Indirect Adaptive Control Approach Research Articles

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.

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.

Experimental investigation of PWM laser standoff distance control for power diode based LBM

Laser Beam Cutting (LBC) is a non-traditional excellent material processing technique preferred for robust, versatile and durable materials like leather, as it offers various benefits such as excellent accuracy and minimal tool wear due to the lack of contact between the specimen and the nozzle. In this investigation, power diode laser was preferred for leather cutting because of its longevity, monochromaticity and tunability. Pulse Width Modulation (PWM) is an input parameter that is tuned to control the output optical power of the diode laser in this study. There is no viable method availablein the field of direct adaptive control for PWM in the application of diode laser leather cutting. Hence an attempt has been proposed in this present study to control and investigate the machinability of leather by implementing the diode based LBC technology and standoff distance (SOD) control using VL6180X time of flight sensor, with indirect adaptive PWM control approach. The cutting trials were conducted on chrome tanned goat leather of thickness 0.8 mm and also this investigation reports the carbonization values and kerf width during leather cutting. It was identified that the closed loop PWM control could reduce the carbonization from 34.645 % to 30.424 % with SOD control of 20 mm. It was observed that PWM based control can enhance the machining process in an effective way.

Uncalibrated image-based visual servoing approach for translational trajectory tracking with an uncertain robot manipulator

This work presents an adaptive image-based visual servoing approach for uncertain robot manipulators using an eye-to-hand camera configuration, which does not require any calibration procedure and image velocity measurement. A monocular camera provides visual feedback for the robot’s joint control, allowing successful tracking of the translational trajectory, prescribed in image space, for a given target object attached to the robot end-effector. As our contribution, an indirect adaptive control approach is proposed to deal with the uncertain parameters of the camera–robot system and any camera misalignment concerning the robot frame. The indirect adaptive visual servoing approach is then combined with a direct adaptive motion controller using a cascade control framework to consider the uncertain robot dynamics in our stand-alone robot vision system. Lastly, an adaptive model-based state observer is designed to avoid the need for measuring the image feature velocity in the interconnected control loops. The Lyapunov stability theory and the passivity paradigm are synergized to demonstrate the stability properties of the overall closed-loop system. Experimental results, obtained with a 4-DoF robot manipulator carrying out translational trajectory tracking tasks using a commercial webcam, illustrate the performance and effectiveness of the proposed adaptive control methodology.

Indirect adaptive model predictive control and its application to uncertain linear systems

SummaryWe consider constrained systems that are represented by uncertain models with unknown constant or slowly varying parameters. We propose an indirect adaptive model predictive control (IAMPC) approach where the prediction model can be adjusted during controller operation by a separately designed estimator that satisfies only a minimal set of assumptions. The controller guarantees constraint satisfaction despite the uncertainty in the parameters by means of a robust control invariant set, and input‐to‐state stability with respect to the estimation error by means of an appropriately designed method for adjusting the IAMPC prediction model and cost function based on the evolution of the parameter estimate. The controller has minimal computational overhead with respect to a nominal MPC and for the special case of uncertain linear systems, we obtain a constructive design procedure for the IAMPC which only solves quadratic programs during closed‐loop control.

Adaptive Path Following for a Nonholonomic Mobile Manipulator

Abstract We investigate an adaptive path following problem for a nonholonomic mobile manipulator system and closed planar curves. As opposed to adapting to uncertain or unknown dynamics in the plant, we apply an adaptation approach with respect to an unknown path. First, we present a solution to the non-adaptive path following problem using the concept of a path following output. Then, we use an indirect adaptive control approach to design path following controllers for a feasible class of strictly convex paths.

An Indirect Adaptive Control Approach to Image Based Visual Servoing for Translational Trajectory Tracking

Abstract In this work, it is considered an image based visual servoing control problem, for uncertain robot manipulators. Visual feedback is provided by a fixed monocular camera with uncertain parameters, for the purpose of tracking translational trajectories of a spherical target, both the trajectory on image plane and depth. Based on a cascade structure, the proposed adaptive visual servoing is combined with an adaptive motion control strategy. Stability and passivity properties are analyzed with the Lyapunov method. Simulation results illustrate and highlight performance of the proposed controller.

A note on the ‘task-space control of robots using an adaptive Taylor series uncertainty estimator’

This note points out some errors in the above paper that presents an indirect adaptive Taylor series control approach for the asymptotic tracking task-space control of electrically driven robots. In the proposed approach, Taylor series estimator is designed to approximate the lumped uncertainties (a real-valued function of several variables) in a decentralised form. However, in this note, it is proven that according to Stone–Weierstrass theorem, such an approximation is not technically true, and consequently, the obtained results in the above-mentioned paper are not correct.

Comments on ``Robust control of electrically driven robots using adaptive uncertainty estimation''

This note points out some errors in the above paper, which presents an indirect adaptive Taylor series control approach toward electrically driven robots in the joint space. In the proposed approach, the first order of Taylor series estimator is designed to approximate the lumped uncertainties (a real-valued function of several variables) in a decentralized form. However, in this note, it is proven that according to Stone-Weierstrass theorem, such an approximation is not technically correct, and consequently, the obtained results in the above-mentioned paper are not valid.

Impedance control of secondary regulated hydraulic crane in the water entry phase

During the water entry phase in heavy lift marine operations, the involved crane system must satisfy stringent safety requirements. A hydraulic crane driven by a secondary controlled unit that has the advantage of high dynamic behavior and energy efficiency is investigated in this paper. The detailed models of the crane system and the offshore operating conditions are established. To guarantee functionality of the crane system under harsh sea conditions, a cascade control structure is constructed. In the outer loop, an extended state observer is used to estimate the unmeasured payload states. Then, an impedance controller is designed to realize trajectory tracking and impedance regulation of the payload. In the inner loop, the reference trajectory is provided by the impedance controller. An indirect adaptive robust control approach is applied to maximize the achievable tracking performance and to obtain accurate parameter estimates of the secondary controlled unit in the presence of parametric uncertainties and unknown disturbances. The proposed controller can lower a payload to a desired position, and keep the values of the hydrodynamic forces and wire tension within acceptable bounds at the same time. Comparative simulation results verify the effectiveness of the proposed control method.

Stability Analysis of Indirect Binary Model Reference Adaptive Controller for Plants with Relative Degree One

This paper presents the Lyapunov-based stability analysis of the Indirect Binary Model Reference Adaptive Controller (IB-MRAC) for relative-degree-one systems. The motivation for indirect adaptive control approaches relies on their capabilities of providing an easier and more intuitive controller design. IB-MRAC is a mixed controller, acting as an Indirect MRAC or as an Indirect Variable Structure MRAC depending on a design parameter. The main result herein described states that the overall system error tends exponentially fast to some small residual set.

Design and implementation of adaptive PI control schemes for web tension control in roll-to-roll (R2R) manufacturing

In this paper, two adaptive Proportional-Integral (PI) control schemes are designed and discussed for control of web tension in Roll-to-Roll (R2R) manufacturing systems. R2R systems are used to transport continuous materials (called webs) on rollers from the unwind roll to the rewind roll. Maintaining web tension at the desired value is critical to many R2R processes such as printing, coating, lamination, etc. Existing fixed gain PI tension control schemes currently used in industrial practice require extensive tuning and do not provide the desired performance for changing operating conditions and material properties. The first adaptive PI scheme utilizes the model reference approach where the controller gains are estimated based on matching of the actual closed-loop tension control systems with an appropriately chosen reference model. The second adaptive PI scheme utilizes the indirect adaptive control approach together with relay feedback technique to automatically initialize the adaptive PI gains. These adaptive tension control schemes can be implemented on any R2R manufacturing system. The key features of the two adaptive schemes is that their designs are simple for practicing engineers, easy to implement in real-time, and automate the tuning process. Extensive experiments are conducted on a large experimental R2R machine which mimics many features of an industrial R2R machine. These experiments include trials with two different polymer webs and a variety of operating conditions. Implementation guidelines are provided for both adaptive schemes. Experimental results comparing the two adaptive schemes and a fixed gain PI tension control scheme used in industrial practice are provided and discussed.

Two-mode Indirect Adaptive Control Approach for the Synchronization of Uncertain Chaotic Systems by the Use of a Hierarchical Interval Type-2 Fuzzy Neural Network

Two-mode adaptive controllers have two phases of operation: a learning phase and an operation phase. This paper presents a two-mode indirect adaptive control approach for the synchronization of chaotic systems, using a hierarchical interval type-2 fuzzy neural network (HT2FNN). Its contribution to the existing literature is the adaptation laws derived for the parameters of the membership functions, based on Lyapunov stability analysis. Since, in hierarchical case, each T2FNN has only two inputs, the computing of the derivatives is much simpler than the case in classical interval type-2 FNN. Moreover, a novel approach is presented for the compensation of the approximation error. The tuning of the parameters of the membership functions (MF) and the use of an interval type-2 FNN ensures that the estimation error is very small so that it can be negligible. Furthermore, the number of MF required is seen to be less than that needed with type-1 fuzzy sets. The simulation results confirm the efficacy of the proposed scheme in the synchronization of a uncertain nonidentical chaotic systems.

Direct adaptive actuator failure compensation control: a tutorial

A resilient control system is expected to have the capacity to restore the desired system stability and tracking performance in the presence of uncertain system faults such as actuator failures. While redundant actuators are used for actuator failure accommodation, uncertain actuator failures, whose failure time, pattern, and values may be unknown, can bring new challenges to feedback control design as such uncertain failures can introduce large structural, parametric, and actuation uncertainties. Two technical issues are associated with using redundant actuators: how redundant actuators should be coordinated for effective failure compensation control, and how a feedback control law should be adaptively designed to compensate uncertain actuator failures. In this paper, we present a tutorial on direct adaptive failure compensation-based solutions to these issues for different types of control systems: state tracking using state feedback, output tracking using state feedback or output feedback, for linear, non-linear, and multi-input multi-output systems. We give an overview on such adaptive actuator failure compensation designs which have special capacities to effectively use actuation redundancy to handle uncertain actuator failures, using either direct or indirect adaptive control approaches for direct adaptive actuator failure compensation without explicit failure detection, for fast and effective failure accommodation.

VGT and EGR Control of Common-Rail Diesel Engines Using an Artificial Neural Network

In diesel engines, variable geometry turbocharger (VGT) and exhaust gas recirculation (EGR) systems are used to increase engine specific power and reduce NOx emissions, respectively. Because the dynamics of both the VGT and EGR are highly nonlinear and coupled to each other, better performance may be attained by substituting nonlinear multiple input, multiple output (MIMO) controllers for the existing conventional lookup table-based linear controllers. This paper presents a coordinated VGT/EGR control system for common-rail direct injection diesel engines. The objective of the control system is to track target mass air flow and target intake manifold pressure by adjusting the EGR and VGT actuator positions. We designed a nonlinear MIMO control system using a neural control scheme that adopts an indirect adaptive control approach. The neural control system is comprised of a neural network identifier, which mimics the target air system, and a neural network controller, which calculates the actuator positions. The proposed control system has been validated with engine experiments under transient operating conditions. It was demonstrated from experimental results that the proposed control system shows improved target value tracking performance over conventional VGT/EGR control system.

Mass air flow control of common-rail diesel engines using an artificial neural network

This paper presents a non-linear control system design for managing the mass flow rate in a common-rail direct-injection diesel engine. In diesel engines, exhaust gas recirculation systems are used to reduce the nitrogen oxide emissions. Since an exhaust gas recirculation system has highly non-linear characteristics and is coupled with a variable-geometry turbocharger, it requires a non-linear control system, which can substitute for an existing conventional lookup-table-based controller, in order to secure a control performance in the transient operating regime. In this project, the objective of the control system is to track the target mass air flow by adjusting the exhaust gas recirculation valve lift. In order to accomplish this objective, a non-linear control system is proposed that adopts a neural-network-based control scheme and an indirect adaptive control approach. The neural adaptive controller determines the position of the exhaust gas recirculation valve lift for tracking target values, based on measured values. An error back-propagation algorithm for online training of neural networks is employed. The proposed control system was validated with engine experiments under transient operating conditions. It was demonstrated from experimental results that the proposed control system shows an improved target-value-tracking performance, when compared with conventional mass air flow controllers.

Adaptive Backstepping Fuzzy Control Based on Type‐2 Fuzzy System

A novel indirect adaptive backstepping control approach based on type‐2 fuzzy system is developed for a class of nonlinear systems. This approach adopts type‐2 fuzzy system instead of type‐1 fuzzy system to approximate the unknown functions. With type‐reduction, the type‐2 fuzzy system is replaced by the average of two type‐1 fuzzy systems. Ultimately, the adaptive laws, by means of backstepping design technique, will be developed to adjust the parameters to attenuate the approximation error and external disturbance. According to stability theorem, it is proved that the proposed Type‐2 Adaptive Backstepping Fuzzy Control (T2ABFC) approach can guarantee global stability of closed‐loop system and ensure all the signals bounded. Compared with existing Type‐1 Adaptive Backstepping Fuzzy Control (T1ABFC), as the advantages of handling numerical and linguistic uncertainties, T2ABFC has the potential to produce better performances in many respects, such as stability and resistance to disturbances. Finally, a biological simulation example is provided to illustrate the feasibility of control scheme proposed in this paper.

Combined direct and indirect adaptive control of robot manipulators using multiple models

A novel methodology is proposed for the adaptive control of rigid robotic manipulators. The proposed method utilizes multiple adaptive models for the identification and control of the manipulator. The present study is an extension of our previous work which utilized an indirect adaptive control approach with multiple models for better transient performance. The proposed scheme uses a composite approach where both prediction and tracking errors are used in a combined direct and indirect adaptive control framework. Simulation results are given to demonstrate the efficient use of the methodology.

A fuzzy controller with evolving structure

An approach to on-line design of fuzzy controllers of Takagi–Sugeno type with gradually evolving structure is treated in the paper. Fuzzy rules, representing the structure of the controller are generated based on data collected during the process of control using newly introduced technique for on-line identification of Takagi–Sugeno systems. The output of the plant under control (including its dynamic) and the respective control signal has been memorised and stored in on-line mode. These data has been used to train in a non-iterative, recursive way the fuzzy controller. The indirect adaptive control approach has been used in combination with the novel on-line identification technique. This approach exploits the quasi-linear nature of Takagi-Sugeno models and builds-up the control rule-base structure and adapts it in on-line mode with recursive, non-iterative learning. The method is illustrated with an example from air-conditioning systems, though it has wider potential applications.

Identification and control of dynamic systems using recurrent fuzzy neural networks

Proposes a recurrent fuzzy neural network (RFNN) structure for identifying and controlling nonlinear dynamic systems. The RFNN is inherently a recurrent multilayered connectionist network for realizing fuzzy inference using dynamic fuzzy rules. Temporal relations are embedded in the network by adding feedback connections in the second layer of the fuzzy neural network (FNN). The RFNN expands the basic ability of the FNN to cope with temporal problems. In addition, results for the FNN-fuzzy inference engine, universal approximation, and convergence analysis are extended to the RFNN. For the control problem, we present the direct and indirect adaptive control approaches using the RFNN. Based on the Lyapunov stability approach, rigorous proofs are presented to guarantee the convergence of the RFNN by choosing appropriate learning rates. Finally, the RFNN is applied in several simulations (time series prediction, identification, and control of nonlinear systems). The results confirm the effectiveness of the RFNN.