مجله کنترل
سال هفدهم شماره 1 (بهار 1402)

In this study, by using the adaptive-robust nonlinear control approach, an innovative hybrid finite-time control framework is introduced to tackle the tracking problem for a great group of exoskeleton robots (in the presence of friction forces, two types of uncertainties, and unknown forces generated by the disabled person). According to the tracking aim, angular displacement of robot must exactly tend to required trajectories within the finite time. Firstly, a general nonlinear model is represented to characterize dynamical behavior of a typical exoskeleton robot possessing unknown physical constants. To complete this model, friction forces, modelling uncertainties, and unknown human torques (external disturbances) are considered. Two components of the exoskeleton model (unknown friction forces and parametric uncertainties) are rewritten as two detached linear regression forms. Secondly, a finite-time adaptive-robust nonlinear control structure is proposed to accomplish the aforementioned tracking aim and, as a result, the global finite-time stability is provided for the closed-loop exoskeleton robot. The mentioned finite-time nonlinear controllers are designed by combining the adaptation rules and the terminal sliding mode control strategy (along with new defined sliding manifolds). These adaptation rules estimates model physical constants, unknown coefficients of the friction forces, unknown human torques and upper bound of the Euclidean norm of the external disturbance vector. Mathematical analysis illustrates that time responses of the estimations precisely tend to constant values after the finite-time. Eventually, the combined finite-time control framework is simulated onto the 2-DOF exoskeleton numerically and obtained results reveal that the proposed control structure appropriately provides the finite-time tracking objective. the maximum power point tracking objective.

The 320 MW boiler of Bandar Abbas power plant has been subjected to parametric variations due to its long lifespan and numerous renovations, so a relatively accurate dynamic model is needed to retune the characteristics of its control system, simulate events, and evaluate and optimize its performance. Due to the lack of standard models for boilers with different structures, this paper deals with the modeling of this forced circulation subcritical boiler. As a result, a ninth order multivariable nonlinear state space model is developed using the physical modeling method. Due to the limitation of the measured variables and the lack of sufficient data with dynamic specifications suitable for identification algorithms, a computational procedure that uses only steady state measurements of the process is introduced to determine the unknown parameters of the model. The resulting model presents reasonable step responses and its ability in predicting the boiler outputs is confirmed using the operational data of the power plant during a sudden gas fuel pressure reduction event. Finally, the accuracy of its parameters is evaluated by performing sensitivity analysis.

In this paper, an observer-based controller is introduced for a class of nonlinear fractional order systems. First of all, considering a stable linear fractional order system known as the reference model, the controller is designed so that the closed loop system tracks the states of the reference system. In most systems, some states are unmeasurable or unreachable, so the controller must be designed based on the observer. The observer has been suggested in this research, using the well-known differential mean value theorem approach, converts the nonlinear error dynamics of the observer into linear and parameters-varying, so that the stability analysis is done simply using the Lyapunov quadratic function and linear matrix inequality. The stability analysis of the observer-based controller is also performed using the Lyapunov theorem in the following section. Finally, the efficiency and effectiveness of the proposed controller are shown through the simulation results of two nonlinear fractional order systems.

Waypoint guidance is one of the most common guidance methods for following a predetermined path in all types of robots. In this method, in addition to the starting point and the goal, several intermediate points are determined as waypoints, and the robot must consider each of the intermediate waypoints as a virtual destination and reach that point. Then Switch to the next waypoint and reach the final destination by passing through all of them. The aim of this paper is to design a multi-point guidance algorithm based on Haversine equations and spherical earth model for an Autonomous Underwater Vehicle. It will be possible to implement all kinds of research projects, sonar photography of the seabed and waterways, and monitoring of energy and telecommunication transmission lines through the design of various routes using the proposed method. The proposed multi-point guidance algorithm has been implemented in the field of a research Autonomous Underwater Vehicle and its efficiency has been evaluated.

Intelligent control of real control problems based on reinforcement learning often requires decision-making in a large or continuous state-action space. Since the number of adjustable parameters in discrete reinforcement learning has a direct relationship with cardinality of the state-action space of the problem, so in such problems, we are faced with the curse of dimensiality, low learning speed and low efficiency. The use of continuous reinforcement learning methods to overcome these problems have attracted many research interests. In this paper a novel Neural Reinforcement Learning (NRL) scheme is proposed. The presented method is model free and learning rate independent, and is obtained by combining Least Squares Policy Iteration (LSPI) with Radial Basis Functions (RBF) as a function approximator, and we call it "Neural Least Squares Policy Iteration" (NLSPI). In this method, by using the basis functions defined in the RBF neural network structure, we have provided a solution to solve the challenge of defining the state-action basis functions in LSPI. In order to validate the presented method, the performance of the proposed algorithm in solving two control problems has been compared with other methods. The overall results show the superiority of our method in learning the pseudo-optimal policy.

Development and implementation of advanced monitoring and control techniques requires measurement of variables which cannot be determined physically or difficult to measure. Soft sensors can be used as a relatively inexpensive alternative for hardware sensors as a suitable solution in the process industries by estimation of easy-to-measure variables using hard-to-measure variables. In this study, design of data-driven soft sensor based on state-dependent parameter modeling method by using local instrumental variable (LIV) have been presented to predict quality variables in Tennessee Eastman (TE) process. Unlike other soft sensor modeling methods, the state dependent parameter modeling method has simple structure and often requires fewer input variables. Moreover, state dependent modeling method using local instrumental variable can identify influencing variables which have been affected the target variables. The performance of identifying technique and proposed soft sensors has been investigated on Tennessee Eastman process. In the present study, LIV based Soft sensor models have been developed using MATLAB software to predict concentration of A and E components. The evaluation results of the proposed models on the test data set report that the root mean squared error (RMSE) for concentration of components A and E are 0.3191 and 0.0174, respectively. The proposed LIV model reduced prediction error (RMSE) for the concentration of component E by 98.18% and 97.6% as compared to Partial Least Squares (PLS) and Dynamic Inner Partial least squares (DiPLS) methods, respectively.

This paper focuses on the control of uncertain switched linear systems with asynchronous switching in the presence of disturbance using an observer with average dwell time. For this purpose, firstly, an appropriate model for the system is introduced, The maximum allowed time of non-synchronism, the minimum average dwell time and the necessary conditions for system stability are determined, using pseudo-Lyapunov functions. The novelty of this research is taking into account several undesirable parameters, i.e. asynchronous switching, uncertainty, disturbance and lack of access to several variables, simultaneously. Considering these factors together increase the complexity of the problem. Finally, the effectiveness of the proposed algorithm is shown through the implementation of the method on the system of a flying object in the MATLAB simulation environment.