Hybrid Control Structure Research Articles

Hybrid predictive control of process systems

AbstractThe operation of chemical processes often requires respecting constraints on manipulated inputs and process states. Input constraints typically reflect limits on the capacity of control actuators, such as valves or pumps, whereas state constraints represent desirable ranges of operation for process variables, such as temperatures or concentrations. Constraints, however, limit the set of initial conditions, starting from where a process can be stabilized at a possibly open‐loop unstable steady state. Therefore, in control of constrained processes, it is important to obtain an explicit characterization of the region of closed‐loop stability. Model predictive control (MPC) provides a suitable framework for implementing control that respects manipulated input and process variable constraints while meeting prescribed performance objectives. Unfortunately, the implicit nature of the feedback law in MPC (the control action is computed by solving on‐line a constrained optimization problem at each sampling time) makes the a priori computation of the closed‐loop stability region a very difficult task. Such a computation, however, is possible when Lyapunov‐based bounded control techniques are used to design controllers for the stabilization of systems with manipulated input constraints. Motivated by this, the present work proposes a hybrid predictive control structure, which seamlessly unites MPC and bounded control, for output feedback stabilization of linear systems with input constraints. The design of the proposed structure is based on the idea of using a bounded controller, with its associated region of stability, as a “fallback” controller for the output feedback implementation of MPC. Switching laws are derived to orchestrate the transition between the two controllers in a way that reconciles their respective stability and optimality properties. The switched closed‐loop system is shown to possess a region of guaranteed stability, achieved by appropriately switching between the two controllers. The proposed hybrid control structure is shown to provide a safety net for the practical implementation of MPC by providing a priori knowledge, through off‐line computations, of a large set of initial conditions for which closed‐loop stability is guaranteed. Finally, two simulation studies are presented to demonstrate the implementation and evaluate the effectiveness of the proposed hybrid predictive control scheme, as well as test its robustness with respect to modeling errors and measurement noise. © 2004 American Institute of Chemical Engineers AIChE J, 50: 1242–1259, 2004

Combined force and visual control of an industrial robot

This work proposes control structures that efficiently combine force control with vision servo control of robot manipulators. Impedance controllers are considered which are based both on visual servoing and on physical or fictitious force feedback, the force and visual information being combined in the image space. Force and visual servo controllers included in extended hybrid control structures are also considered. The combination of both force and vision based control allows the tasks range of the robot to be extended to partially structured environments. The proposed controllers, implemented on an industrial SCARA-type robot, are tested in tasks involving physical and virtual contact with the environment.

Hybrid predictive control of nonlinear systems: method and applications to chemical processes

AbstractA hybrid control structure that unites bounded control with model predictive control (MPC) is proposed for the constrained stabilization of nonlinear systems. The structure consists of: (1) a finite‐horizon model predictive controller, which can be linear or nonlinear, and with or without stability constraints, (2) a family of bounded nonlinear controllers for which the regions of constrained closed‐loop stability are explicitly characterized and (3) a high‐level supervisor that orchestrates switching between MPC and the bounded controllers. The central idea is to embed the implementation of MPC within the stability regions of the bounded controllers and employ these controllers as fall‐back in the event that MPC is unable to achieve closed‐loop stability (due, for example, to infeasibility of a given initial condition and/or horizon length, or due to computational difficulties in solving the nonlinear optimization). Switching laws, that monitor the closed‐loop state evolution under MPC, are derived to orchestrate the transitions in a way that guarantees asymptotic closed‐loop stability for all initial conditions within the union of the stability regions of the bounded controllers. The proposed hybrid control scheme is shown to provide a paradigm for the safe implementation of predictive control algorithms to nonlinear systems, with guaranteed stability regions. The efficacy of the proposed approach is demonstrated through applications to chemical reactor and crystallization process examples. Copyright © 2004 John Wiley & Sons, Ltd.

A new sequential hybrid control structure for car and tractor-trailer parallel parking

This paper presents a new sequential hybrid (reactive and planned) control structure for parallel parking manoeuvres in car and tractor-trailer vehicles. This architecture considers non continuous curvature manoeuvres. Thus, stopping the vehicle during manoeuvring in order to change the steering angle is considered. The efficiency ofthe proposed method is demonstrated using the nonholonomic mobile robot ROMEO-4R, with and without backing-up a trailer, designed and built at the University of Seville.

Hybrid IMC Dead-Beat Controller Design in Delta Domain

The paper presents a control system design technique in delta domain for IMC (Internal Model Control) structure. Also a generalised form for delta domain Dead-beat control algorithm is given, a hybrid implementation of the controller (Z-domain combined with delta domain) is presented and its architecture is compared with the pure deltadomain implementation. The effect of placing the limitations in the IMC structure is also studied. The hybrid control structure is compared with a similar Dead-beat controller in Z-domain for second and third order plants (benchmarks). An illustrative sensitivity analysis between the hybrid control system and the Z-domain system has been performed.

Intelligent control integration in thermal power plants rehabilitation

The problems of soft-computing technologies incorporation in the existing power plant control systems are under consideration. To overcome the traditional conservatism in acceptance of the newest intelligent technologies the possibilities of hybrid control structures at the basic control level are discussed as well as the reasons and the ways of soft-computing implementation at the supervisory level.

Development of a multiple channel active noise cancellation system for car cabins

This paper describes the development of a multiple-channel active noise cancellation system for car cabins. Various active techniques are compared in terms of control structures and control algorithms. The filtered-x least-mean-square algorithm with synthetic reference and the linear quadratic gaussian control are employed for controller synthesis. Feedback, feedforward and hybrid configurations are adopted as the major control structures. The plant model is identified using a frequency-domain method. The controllers are implemented by using a digital signal processor. Experiments are carried out to evaluate the proposed techniques for attenuation of the internal field in a car cabin. The experimental results indicate that the hybrid control structure yields the best performance in terms of noise attenuation and convergence speed.

Communications structure for sensory data in mobile robots

Mobile robotics development provides an excellent opportunity to experiment with various architectural solutions for distributed real-time systems. This is because of the increasing complexity of sensor and actuator hardware, and the interaction between intelligent features and real-time constraints. Currently, hybrid control structures seem to be the most widespread method of control. This paper describes a communications scenario resulting from hybrid structures. The YAIR robot and its communication infrastructure is described by addressing the control problems found and the solutions adopted. This paper presents a case study of implementing a hybrid communications system using the CAN bus. The worst-case message delay analysis for this bus is also presented, as well as the structure of identifiers defining its semantic possibilities. The deliberative part of the communication system is a developed object bus on TCP/IP protocol networks. The programming interface at this level takes the form of a distributed blackboard with extended properties such as a bind-notification mechanism and a temporal register recording the temporal firewall of information supplied. The overlap between both communication systems is a gateway service performing bi-directional mirroring over a set of CAN identifiers. Finally, a system test is presented. The test emphasises the intra-level gateway for validating performance and time expressiveness.

Neural‐hybrid control of systems with sandwiched dead‐zones

AbstractThe control of systems that have sandwiched nonsmooth nonlinearities, such as a dead‐zone sandwiched between two dynamic blocks, is addressed. An adaptive inverse control scheme using a hybrid controller structure and a neural network based inverse compensator, is proposed for such systems with unknown sandwiched dead‐zone. This neural‐hybrid controller consists of an inner loop discrete‐time feedback structure incorporated with an adaptive inverse using a neural network for the unknown dead‐zone, and an outer‐loop continuous‐time feedback control law for achieving desired output tracking. The dead‐zone compensator consists of two neural networks, one used as an estimator of the sandwiched dead‐zone function and the other for the compensation itself. The compensator neural network has neurons that can approximate jump functions such as a dead‐zone inverse. The weights of the two neural networks are tuned using a modified gradient algorithm. Simulation results are given to illustrate the performance of the proposed neural‐hybrid controller. Copyright © 2002 John Wiley & Sons, Ltd.

Design of a Hybrid External Structure for a Multi-Fingered Hand

A previous paper described a decentralized hybrid control structure for a multi-fingered hand (Perdereau and Drouin, 1998). Input set-points were given by a supervisory level to the individual position/force units in an open loop manner. In such a solution, force is explicitly controlled during the object motion, contrary to impedance strategy usually found in the literature, by an external hybrid position/force controller (Perdereau and Drouin, 1996). This paper proposes a hierarchical solution where the actual object position and orientation are measured and used in a closed loop to cancel disturbances. In fact, two decoupled control loops are implemented: one for the position and the other for the orientation. For the last one, an original method is suggested.

A Hybrid Control Structure for Vision Based Soccer Robot System

A soccer robot system consists of multi-agents with highly coordinated operations and movements so as to fulfill specific objectives, even under adverse situations. The coordination of multi-agents...

Fuzzy-PID hybrid control: Automatic rule generation using genetic algorithms

This paper demonstrates that the genetic algorithms can be used effectively for generating fuzzy rules in fuzzy-PID hybrid control structures. While a number of fuzzy-PID hybrid controllers are considered in the context of tuning of the PID controllers, a general view is provided that they are the special cases of either the set-point modification or the gain modification. Simulation results reveal that genetic algorithms are effective tools to search for optimal or near optimal fuzzy rules. Along with this, they also suggest that there is a real need to develop genetic algorithms which are more tractable for fuzzy rule generation and to study theoretical questions related to the hybrid control structure.

An Adaptive Hybrid Fuzzy - Neural Controller

This paper presents investigations toward adaptive fuzzy control using neural networks for the control of nonlinear and time variable physical processes. By using a hybrid structure, the fuzzy logic controller can be composed of its traditional components and two neural networks. The first neural network works as an adaptive rule estimator for online acquisition and modification of the fuzzy rules as it was presented in a previous work. Here a second neural network is proposed as a further development of the hybrid structure which implements the defuzzification function and works as an adaptive neural defuzzifier. The standard one step gradient descent learning procedure is implemented and the output from a conventional feedback controller is used for tuning the parameters of the neural defuzzifier. Comparison with the traditional fuzzy technique is discussed. The results obtained by simulation show the better performance of the proposed hybrid control structure.

Nonlinear identification and control of a heat exchanger: A neural network approach

In this paper the potentials of neural networks-based control techniques are explored by applying a nonlinear generalized minimum variance control methodology to a simulated application example. In particular, reference is made to the control problem of regulating the output temperature of a liquid-satured steam heat exchanger by acting on the liquid flow-rate. Due to the non-minimum phase characteristic of the dynamics of the process, a simple inverting minimum variance controller is unsuitable. On the other hand, an effective solution is provided by a detuned model reference approach, which introduces a penalization factor in the control variable. A steady-state off-set error problem, caused by the neural network approximations, is tackled by means of an hybrid control structure, which combines a nonlinear integral action block with a neural controller. A comparison analysis is made to show the effectiveness of the proposed neural control schemes with respect to classical linear controllers.

Position/force control of a manipulator mounted on a vehicle

To perform complex tasks with a mobile manipulator (consisting of a robot arm mounted on a vehicle) moving outdoors on an uneven ground, a first issue is to develop robust control laws for tasks in a vehicle-embedded frame. So, we have tested the behavior of the robot arm with classical control laws for several disturbances due to the motions of the vehicle. First, we have tested positioning tasks of the end effector with respect to its base reference frame with a Cartesian PID and a Cartesian Variable Structure Control-High Frequency. Then, we have tested hybrid and external force control structures for an experiment of following a surface situated on the vehicle. Results are presented in the form of graphs.

Neuro-adaptive hybrid controller for robot-manipulator tracking control

The paper is concerned with the design of a hybrid controller structure, consisting of the adaptive control law and a neural-network-based learning scheme for adaptation of time-varying controller parameters. The target error vector for weight adaptation of the neural networks is derived using the Lyapunov-function approach. The global stability of the closed-loop feedback system is guaranteed, provided the structure of the robot-manipulator dynamics model is exact. Generalisation of the controller over the desired trajectory space has been established using an on-line weight-learning scheme. Model learning, using a priori knowledge of a robot arm model, has been shown to improve tracking accuracy. The proposed control scheme has been implemented using both MLN and RBF networks. Faster convergence, better generalisation and superior tracking accuracy have been achieved in the case of the RBF network.

Fuzzy Sliding-Mode Control of Gasoline Fuel-Injection System with Oxygen Sensor

The fuzzy "sliding mode" control method is proposed in this paper for the design of a closed-loop fuel-injection system. A fuzzy logic control using variable structure system is combined with current oxygen sensors to maintain the stoichiometric air-to-fuel ratio. The fuzzy cell technique is also proposed to implement the designed controller on nonfuzzy processors. The proposed method offers good potential for the design of an engine controller since : (1) bias and large chattering errors due to the hysteresis of the oxygen sensor can be reduced by utilizing fuzzy logic control inside the boundary layer of the sliding mode ; (2) robustness and stability are analytically guaranteed by the hybrid control structure. Simulation studies based on a typical engine-only model demonstrate the usefulness of the proposed method.

L2-stability and the related design concept for SISO linear system associated with fuzzy logic controller

A hybrid (fuzzy and nonfuzzy) design concept has been proposed for the linear single-input single-output (SISO) system associated with fuzzy logic controllers. In the present design techniques, the concept of L2-stability and its graphical interpretation, i.e. the circle criterion, are used. Thus the use of the classical Nyquist locus and the gain-phase chart are brought into the picture. The drawbacks of the L2-stability/circle criterion are indicated and overcome. In the present design technique, which is graphical in nature, the designer is given a feel for the system. Examples are provided to illustrate the application of hybrid controller's structure for the synthesis of the linear SISO system associated with the fuzzy logic controller.