameterizable form. Different from conventional estimation methods, the filtered regression estimation parameter is introduced in the novel adaptive laws, which can guarantee that both the observer error and parameter error exponentially converge to zero. Lyapunov theory has been used to prove the stability of the proposed methods. The effectiveness of the estimation algorithm is illustrated via a bus simulation model in the Trucksim software and simulation environment. The relatively accurate tire-road friction force was estimated just by the easily existing sensors signals wheel rotational speed and vehicle speed and the proposed method also displays strong robustness against bounded disturbances.

Adaptive Parameter Identification of an Accurate Nonlinear Dynamical Model for Marine Thrusters

Recently reported finite-dimensional nonlinear thruster models employing empirically determined lift–drag curves have been shown to accurately model both the transient and steady state response of marine thrusters. These reports have employed the standard off-line paradigm for model parameter identification: First, real-time sensor data (force, torque, fluid velocity) is logged in laboratory experiments. Second, the experimental data is analyzed with a least-square regression technique to complete a best fit for the model parameters. This paper reports an on-line technique for adaptive identification of model parameters for marine thrusters. The stability of the proposed technique is shown analytically. The performance of the on-line adaptive identification technique, evaluated with respect to an experimentally validated plant model, is shown to compare favorably to its off-line counterpart, but does not require the thrust and torque instrumentation required by conventional off-line least-squares parameter identification techniques.

Adaptive identification of dynamically positioned underwater robotic vehicles

This paper reports a stable online adaptive identification technique for the identification of finite-dimensional dynamical models of dynamically positioned underwater robotic vehicles. Proofs for the identifier's global stability, and for the input-to-state stability of this class of plants are reported. A direct comparison of the adaptive identification method to a conventional, off-line, least-squares method is reported. Using experimental data obtained with the Johns Hopkins University remotely operated underwater robotic vehicle, both methods are employed to identify decoupled, single-degree-of-freedom dynamical plant models. Performance of the resulting identified dynamical plant models is quantitatively compared to the experimentally observed motion of the actual vehicle.

Fuzzy Nonlinear Adaptive Internal Model Control (FNAIMC)

In this paper, it is proposed to apply Fuzzy Nonlinear Adaptive Internal Model Control (FNAIMC) for dealing with stable nonlinear systems. The developed strategy consists in including a Takagi-Sugeno fuzzy model of the plant within the control structure. In this way, the controller design simply results in a fuzzy model inversion. In this framework, the originality of the presented work lies in the use of a fuzzy model and its inversion. In order to be able to implement the control structure, two crucial points have to be solved in the considered fuzzy context, on the one hand the model identification, and on the other, the model inversion. A method is proposed to handle these two tasks when considering Takagi-Sugeno fuzzy formalism. As the structure of the fuzzy model (number of rules and rule premise) is assumed to be known, the fuzzy model identification is restricted to parameter identification (singleton consequence of the rules). Based on input-output process data, an online adaptive identification method is developed. The stability and convergence of the proposed recursive algorithm is guaranteed according to a Lyapunov synthesis. The model inversion strategy is viewed as a rulebase inversion problem. According to a linguistic interpretation of the model rulebase, each rule is directly inverted. By doing so, exact inversion is obtained and offset-free performances are ensured. In order to impose a desired regulation dynamic behavior and ensure the robustness of stability of the control system, the fuzzy controller is cascaded with a robustness filter. The potential of the proposed method is demonstrated by simulation examples.

High Speed · High Precision Tracking Control for Electronically Controlled Winding Machines

In this paper, we propose a new algorithm for on-line adaptive identification of dynamics of general motor servo systems including winding machines. This algorithm is then incorporated into a feedforward controller to realize the high speed tracking control for a prototype of winding machines. The parameter convergence as well as the stability of the closed-loop tracking system is proved theoretically, and simulations and experiments are conducted to study the feasibility of the adaptive indentification algorithm and the control performance of the feedback loop. It is found that the algorithm not only guarantees the parameter convergence, but also yields satisfactory transient responses of the identified parameters. This enables us to use the simple feedforward controller to realize high speed tracking control of servo systems.

Recursive relaxation identification of linear multivariable systems with its parallel algorithm

In this paper, we propose a new method for the identification of discrete-time linear multivariable systems. This method comprises three recursive computation schemes including two parameter estimate schemes for both state equation and observation equation, respectively, and the state estimate scheme via Kalman filtering. We call this method the recursive relaxation identification, abbreviated as RRI. Compared with the existing methods, the RRI algorithm has advantages of simplification, fast computation, and high efficiency, and is suitable for fast real-time on-line adaptive identification of linear multivariable systems.

ON SYSTEM IDENTIFICATION WITH AND WITHOUT CERTAINTY

Abstract Information-theoretic concepts are utilized to develop a procedure for identifying a parameter of a stochastic linear discrete time dynamic scalar system based on noisy linear measurements of the system's state. After various simplifying approximations, the derived error entropy identification algorithm reduces to an on-line adaptive identification algorithm that is similar in many respects to well-established identification techniques. Conditions under which the developed on-line adaptive algorithm identifies the system with certainty are presented. Using an error entropy estimation lower bound, which is independent of any estimation procedure, conditions for which identification cannot be made with certainty are also presented. Examples involving non-Gaussian statistics are used to illustrate the efficiency of the error entropy adaptive identification algorithm as well as to compare it with several other identification procedures.