Preview control of flexible manufacturing systems

The availability of predictions of future system inputs has motivated research into preview control to improve set-point tracking and disturbance rejection beyond that achievable via conventional feedback control. The design of preview controllers, typically based upon model predictive control (MPC) for its constraint handling properties, is often performed in a monolithic nature, coupling the feedback and feed-forward problems. This can create problems, such as: (i) an additional feedback loop is introduced by MPC, which alters the closed-loop dynamics of the existing feedback compensator, potentially resulting in a deterioration of the nominal sensitivities and robustness properties of an existing closed-loop and (ii) the default preview action from MPC can be poor, degrading the original feedback control performance. In our previous work, the former problem is addressed by presenting a modular MPC design on top of a given output-feedback controller, which retains the nominal closed-loop robustness and frequency-domain properties of the latter, despite the addition of the preview design. In this paper, we address the second problem; the preview compensator design in the modular MPC formulation. Specifically, we derive the key conditions that ensure, under a given closed-loop tuning, the preview compensator within the modular MPC formulation is systematic and well-designed in a sense that the preview control actions complement the existing feedback control law rather than opposing it. In addition, we also derive some important results, showing that the modular MPC can be implemented in a cascade over any given linear controllers and the proposed conditions hold, regardless of the observer design for the modular MPC. The key benefit of the modular MPC is that the preview control with constraint handling can be implemented without replacing the existing feedback controller. This is illustrated through some numerical examples.

In this paper, we propose an adaptive fuzzy output feedback control method for trajectory tracking control problem for robotic systems. Using Lyapunov method, we first develop a stable adaptive fuzzy state feedback control algorithm by assuming that the systems output and its derivatives are available for feedback control design. The algorithm combines fuzzy systems with robust adaptive controller. The fuzzy system approximates the certainty equivalent (CE)-based optimal controller while robustifying adaptive control term is used to cope with uncertainties that appeared from the effect of external disturbance, fuzzy approximation errors and other modeling errors. Then, an output feedback form of the position-velocity (state feedback) controller is proposed where unknown velocity signal is replaced by the output of model-free linear estimator. We show via asymptotic analysis that the tracking performance of the output feedback design can recover the performance achieved under the state feedback control design. Finally, the proposed method is implemented and evaluated on a 2-DOF robotic system to demonstrate the theoretical development for the real-time applications.

Intelligent Control of Manufacturing Systems Using Open Communication

Control of flexible manufacturing systems (FMS) requires the complex interaction of two components: (1) computers to perform automated control and routing activities, and (2) humans to supervise the automation, to monitor system flows and outputs, and to intervene to diagnose and either correct or compensate for unanticipated events. Current academic FMS scheduling research has focused mainly on the first component in the control loop, development of scheduling algorithms for optimization and control. Here, the second component is included in both the definition of the FMS control problem and the corresponding control approach. A more realistic definition of the FMS control environment is presented using a supervisory control framework. Within this context, the concept of aiding a human operator who supervises the predominantly automated FMS operations is developed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In recent years, the scheduling and control of Flexible Manufacturing Systems (FMS) has received considerable attention. Due to complexity considerations, heuristic solutions are often the norm for real-time scheduling and implementation of FMS control activities. However, heuristic solutions often lack consideration of overall system implications, and various practical difficulties may arise in the control of unmanned FMS.The purpose of this research is to address deadlock situations that may arise in unmanned FMS. Petri nets are used to model the sequencing of material through the workstations and the limited capacity of resources available in the FMS. The model is then used for defining restriction policies (i.e., policies that disallow some of the options of the real-time resource allocation strategy) for deadlock avoidance. We propose an on-line restriction policy that avoids deadlock situations, while enabling completion of all active jobs in the FMS. The proposed algorithm is minimally restrictive and suitable for real-time implementation.KeywordsInternal TransitionProduction SequenceRestriction PolicyReachability GraphProduction PlaceThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Tools for Design and Optimal Control of Production Line and Flexible Manufacturing Systems

Permanent magnet synchronous motors (PMSM) are mainly used in high-performance and high-efficiency motor drives such as used in railways. PMSM has the performance required for traction motors and is more compact, light weighted and efficient than induction motors. This paper presents the detailed modeling of PMSM drive in Simulink environment and also presents the speed control of PMSM using vector control approach and by use of state feedback (SFB) controller. The observer is designed to eliminate the need of sensors. The observer is designed for estimating any one of the state variable if the other state variable is known thus eliminating the need to sense both speed and current. The state feedback (SFB) controller is designed such to provide zero steady state error. For observation purpose a PI controller is also designed and the results of control actions have been compared and observed that SFB controller are more robust than PI controllers bpth in case of normal and over loading. In this paper, we have designed and simulated the mathematical model of PM synchronous motor and designed the state feedback control system using pole placement method and PI controller using bode plot method, for speed control of PMSM. The controller hence designed is found to be satisfactory and robust over wide range of operating condition and parameter variations.

Output feedback control of switched nonlinear systems using multiple Lyapunov functions

In recent years, a great deal of research has been focused on preventing deadlock in Flexible Manufacturing Systems. Policies based largely on Petri net models have been presented in the literature. Recently, a quite different approach has been developed based on supervisory control theory, and it has been adapted to solve the nonblocking maximally permissive control problem in various resource allocation systems, such as an Automatic Guided Vehicle system and a Production Cell. In this chapter, the authors obtain the corresponding control policy for a Flexible Manufacturing System, and from it derive an equivalent distributed control using the recent theory of supervisor localization.

ABSTRACTThe paper outlines a systematic method for controlling suspension deflection offset and drift, that can arise when an actively sprung vehicle encounters a constant gradient or a long wavelength road surface undulation. A brief review of the problem is given and it is shown that the inherent filtering effects of a standard Kalman observer does not sufficiently address the issue. An expanded Kalman filter is introduced to estimate the underlying vertical road velocity, and this is applied within a linear state feedback controller. Its effect on suspension performance is shown to be advantageous when compared with simpler state feedback and integral control techniques.

Deadlock prevention plays an important role in the modeling and control of flexible manufacturing systems (FMS). This paper presents a novel and computationally efficient method to design optimal control places, and an iteration approach that only computes the reachability graph of a plant Petri net model once in order to obtain a maximally permissive liveness-enforcing supervisor for an FMS. By using a vector covering approach, a minimal covering set of legal markings and a minimal covered set of first-met bad markings (FBM) are computed. At each iteration, an FBM from the minimal covered set is selected. By solving an integer linear programming problem, a place invariant is designed to prevent the FBM from being reached and no marking in the minimal covering set of legal markings is forbidden. This process is carried out until no FBM can be reached. In order to make the considered problem computationally tractable, binary decision diagrams (BDD) are used to compute the sets of legal markings and FBM, and solve the vector covering problem to get a minimal covering set of legal markings and a minimal covered set of FBM. Finally, a number of FMS examples are presented to illustrate the proposed approaches.

Flexible Manufacturing Systems (FMSs) are usually composed of general purpose machines with automatic tool changing capability and integrated material handling. The complexity of FMSs requires sophisticated control. In this paper we present a four-level control hierarchy and outline computationally feasible control algorithms for each level. The top level is concerned with the choice of part types and volumes to be assigned to the FMS over the next several months. The second level plans daily or shift production. Production levels are set and tools are allocated to machines so as to minimize holding and shortage costs. Various FMS environments are presented. The third level determines process routes for each part type in order to minimize material handling. Additional tools are loaded on machines when possible to maximize alternate routeing. Routes are then assigned to parts to minimize workload assignment, and these are used by level four for actual routeing, sequencing and material handling path control. The level three model is formulated as a linear program, and heuristics are used for level four. An example is provided to illustrate the completeness of the decision hierarchy and the relationships between levels.

An approach for integrated flexible manufacturing system (FMS) control is developed. Decentralized control is examined and shown to compare favorably with conventional centralized control. It complies with the requirements of flexibility, reactivity, and accessibility for use on FMSs. The principles of a coordination founded on an event-based approach are given. A presentation model and its implementation principles are presented. >

Model Based Intelligent Control in Flexible Manufacturing Systems

In this paper we discuss a new matrix-based modeling technique for flexible manufacturing systems (FMS). Four Task Plan Matrices can completely describe the job sequencing and resource assignment details of an FMS. The matrices come directly from plans generated by machine planners, or from the bill of materials and resource requirements matrix. Given the matrices, a rule-based supervisory controller operates in real-time to sequence jobs and assign resources depending on dynamic situational information about parts available, resources idle, and jobs completed. The function of the FMS can be quickly changed when new products are needed simply by changing the four matrices. By deriving explicit mathematical formulas for p-invariants and the resource requirement matrix for generalized reentrant flow lines, NP-complexity problems are avoided, which is often an issue in other modeling techniques.KeywordsManufacturingdiscrete event systemsflexible manufacturing systems.