This paper formally presents a class of planning problems, the SAS-PUS class, which allows non-binary state variables and parallel execution of actions. The class is proven to be tractable, and we provide a sound and complete, polynomial time algorithm for planning within this class. Since the SAS-PUS class is an extension of the previously presented SAS-PUBS class, this result means that we are getting closer to tackling realistic planning problems in sequential control. In such problems, a restricted problem representation is often sufficient but the size of the problems make tractability an important issue.

This article describes a polynomial-time, O(n³), planning algorithm for a limited class of planning problems. Compared to previous work on complexity of algorithms for knowledge-based or logic-based planning, our algorithm achieves computational tractability, but at the expense of only applying to a significantly more limited class of problems. Our algorithm is proven correct, and it always returns a parallel minimal plan if there is a plan at all.

Vi belyser olikheter och likheter i hur grundutbildningen styrs på fyra svenska tekniska högskolor. Vi jämför hur lärare och examinatorer väljs ut, hur medel fördelas och vilken roll programansvariga (eller motsvarande) har. De strukturella skillnaderna är relativt stora med störst autonomi för programansvariga på Chalmers tekniska högskola vilket delvis har att göra med att detta lärosäte lyder under aktiebolagslagen.

Some aspects of computer support in learning and assessment in engineering education are discussed. It is emphasized that the use of computer support, like e.g. simulations, computations, visualizations, has to be closely connected to the formulation of the expected learning outcomes and the assessment methods. Some examples of computer support are related to the CDIO Syllabus. Some experiences from more that two decades of computer aided learning and assessment within the Division of Automatic Control at Linköping University are presented     

The automatic control subject has several connections to sustainability and can play an important role in the strive towards a more sustainable society. An example of how sustainability is included in a basic course in automatic control is presented, where the links between the degree requirements, sustainability and the subject are illustrated using The Global Goals for Sustainable Development (SGDs). The key idea is to present real world application examples where automatic control is a vital component and there are clear connections to the SDGs. The examples are inspired and illustrated using videos and images taken from the internet. Several times during the course a part of the lecture time is used to show a video, describe how the control subject comes in, and how the use of feedback control via the application can contribute to the fulfillment of the SDG

An example of how sustainability aspects can be treated in a basic course in automatic control is presented. This is done by connecting the subject to some of the Sustainable Development Goals (SDGs) and giving examples of how automatic control can contribute to the fulfillment of these goals. The examples are inspired and illustrated using videos and images taken from the internet, showing various examples of applications where feedback control plays a crucial role. On several occasions during the course a part of the lecture time is used to show a video, describe how the control subject comes in, and how the use of feedback control via the application can contribute to the fulfillment of the SDG. 

This paper reviews and compares two methods for fault detection and isolation in a stochastic setting, assuming additive faults on input and output signals and stochastic unmeasurable disturbances. The first method is the parity space approach, analyzed in a stochastic setting. This leads to Kalman filter like residual generators, but with a FIR filter rather than an IIR filter as for the Kalman filter. The second method is to use principal component analysis (PCA). The advantage is that no model or structural information about the dynamic system is needed, in contrast to the parity space approach. We explain how PCA works in terms of parity space relations. The methods are illustrated on a simulation model of an F-16 aircraft, where six different faults are considered. The result is that PCA has similar fault detection and isolation capabilities as the stochastic parity space approach.

This paper compares two methods for fault detection and isolation in a stochastic setting. We assume additive faults on input and output signals, and stochastic unmeasurable disturbances. The first method is the parity space approach, analyzed in a stochastic setting. The stochastic parity space approach is similar to a Kalman filter, but uses an FIR fiter, while the Kalman filter is IIR. This enables faster response to changes. The second method is to use PCA, principal component analysis. In this case no model is needed, but fault isolation will be more difficult. The methods are illustrated on a simulation model of an F-16 aircraft. The fault detection probabilities can be calculated explicitly for the parity space approach, and are verified by simulations. The simulations of the PCA method suggest that the residuals have similar fault detection and isolation capabilities as for the stochastic parity space approach.

This paper compares two methods for fault detection and isolation in a stochastic setting. We assume additive faults on input and output signals, and stochastic unmeasurable disturbances. The first method is the parity space approach, analyzed in a stochastic setting. The stochastic parity space approach is similar to a Kalman filter, but uses an FIR fiter, while the Kalman filter is IIR. This enables faster response to changes. The second method is to use PCA, principal component analysis. In this case no model is needed, but fault isolation will be more difficult. The methods are illustrated on a simulation model of an F-16 aircraft. The fault detection probabilities can be calculated explicitly for the parity space approach, and are verified by simulations. The simulations of the PCA method suggest that the residuals have similar fault detection and isolation capabilities as for the stochastic parity space approach.

Problem Based Learning, PBL, has been used for several years, especially in the medical commmunity. At Linköping University, now also the program in Information Technology is taught using this method. We describe how PBL is used in a basic course in control theory, including linear algebra and Laplace transforms. The experience from the first three years of the course is promising. The students are in general more active and more motivated than students in traditional courses. The teachers spend roughly the same number of hours on the course, but the focus has shifted to more contact with the students.

Hösten 2013 startade Linköpings universitet den första civilingenjörsutbildningen i Mjukvaruteknik. Utbildningens mål är att bland annat att ge ett helhetsperspektiv på modern storskalig mjukvaruutveckling, ge en gedigen grund i datavetenskap och computational thinking samt främja entreprenörskap och innovation. Studenternas gensvar har varit över förväntan med över 600 sökande till de 30 platserna varav 134 förstahandssökande. Här presenterar vi programmets vision, mål, designprinciper samt det färdiga programmet. En viktig förebild är ACM/IEEE Computer Science Curricula som precis kommit i en ny uppdaterad version. Tre pedagogiska idéer vi har följt är (1) att använda projektkurser för att integrera teori och praktik samt ge erfarenhet i den vanligaste arbetsformen i näringslivet; (2) att undervisa i flera olika programspråk och flera olika programutvecklingsmetodiker för att ge en plattform att ta till sig det senaste på området; och (3) att införa en programsammanhållande kurs i ingenjörsprofessionalism i årskurs 1–3 som ger studenterna verktyg att reflektera över sitt eget lärande, att jobba i näringslivet samt sin professionella yrkesroll. Artikeln avslutas med en diskussion om viktiga aspekter som computational thinking och ACM/IEEE CS Curricula.

Of all hard- and software developed for industrial control purposes, the majority is devoted to sequential, or binary valued, control and only a minor part to classical linear control. Typically, the sequential parts of the controller are invoked during startup and shut-down to bring the system into its normal operating region and into some safe standby region, respectively. Despite its importance, fairly little theoretical research has been devoted to this area, and sequential control programs are therefore still created manually without much theoretical support to obtain a systematic approach.

We propose a method to create sequential control programs automatically. The main idea is to spend some eort off-line modelling the plant, and from this model generate the control strategy, that is the plan. The plant is modelled using action structures, thereby concentrating on the actions instead of the states of the plant. In general the planning problem shows exponential complexity in the number of state variables. However, by focusing on the actions, we can identify problem classes as well as algorithms such that the planning complexity is reduced to polynomial complexity. We prove that these algorithms are sound, i.e., the generated solution will solve the stated problem, and complete, i.e., if the algorithms fail, then no solution exists. The algorithms generate a plan as a set of actions and a partial order on this set specifying the execution order. The generated plan is proven to be minimal and maximally parallel.

For a larger class of problems we propose a method to split the original problem into a number of simpler problems that can each be solved using one of the presented algorithms. It is also shown how a plan can be translated into a GRAFCET chart, and to illustrate these ideas we have implemented a planning tool, i.e., a system that is able to automatically create control schemes. Such a tool can of course also be used on-line if it is fast enough. This possibility opens up completely new applications such as operator supervision and simplied error recovery and restart procedures after a plant fault has occurred.

Additionally we analyze reachability for a restricted class of problems. For this class we state a reachability criterion that may be checked using a slightly modified version of one of the above mentioned algorithms.

Sequential control is probably the most common control problem in industry. Despite its importance, fairly little theoretical research has been devoted to this problem, and practical problems are typically solved by ad hoc methods, sometimes quite time-consuming.

Sequential control is really a planning problem. In this thesis, we describe and analyse sequential problems in a systematic way based on a formalism from artificial intelligence (AI). We focus on solving the planning prblem, i.e., the problem of finding a sequence of actions which transforms a given initial state into a desired final state.

The complexity of planning using a method based on exhaustive search increases exponentially with the number of state variables, ans so there is a need for alternative methods.

We suggest a method for solving the planning problem for a class of sequential problems. The complexity of this method increases polynomially with the number of state variables. The method is proven correct and complete, and it always returns a minimal plan if there is a plan at all. We also discuss how planning can be handled outside this subclass.

Sequential control is probably the most common control problem in industry. Despite its importance, fairly little theoretical research has been devoted to this problem, and practical problems are typically solved by ad hoc methods, sometimes quite time-consuming.

Sequential control is really a planning problem. In this thesis, we describe and analyse sequential problems in a systematic way based on a formalism from artificial intelligence (AI). We focus on solving the planning prblem, i.e., the problem of finding a sequence of actions which transforms a given initial state into a desired final state.

The complexity of planning using a method based on exhaustive search increases exponentially with the number of state variables, ans so there is a need for alternative methods.

We suggest a method for solving the planning problem for a class of sequential problems. The complexity of this method increases polynomially with the number of state variables. The method is proven correct and complete, and it always returns a minimal plan if there is a plan at all. We also discuss how planning can be handled outside this subclass.

Of all control related problems within industry, those of sequential character are particularly common. Almost all industrial plants contain sequential parts; startup or shutdown phases are typical examples of this. Despite its importance fairly little theoretical research has been devoted to this problem. We study a subclass of sequential control problems, which we call the SAS-PUBS class. For this class of problems we give a reachability criterion. The complexity of checking if the criterion is fulfilled only increases polynomially with the number of state variables. 

Sequential control is a common control problem in industry. Despite its importance fairly little theoretical research has been devoted to this problem. We study a subclass of sequential control problems, which we call the SAS-PUBS class, and present a planning algorithm for this class. The algorithm is developed using formalism from articial intelligence (AI). For planning problems in the SAS-PUBS class the algorithm nds a plan from a given initial state to a desired final state if and only if any plan exists solving the stated planning problem. Furthermore the complexity of the given algorithm increases polynomially with the number of state variables.

Sequential control is a common control problem in industry. Despite its importance fairly little theoretical research has been devoted to this problem. We study a subclass of sequential control problems, which we call the SAS-PUBS class, and present a planning algorithm for this class. The algorithm is developed using formalism from articial intelligence (AI). For planning problems in the SAS-PUBS class the algorithm nds a plan from a given initial state to a desired final state if and only if any plan exists solving the stated planning problem. Furthermore the complexity of the given algorithm increases polynomially with the number of state variables.

This paper formally presents a class of planning problems which allows non-binary state variables and parallel execution of actions. The class is proven to be tractable, and we provide a sound and complete polynomial time algorithm for planning within this class. This result means that we are getting closer to tacking realistic planning problems in sequential control, where a restricted problem representation is often sufficient, but where the size of the problems make tractability an important issue.

This paper formally presents a class of planning problems which allows non-binary state variables and parallel execution of actions. The class is proven to be tractable, and we provide a sound and complete polynomial time algorithm for planning within this class. This result means that we are getting closer to tacking realistic planning problems in sequential control, where a restricted problem representation is often sufficient, but where the size of the problems make tractability an important issue.

This paper describes a polynomial-time, O(n³), planning algorithm for a limited class of planning problems. Compared to previous work on complexity of algorithms for knowledge-based or logic-based planning, our algorithm achieves computational tractability, but at the expense of only applying to a significantly more limited class of problems. Our algorithm is proven correct and complete, and it always returns a minimal plan if there is a plan at all.

This paper describes a polynomial-time, O(n³), planning algorithm for a limited class of planning problems. Compared to previous work on complexity of algorithms for knowledge-based or logic-based planning, our algorithm achieves computational tractability, but at the expense of only applying to a significantly more limited class of problems. Our algorithm is proven correct and complete, and it always returns a minimal plan if there is a plan at all.

This paper describes a polynomial-time, O(n 3), planning algorithm for a limited class of planning problems. Compared to previous work on complexity of algorithms for knowledge-based or logic-based planning, our algorithm achieves computational tractability, but at the expense of only applying to a significantly more limited class of problems. Our algorithm is proven correct and complete, and it always returns a minimal plan if there is a plan at all.

This article describes a polynomial-time, O(n³), planning algorithm for a limited class of planning problems. Compared to previous work on complexity of algorithms for knowledge-based or logic-based planning, our algorithm achieves computational tractability, but at the expense of only applying to a significantly more limited class of problems. Our algorithm is proven correct, and it always returns a parallel minimal plan if there is a plan at all.

This article describes a polynomial-time, O(n³), planning algorithm for a limited class of planning problems. Compared to previous work on complexity of algorithms for knowledge-based or logic-based planning, our algorithm achieves computational tractability, but at the expense of only applying to a significantly more limited class of problems. Our algorithm is proven correct, and it always returns a parallel minimal plan if there is a plan at all.

The industry wants provably correct and fast formal methods for handling combinatorial dynamical systems. One example of such problems is error recovery in industrial processes. We have used a provably correct, polynomial-time planning algorithm to plan for a miniature assembly line, which assembles toy cars. Although somewhat limited, this process has many similarities with real industrial processes. By exploring the structure of this assembly line we have extended a previously presented algorithm, thus extending the class of problems that can be handled in polynomial time.

The industry wants provably correct and fast formal methods for handling combinatorial dynamical systems. One example of such problems is error recovery in industrial processes. We have used a provably correct, polynomial-time planning algorithm to plan for a miniature assembly line, which assembles toy cars. Although somewhat limited, this process has many similarities with real industrial processes. By exploring the structure of this assembly line we have extended a previously presented algorithm, thus extending the class of problems that can be handled in polynomial time. The planning tool presented here contains general-purpose algorithms that generate plans in the form of GRAFCET charts that are automatically translated into PLC code using a commercial PLC compiler.

The industry wants formal methods for dealing with combinatorial dynamical systems that are provably correct and fast. One example of such problems is error recovery in industrial processes. We have used a provably correct, polynomial-time planning algorithm to plan for a miniature assembly line, which assembles toy cars. Although somewhat limited, this process has many similarities with real industrial processes. By exploring the structure of this assembly line we have extended apreviously presented algorithm making the class of problems that can be handled in polynomial time larger.

Problem Based Learning, PBL, has been used for several years, especially in the medical commmunity. At Linköping University, now also the program in Information Technology is taught using this method. We describe how PBL is used in a basic course in control theory, including linear algebra and Laplace transforms. The experience from the first three years of the course is promising. The students are in general more active and more motivated than students in traditional courses. The teachers spend roughly the same number of hours on the course, but the focus has shifted to more contact with the students.

The industry wants provably correct and fast formal methods for handling combinatorial dynamical systems. One example of such problems is error recovery in industrial processes. We have used a provably correct, polynomial-time planning algorithm to plan for a miniature assembly line, which assembles toy cars. Although somewhat limited, this process has many similarities with real industrial processes. By exploring the structure of this assembly line we have extended a previously presented algorithm, thus extending the class of problems that can be handled in polynomial time.

The industry wants provably correct and fast formal methods for handling combinatorial dynamical systems. One example of such problems is error recovery in industrial processes. We have used a provably correct, polynomial-time planning algorithm to plan for a miniature assembly line, which assembles toy cars. Although somewhat limited, this process has many similarities with real industrial processes. By exploring the structure of this assembly line we have extended a previously presented algorithm, thus extending the class of problems that can be handled in polynomial time. The planning tool presented here contains general-purpose algorithms that generate plans in the form of GRAFCET charts that are automatically translated into PLC code using a commercial PLC compiler.

This paper presents a provably correct and efficient, polynomial time, planning tool and its application to a miniature assembly line for toy cars. Although somewhat limited, this process has many similarities with real industrial processes. One of our previous polynomial-time planning algorithms has been extended and adapted to work for a larger class of planning problems, including this particular process. The plans produced by the planner are then translated into GRAFCET charts, which are compiled into code for a programmable logic controller. Although capable of producing ordinary assembly plans, the system is mainly intended for producing plans in error recovery situations.

This paper presents a provably correct and efficient, polynomial time, planning tool and its application to a miniature assembly line for toy cars. Although somewhat limited, this process has many similarities with real industrial processes. One of our previous polynomial-time planning algorithms has been extended and adapted to work for a larger class of planning problems, including this particular process. The plans produced by the planner are then translated into GRAFCET charts, which are compiled into code for a programmable logic controller. Although capable of producing ordinary assembly plans, the system is mainly intended for producing plans in error recovery situations.

The industry asks for formal, efficient methods for tackling dynamic systems of combinatorial nature, eg. error recovery in industrial processes. As an application example, we have used a minituare assembly line that assembles toy cars---a process that has many similarities with real industrial processes. We have tried to apply one of our provably correct, polynomial-time planning algorithms to plan for this assembly line. Analysing why this did not work provided insight into the inherent structure of the process. This was used as feedback for modifying the theory and extending the previous theoretical results. This resulted in a new provably correct, polynomial-time planner which is sufficient for this application.

The industry wants formal methods for dealing with combinatorial dynamical systems that are provably correct and fast. One example of such problems is error recovery in industrial processes. We have used a provably correct, polynomial-time planning algorithm to plan for a miniature assembly line, which assembles toy cars. Although somewhat limited, this process has many similarities with real industrial processes. By exploring the structure of this assembly line we have extended apreviously presented algorithm making the class of problems that can be handled in polynomial time larger.

The industry wants formal methods for dealing with combinatorial dynamical systems that are provably correct and fast. One example of such problems is error recovery in industrial processes. We have used a provably correct, polynomial-time planning algorithm to plan for a miniature assembly line, which assembles toy cars. Although somewhat limited, this process has many similarities with real industrial processes. By exploring the structure of this assembly line we have extended apreviously presented algorithm making the class of problems that can be handled in polynomial time larger.

This article addresses the problem of fault propagation between software modules in a large-scale control system with object oriented architecture. There exists a conflict between object-oriented design goals such as encapsulation and modularity, and the possibility to suppress propagating error conditions. The propagation manifests itself as many irrelevant error messages, and hence causes problems for system operators and service personnel when attempting to isolate the real fault. We propose a fault isolation scheme aimed at achieving clear and concise fault information to the operator without violating encapsulation and modularity. The approach is implemented and tested on a commercial industrial robot control system from ABB Robotics and a patent application has been filed with the swedish patent office (PRV).

We propose a fault isolation scheme based on model-checking in order to reason about temporal properties of loosely coupled systems of concurrent processes. To address the problem of state space explosion we advocate an automatic abstraction technique based on a notion of observational equivalence. We statically analyze a system and construct a total function from possible message logs to isolated faults. Thus, fault isolation reduces to table lookup. Tables can be used at design time to find non-diagnosable failures of the system as well as redundant error messages.

We investigate the problem of doing post mortem fault isolation for concurrent systems using a behavioral model. The aim is to isolate the action that has caused the failure of the system, the root action. The naive approach would be to say that a certain action is the root action if it is a logical consequence of the model and observations that the action is the first "bad thing to happen". This, however, is a strong requirement and puts high demand on the model. In this paper we describe the concept of strong root candidate, a relaxation of the naive approach. The advantage of determining the strong root candidate directly from model and observations is that the set of traces consistent with model and observations need not be explicitly computed. The property of strong root candidate can instead be determined on-the-fly, thus only computing relevant parts of the reachable state space.

We propose a fault isolation scheme based on model checking in order to reason about temporal properties of loosely coupled systems of concurrent processes. To address the problem of state space explosion we advocate an automatic abstraction technique based on a notion of observational equivalence. We statically analyze a system and construct a total function from possible message logs to isolated faults. Thus, fault isolation reduces to table lookup. Tables can be used at design time to find non-diagnosable failures of the system as well as redundant error messages.

We describe an approach to model-based fault isolation in large object oriented control systems. Our method relies on models of structure and behavior expressed in a subset of the Unified Modeling Language (UML). We consider system failures where on-line analysisis problematic, e.g. in safety-critical applications, leading to propagating alarms which have to be analyzed off-line. In our previous work we have shown how to use structural models, in the form of UML class diagrams, to reason about causal dependencies between error messages. In this paper we argue that models of behavior, in the form of UML state charts, together with verification techniques based on model checking further improve the ability to isolate faults off-line even when both models and observations are incomplete.

Of all hard- and software developed for industrial control purposes, the majority is devoted to sequential, or binary valued, control and only a minor part to classical linear control. The sequential parts of the controller are typically invoked during startup or shut-down phases to bring the system either into its normal operating region or into some safe standby region. Despite its importance, fairly little theoretical research has been devoted to this area, and sequential control programs are still created manually without much support for a systematic approach. We propose a method to create sequential control programs automatically and online upon request, for example when a plant fault has occurred. The main idea is to spend some effort off-line on modeling the process, and from this model generate the control strategy, i.e. the plan. Here we present a planning tool implemented in a real-time expert system called G2. The planning system contains algorithms for creating plans in form of minimal GRAFCET charts that show maximal parallelism. The algorithms can handle a restricted class of problems and for this class the complexity only increases polynomially with the number of state variables.

Of all hard- and software developed for industrial control purposes, the majority is devoted to sequential, or binary valued, control and only a minor part to classical linear control. The sequential parts of the controller are typically invoked during startup or shut-down phases to bring the system either into its normal operating region or into some safe standby region. Despite its importance, fairly little theoretical research has been devoted to this area, and sequential control programs are still created manually without much support for a systematic approach. We propose a method to create sequential control programs automatically and online upon request, for example when a plant fault has occurred. The main idea is to spend some effort off-line on modeling the process, and from this model generate the control strategy, i.e. the plan. Here we present a planning tool implemented in a real-time expert system called G2. The planning system contains algorithms for creating plans in form of minimal GRAFCET charts that show maximal parallelism. The algorithms can handle a restricted class of problems and for this class the complexity only increases polynomially with the number of state variables.

Of all hard- and software developed for industrial control purposes, the majority is devoted to sequential, or binary valued, control and only a minor part to classical linear control. The sequential parts of the controller are typically invoked during startup or shut-down phases to bring the system either into its normal operating region or into some safe standby region. Despite its importance, fairly little theoretical research has been devoted to this area, and sequential control programs are still created manually without much support for a systematic approach. We propose a method to create sequential control programs automatically and online upon request, for example when a plant fault has occurred. The main idea is to spend some effort off-line on modeling the process, and from this model generate the control strategy, i.e. the plan. Here we present a planning tool implemented in a real-time expert system called G2. The planning system contains algorithms for creating plans in form of minimal GRAFCET charts that show maximal parallelism. The algorithms can handle a restricted class of problems and for this class the complexity only increases polynomially with the number of state variables.

Studierektorsområdet Reglersystem vid LiTH (Tekniska Högskolan vid Linköpings universitet) tilldelades 2007 utmärkelsen Framstående Utbildningsmiljö av Högskoleverket. Utmärkelsen gavs till sammanlagt fem utbildningsmiljöer, varav två är verksamma inom ingenjörsutbildning. I detta bidrag avser vi att redogöra för denna process och de faktorer som främst bidrog till att området Reglersystem fick denna utmärkelse. De faktorer som lyfts fram som centrala för att åstadkomma en god utbildningskvalité är framför allt att ha tydliga mål för utbildningen, en gedigen ämnesmässig grund, en väl fungerande organisation och positiv attityd bland alla medverkande samt former för lärande och examination som är anpassade till utbildningens mål.

This article addresses the problem of fault propagation between software modules in a large-scale control system with object oriented architecture. There exists a conflict between object-oriented design goals such as encapsulation and modularity, and the possibility to suppress propagating error conditions. The propagation manifests itself as many irrelevant error messages, and hence causes problems for system operators and service personnel when attempting to isolate the real fault. We propose a fault isolation scheme aimed at achieving clear and concise fault information to the operator without violating encapsulation and modularity. The approach is implemented and tested on a commercial industrial robot control system from ABB Robotics and a patent application has been filed with the swedish patent office (PRV).

This report addresses the problem of fault propagation between software modules in a large industrial control system with anobject oriented architecture. There exists a conflict between object-oriented design goals such as encapsulation and modularity, and the possibility to suppress propagating error conditions. When an object detects an error condition, it is not desirable toperform the extensive querying of other objects that would be necessary to decide how close to the real fault the object is and hence whether it should report to the user. The fault propagation manifests itself as many irrelevant error messages thus causing problems for system operators and service personnel trying to quickly isolate the real fault. A system developer with insight in the system design, can, of course, often easily interpret the multitude of error messages from a fault scenario and isolate the primary cause. The key observation is thatt his can often be done using high-level models of the system and the fault propagation. We have made an effort to automate this procedure, and we propose a fault isolation scheme as an extra layer between the operator and the core control system. In the fault isolation layer, post-processing of the fault information from the system is performed, to achieve clear and concise fault information to the operator without violating encapsulation and modularity. A high-level and informal explanation model for the fault propagation is presented and a taxonomy for error conditions in an object oriented system is proposed. We outline algorithms and methods that use the explanation model and the error condition taxonomy together with a structural system model to form a cause-effect relation on the error messages, that can be used to find the most significant error message(s) in a fault scenario.The approach is illustrated by means of several examples. The approach has been implemented and tested on a commercial control system for industrial robots developed by ABB Robotics. A patent claim has also been filed with the Swedish Patent Office (PRV).

This report discusses the problem with fault propagation in large scale control systems with object oriented architecture. There seems to be a trade-off between the degree of object encapsulation and the possibility of suppressing propagating error messages -- when an individual object detects a fault, it does not in general know how close it is to the real fault, and hence whether it should report an error to the operator or not. Mechanisms for querying other objects on-the-fly is feasible only for closely related objects due to OO architecture goals.

We present a model based approach to diagnosability analysis for interacting ¯nitestate systems where fault isolation is deferred until the system comes to a stand-still. Local abstractions of the system model are used to alleviate the state spaceexplosion. Pairs of closely coupled automata are merged and replaced by a sin-gle automaton with an equivalently behavior as seen from the rest of the system;interaction between the merged automata is internalized and the new equivalentautomaton is subsequently abstracted from internal behavior irrelevant to fault iso-lation. In moderately concurrent systems these steps can often be iterated until thesystem consists of a single automaton providing a compact encoding of all possiblefault scenarios of the original model. We illustrate how the resulting abstractioncan be used as a basis for post mortem diagnosability analysis.