Hybrid Adaptive Control Research Articles

Decentralized Adaptive Coordinated Control of Multiple Robot Arms Handling One Object

This paper presents an adaptive decentralized coordinated control method for multiple robot arms grasping a common object. In the proposed controller, the dynamic parameters of both object and robot arms are estimated adaptively. The desired motions of the robot arms are generated by an estimated object reference model and each robot controller works independently as hybrid adaptive controller. The asymptotic stability of position and internal force of the object is proven by the Lyapunov-Like Lemma. Experimental comparisons between adaptive control with force sensor and one without force sensor for the two robot arms grasping a common object are shown.

Hybrid adaptive control for non‐linear uncertain impulsive dynamical systems

AbstractA direct hybrid adaptive control framework for non‐linear uncertain hybrid dynamical systems is developed. The proposed hybrid adaptive control framework is Lyapunov‐based and guarantees partial asymptotic stability of the closed‐loop hybrid system; that is, asymptotic stability with respect to part of the closed‐loop system states associated with the hybrid plant states. Furthermore, hybrid adaptive controllers guaranteeing attraction of the closed‐loop system plant states are also developed. Finally, two numerical examples are provided to demonstrate the efficacy of the proposed hybrid adaptive stabilization approach. Copyright © 2004 John Wiley & Sons, Ltd.

Adaptive Hybrid Control for Linear Piezoelectric Ceramic Motor Drive Using Diagonal Recurrent CMAC Network

This paper presents an adaptive hybrid control system using a diagonal recurrent cerebellar-model-articulation-computer (DRCMAC) network to control a linear piezoelectric ceramic motor (LPCM) driven by a two-inductance two-capacitance (LLCC) resonant inverter. Since the dynamic characteristics and motor parameters of the LPCM are highly nonlinear and time varying, an adaptive hybrid control system is therefore designed based on a hypothetical dynamic model to achieve high-precision position control. The architecture of DRCMAC network is a modified model of a cerebellar-model-articulation-computer (CMAC) network to attain a small number of receptive-fields. The novel idea of this study is that it employs the concept of diagonal recurrent neural network (DRNN) in order to capture the system dynamics and convert the static CMAC into a dynamic one. This adaptive hybrid control system is composed of two parts. One is a DRCMAC network controller that is used to mimic a conventional computed torque control law due to unknown system dynamics, and the other is a compensated controller with bound estimation algorithm that is utilized to recover the residual approximation error for guaranteeing the stable characteristic. The effectiveness of the proposed driving circuit and control system is verified with hardware experiments under the occurrence of uncertainties. In addition, the advantages of the proposed control scheme are indicated in comparison with a traditional integral-proportional (IP) position control system.

Hybrid adaptive control laws solving a path following problem for non-holonomic mobile manipulators

In this paper a general solution to the path following problem for mobile manipulators with non-holonomic mobile platform has been presented. New proposed control algorithms — for mobile manipulators with fully known dynamics or with parametric uncertainty in the dynamics — take into considerations the kinematics as well as the dynamics of the non-holonomic mobile manipulator. The convergence of the control algorithms is proved using the LaSalle's invariance principle.

Force control in a pneumatic system using hybrid adaptive neuro-fuzzy model reference control

The dynamic model of a force control system depends not only on the actuator but also on the manipulated object. Generally the object dynamics are unknown and frequently change, and it therefore is difficult to identify the actual dynamics of the system. Designing a nonadaptive controller to perform well under unknown and variant conditions is not feasible. To enhance the controller performance for the pneumatic system, in this paper, a hybrid adaptive neuro-fuzzy model reference is proposed. The concept of multimode switching is applied to activate either a ‘bang–bang’ controller or an adaptive neuro-fuzzy model reference controller ( ANFMRC). Bang–bang control is applied when the actual output is far away from set point, which results in high error value. In this mode, fast tracking of the output is required. ANFMRC is applied in medium and small error ranges to perform a good response. In this paper, two types of controller; ANFMRC and hybrid ANFMRC are investigated. Simulation and experiment of a force controlled pneumatic system is investigated to evaluate the efficiency of the algorithm.

Frequency-weighted adaptive control for simultaneous precision positioning and vibrationsuppression of smart structures

This paper focuses on a frequency-weighted hybrid adaptive control with application tosimultaneous precision positioning and vibration suppression of smart composite structures.Following the introduction of the structure and materials system of an active compositepanel (ACP) with a surface-mounted and two embedded piezoelectric ceramic patches, thesensor selection for the purpose of precision positioning or vibration control is discussed,and the function assignment of a laser displacement sensor as well as the embeddedpiezoelectric sensor is determined for the ACP. The frequency-weighted hybrid adaptivecontrol approach is then developed. The approach consists of an adaptive feedforwardposition controller for precision positioning, an adaptive feedforward vibrationcontroller for vibration suppression as well as an adaptive feedback controller for bothprecision positioning and vibration suppression. The frequency-weighted filtersintroduced in the approach realize the signal fusion of the two sensors. Finally,experiments are also performed for harmonic disturbances at the first two naturalfrequencies of the ACP and a random disturbance for both cases of with/without theadaptive feedforward vibration controller, demonstrating that the frequency-weightedhybrid adaptive control approach can provide satisfactory precision positioningand vibration suppression in both cases, and that better position accuracy canbe achieved if the adaptive feedforward vibration controller is also employed.

Adaptive fuzzy decentralized control for a class of large-scale nonlinear systems.

In this paper, direct and indirect adaptive output-feedback fuzzy decentralized controllers for a class of uncertain large-scale nonlinear systems are developed. The proposed controllers do not need the availability of the state variables. By designing the state observer, the adaptive fuzzy systems, which are used to model the unknown functions, can be constructed using the state estimations, and a new hybrid adaptive fuzzy control methodology is proposed by combining the adaptive fuzzy systems with H infinity control and the sliding mode control techniques. Based on Lyapunov stability theorem, the stability of the closed-loop systems can be verified. Moreover, the proposed overall control schemes guarantee that all the signals involved are bounded and achieve the H infinity-tracking performance. To demonstrate the effectiveness of the proposed methods, simulation results are illustrated in this paper.

Observer-based adaptive fuzzy control for SISO nonlinear systems

The observer-based indirect and direct adaptive fuzzy controllers are developed for a class of SISO uncertain nonlinear systems. The proposed approaches do not need the availability of the state variables. By designing the state observer, the adaptive fuzzy systems, which are used to model the unknown functions, can be constructed using the state estimations. Thus, a new hybrid adaptive fuzzy control method is proposed by combining the above adaptive fuzzy system with the H ∞ control technique. Based on Lyapunov stability theorem, the proposed adaptive fuzzy control system can guarantee the stability of the whole closed-loop systems and obtain good tracking performance as well. The proposed methods are applied to an inverted pendulum system and a chaotic system and achieve satisfactory simulation results.

Adaptive decentralized coordinated control of multiple robot arms

This paper presents an adaptive decentralized coordinated control method for multiple robot arms grasping a common object. In the proposed controller, the dynamic parameters of both object and robot arms are estImated adaptively. The desired motions of the robot arms are generated by an estimated object reference model and each robot controller works independently as hybrid adaptive controller. The asymtotic stability of position and internal force of the object is proven by the Lyapunov-Like Lemma. Expenmental result for the two robot arms grasping a common object is shown.

Vibration control of smart structureswith bonded PZT patches: novel adaptive filtering algorithm and hybridcontrol scheme

This paper focuses on a novel adaptive filtering algorithm, hybrid adaptive controlscheme and their application to real-time vibration control of smart structures. Anovel adaptive filtering algorithm is presented and compared with the least meansquare algorithm, showing a faster convergence rate, better noise-smoothingcapability and wider search field. A hybrid adaptive control scheme is thenproposed and analyzed. The scheme takes advantage of both feedback control andfeedforward control and provides the high damping, fast vibration suppression,good robustness and easy realization due to small computation complexity. Thenovel adaptive filtering algorithm and the hybrid control scheme are verified on acantilever beam with four bonded PZT patches. Satisfactory vibrationreduction has been observed for both single-input and single-output, andmulti-input and multi-output, control cases under sinusoidal, swept frequencysinusoidal and random disturbances, confirming the reliability and validity ofthe novel adaptive filtering algorithm and the hybrid adaptive controlscheme.

Radar and vision data fusion for hybrid adaptive cruise control on highways

Abstract. A system for hybrid adaptive cruise control (HACC) on high-speed roads designed as a combination of a radar-based ACC and visual perception is presented. The system is conceived to run on different performance levels depending on the actual perception capabilities. The advantages of a combination of the two different types of sensors are discussed in comparison to the shortcomings of each single sensor. A description of the visual lane detection and tracking procedure is given, followed by an overview of the vehicle detection, hypothesis generation, and tracking procedure. Enhanced robustness is achieved by cooperative estimation of egomotion and the dynamics of other vehicles using the lane-coordinate system as a common reference. Afterwards, the assignment of vehicles to lanes and the determination of the relevant vehicle for the longitudinal controller is described.

Hybrid adaptive optimal control of anaerobic fluidized bed bioreactor for the de-icing waste treatment

Hybrid adaptive control strategy was developed and tested for the degradation of propylene glycol, a major component in de-icing waste, in an anaerobic fluidized bed bioreactor (AFBR). A linearized model with time-varying parameters was first employed to describe the dynamic behavior of the AFBR using a recursive off-line system identification method. A hybrid adaptive control strategy was then tested using a recursive off-line system identification routine followed by an on-line adaptive optimal control algorithm. The objective of the controller was to achieve the desired set point value of the propionate concentration (stand-alone control output variable) by manipulating the dilution rate (control input variable). To do so, the optimal control law was developed by minimizing a cost function with constraint equations. This novel idea was successfully applied to the underlying system for 200 h. The set point (700 mg HPr l −1) was achieved even in the case where the feed concentration suddenly increased by 50% (9000 mg HPr l −1 to 13500 mg HPr l −1).

A hybrid adaptive fuzzy control for a class of nonlinear MIMO systems

A hybrid indirect and direct adaptive fuzzy output tracking control schemes are developed for a class of nonlinear multiple-input-multiple-output (MIMO) systems. This hybrid control system consists of observer and other different control components. Using the state observer, it does not require the system states to be available for measurement. Assisted by observer-based state feedback control component, the adaptive fuzzy system plays a dominant role to maintain the closed-loop stability. Being the auxiliary compensation, H/sup /spl infin// control and sliding mode control are designed to suppress the influence of external disturbance and remove fuzzy approximation error, respectively. Thus, the system performance can be greatly improved. The simulation results demonstrate that the proposed hybrid fuzzy control system can guarantee the system stability and also maintain a good tracking performance.

An adaptive dead-zone inverse controller for systems with sandwiched dead-zones

This paper addresses adaptive control of sandwich non-linear systems having an unknown sandwiched dead-zone between the linear dynamic blocks, as illustrated by a hydraulic valve system. An adaptive hybrid control scheme for control of such sandwiched dead-zone systems is developed. The proposed control scheme employs an inner-loop discrete-time feedback design and an outer-loop continuous-time feedback design, combined with an adaptive dead-zone inverse to cancel the dead-zone effect for improving output tracking. Stability and tracking performance of the closed-loop control system are analysed. Simulation results are used to illustrate the effectiveness of the proposed adaptive dead-zone inverse controller.

Hybrid adaptive fuzzy controllers with application to robotic systems

This paper presents a hybrid adaptive fuzzy control (HAFC) design methodology, where the nonlinear system is controlled by a state feedback controller and an adaptive fuzzy controller. In the adaptive fuzzy controller, an adaptive law is developed to tune a robust gain of the sliding-mode controller (SMC) so as to cope with the uncertainties and model errors. The proposed design method is applied to investigate the position and tracking control of a two-link robot. A comparison between the proportional-derivative control, SMC, adaptive control and the proposed HAFC for a two-link robot is made. The simulation results demonstrate that, by using the HAFC, the system performances is improved and the system also exhibits stability and robustness.

Adaptive hybrid intelligent control for uncertain nonlinear dynamical systems

A new hybrid direct/indirect adaptive fuzzy neural network (FNN) controller with a state observer and supervisory controller for a class of uncertain nonlinear dynamic systems is developed in this paper. The hybrid adaptive FNN controller, the free parameters of which can be tuned on-line by an observer-based output feedback control law and adaptive law, is a combination of direct and indirect adaptive FNN controllers. A weighting factor, which can be adjusted by the tradeoff between plant knowledge and control knowledge, is adopted to sum together the control efforts from indirect adaptive FNN controller and direct adaptive FNN controller. Furthermore, a supervisory controller is appended into the FNN controller to force the state to be within the constraint set. Therefore, if the FNN controller cannot maintain the stability, the supervisory controller starts working to guarantee stability. On the other hand, if the FNN controller works well, the supervisory controller will be deactivated. The overall adaptive scheme guarantees the global stability of the resulting closed-loop system in the sense that all signals involved are uniformly bounded. Two nonlinear systems, namely, inverted pendulum system and Chua's (1989) chaotic circuit, are fully illustrated to track sinusoidal signals. The resulting hybrid direct/indirect FNN control systems show better performances, i.e., tracking error and control effort can be made smaller and it is more flexible during the design process.

Hybrid adaptive fuzzy identification and control of nonlinear systems

We present a combined direct and indirect adaptive control scheme for adjusting an adaptive fuzzy controller, and adaptive fuzzy identification model parameters. First, using adaptive fuzzy building blocks, with a common set of parameters, we design and study an adaptive controller and an adaptive identification model that have been proposed for a general class of uncertain structure nonlinear dynamic systems. We then propose a hybrid adaptive (HA) law for adjusting the parameters. The HA law utilizes two types of errors in the adaptive system, the tracking error and the modeling error. Performance analysis using a Lyapunov synthesis approach proves the superiority of the HA law over the direct adaptive (DA) method in terms of faster and improved tracking and parameter convergence. Furthermore, this is achieved at negligible increased implementation cost or computational complexity. We prove a theorem that shows the properties of this hybrid adaptive fuzzy control system, i.e., bounds for the integral of the squared errors, and the conditions under which these errors converge asymptotically to zero are obtained. Finally, we apply the hybrid adaptive fuzzy controller to control a chaotic system, and the inverted pendulum system.

Robust model reference adaptive control with hybrid adaptive law

Robust model reference adaptive control (RMRAC) with hybrid adaptive law is considered. The advantages of the hybrid RMRAC scheme over other continuous counterparts include smaller computational effort during implementation and better robustness properties in the presence of modelling errors and disturbances. For the hybrid adaptive control scheme, stability and the tracking performance are analysed rigorously.

Hybrid adaptive control based on the characteristics of CNC end milling

In this paper, hybrid adaptive control algorithm that controls feedrate is proposed to regulate spindle current. For this purpose, variation of steady state spindle current, time constant and time delay were examined through experiments based on step end milling under various cutting condition. The developed hybrid adaptive control algorithm is composed of adjustable proportional feedback control (adjustable P control), fine control carries out detailed control, and entry feedrate control reduces the peak current produced when a tool makes contact with a workpiece. The adjustable P control was verified through comparisons between calculate P gains and experimentally obtained P gains. The hybrid adaptive control algorithm was applied to various cutting conditions and it showed global stability as well as excellent applicability behavior.

Real-time hybrid adaptive fuzzy control of a SCARA robot

This paper presents the design, development and implementation of a Hybrid Adaptive Fuzzy Controller (HAFC) suitable for real-time industrial applications. The developed HAFC consists of a weighted combination of the Direct Adaptive Fuzzy Controller (DAFC) and Indirect Adaptive Fuzzy Controller (IAFC) and a gradually activated supervisory controller. The unique feature of the HAFC is that no mathematical models of the plant are required and the proposed controller is able to adaptively estimate the bound functions on-line, which are required for the determination of the supervisory controller. The supervisor controller in the sense that all signals are bounded guarantees global stability of the closed-loop system. The approach of rapid prototyping is employed to implement the HAFC so as to control a Selectively Compliance Assembly Robot Arm (SCARA) Robot in real time. Simulink, an interactive graphical software for simulating dynamic systems, is used to model, simulate and analyse the dynamic system. Once encouraging system performances are achieved, the HAFC is implemented in real-time through Real-Time Workshop (RTW). RTW generates C-codes from the Simulink block diagrams and in turn, the generated codes (object codes) are downloaded to the Texas Instruments TMS320C31 floating-point processor, together with the supporting files, for execution. The performance of the HAFC was found to be superior and it matches favourably the simulation results.