Feb 24, 2007 - The 80C186 instruction set is identical to that of the Intel 8086 .... (1) An external interrupt pin of TMS320C30 is programmed to receive the.
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Implementation of adaptive speed control algorithms for diesel-driven power plants on a digital signal processor a
S. ROY & O. P. MALIK
a
a
Department of Electrical and Computer Engineering, University of Calgary, Calgary, Alberta, T2N 1N4, Canada Version of record first published: 24 Feb 2007.
To cite this article: S. ROY & O. P. MALIK (1994): Implementation of adaptive speed control algorithms for diesel-driven power plants on a digital signal processor, International Journal of Control, 60:4, 467-481 To link to this article: http://dx.doi.org/10.1080/00207179408921476
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CONTROL, 1994, VOL. 60, No.4, 467-481
Implementation of adaptive speed control algorithms for diesel-driven power plants on a digital signal processor
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S. ROyt and O. P. MALIKt A computationally powerful digital signal processor (DSP)-based implementation of adaptive speed control algorithms for a diesel-driven electric generating unit is described in this paper. The complexity of the algorithm and the short control interval required for this application necessitated the use of a DSP. Hardware and software have been designed such that the overall structure of the adaptive controller is similar to the corresponding components of a commercially available PI controller. Illustrative real-time tests with the proposed adaptive controller and the commercial controller illustrate the effectiveness of the adaptive controller. Introduction Implementation of a controller usually involves the following components in hardware and software: 1.
(a) a processor-based unit to perform control computations; (b) data sensing transducer circuits for variables required by the processing unit; (c) output circuits to generate the actuating control signal based on the computations carried out by the processing unit; (d) a programming/measurement interface to set parameters in the processing unit, and to measure and observe values of system variables for monitoring purposes. The capabilities of the processing unit are usually determined by the nature of the controlled plant and the complexity of the algorithm. The data sensing and the output circuits are usually determined by the requirements of the processor and the plant interface with the controller. Item (d), the interface, is the component that makes the system 'user-friendly' to a varying extent. Self-tuning adaptive control algorithms have two major components: plant identification and control computation. Information about the current state of the plant in the form of parameters of a mathematical model of the plant is obtained at the identification stage. This information is then used to generate the control action for future instants of time. In practice, effective real-time implementation of self-tuning adaptive control algorithms is often hampered by the complexity of having these two separate stages (A strom and Wittenmark
.1989). An obvious approach to tackle this problem for real-time implementation is to employ a processor fast enough to carry out the complete identification and Received 1 August 1992. Revised 12 April 1993. t Department of Electrical and Computer Engineering, University of Calgary, Calgary, Alberta TIN IN4, Canada. 0020-7179/94 $10.00
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1994 Taylor & Francis LId
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control computations in a very short time. This, however, is not easy to achieve because most of themicroprocessor-based systems that have been experimented with run at speeds of the order of a few MHz or less, leading to slow floating point computations (Malik et at. 1987). A floating point co-processor is normally used to augment the main CPU. Two adaptive control algorithms to control the speed of diesel-driven electric generating units are described by Roy et al. (1991, 1993). These methods essentially model the generating unit as a plant with dead-time and attempt to identify the plant parameters and the dead-time simultaneously. The identification of dead-time increases the algorithm complexity to approximately double that of the ordinary recursive least-squares identification algorithm used by Malik et al. (1987). To implement the algorithms described by Roy et al. (1991, 1993) in real-time, the problem regarding the choice of a processor becomes particularly severe due to the requirement of estimating the dead-time. Because of their numerical capabilities, as well as the fact that very.little peripheral hardware is required, digital signal processors (DSP) are currently being considered for real-time implementation of adaptive control algorithms (Dessaint et al. 1990). This paper primarily describes the implementation aspects of the adaptive control algorithms on the Texas Instruments' TMS320C30 DSP. Detailed description of the algorithms is omitted here because they are described by Roy et al. (1991, 1993). A brief outline of these algorithms is, however, given in the Appendix. Illustrative results of the DSP implementation of the adaptive algorithm in comparison to a commonly-used industrial control scheme are also given.
Choice of processor It is common practice for control algorithms in the commercially-available controllers to be implemented on processor-based integrated circuits called 'microcontrollers', specifically designed for such applications. Microcontroller integrated circuits are essentially 'enhanced' versions of some basic microprocessor to which several 'control type' functions and features, such as on-chip A/D and 0/ A converters, special purpose ports, high speed input/output (I/O) facilities, free timers. specific digital signal generators, are added to make them more suitable for real-time control. The software environment of such systems can be quite elaborate, including multi-tasking facilities, so that each of the added features can be handled by independent processes, leaving the CPU to do the control computation only. While the above features are undoubtedly invaluable assets in control application, the processing capability of the CPU itself is of greatest concern in adaptive control implementations. In their most elementary forms, self-tuning adaptive techniques can be computationally intensive. Even with a fairly low order model identification, 30-40 ms processor time may be required on sayan Intel 8086/8087 combination. In practice, this would limit the sampling time of the controller to such ranges. With microcontrollers, because of the lack of availability of math coprocessors, the computation time will be significantly greater. Consider for example, the Intel 80C186 microcontroller , which forms the 2.
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processing unit for the Woodward Governor Co.'s 701 Engine Controller (Woodward Governor Co. 1985a). The latter is a widely-applied commercial diesel prime-mover controller that has been used in this paper as the standard of comparison for the developed adaptive control algorithms. The 80C186 works on a 12 MHz clock, and uses an elaborate task management system to operate a fixed parameter proportional-integral (PI) control scheme on the diesel primemover. The complete PI scheme is implemented using integer arithmetic, since employment of floating point macros slows down the system to the extent that the PI can not operate within the chosen time-step of 8·33 ms. On the other hand, the 80C186 has a variety of on-chip control features that are usefully employed in the Woodward 701 controller. Some of the important ones are: (a) high speed output (HSO) pins, which can be programmed as digital signal generators, and output the control signal to be finally amplified and used; (b) high speed input (HSI) pins, that can be employed to input the speed signal in the form of pulses, and the speed thereby computed directly; (c) discrete operational inputs, that can be used as external switch-operated inputs. These can be used to introduce manually-operated 'Start/Stop' signals 'Raise/Lower Speed' signals, etc. It should also be mentioned that, .as a microcontroller, the 80C186 employs distributed processing for each of the above, so that the CPU is devoted only to the main control computation task. It may be noted that despite all these features, it is virtually impossible to employ complicated adaptive algorithms on the 80C186. The 80C186 instruction set is identical to that of the Intel 8086 (with the exception of some special control instructions on the former), so that as mentioned above, even with a fairly low order model identification a self-tuning adaptive controller would require 40 ms processing time with a co-processor. With a library of floating point macros, the time required is likely to be much greater. This puts an obvious restriction on the possible lower limit of data sampling time to something of the order of few tens of milliseconds. In the case of the diesel engine, the resolution of the dead-time as well as the filtering nature of the speed transducer make it imperative that the sampling time always be kept at a value less than 20 ms. Assuming the system dead-time to be less than 100 ms, a choice of 20 ms sampling time gives a resolution of one-in-five. A better choice from this viewpoint is a sampling time of 10 ms. This then ensures the following:
(i) it gives a one-in-ten resolution within the complete dead-time range of 0-0·1 s; (ii) since the maximum signal frequency expected is 20 Hz corresponding to the smallest time constant in the system of 0·05 s, the sampling occurs at a rate of five times the highest frequency, which would ensure good identification and negligible aliasing even at the highest expected frequency; (iii) Hazel and Flower (1971 a, b) advocate a sampling frequency based on the 'one-sample per cylinder' concept. Based on this choice, a four-cylinder
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engine running normally at 1800 rpm would require a sampling period of 8·33 ms (Woodward Governor Co. 1990). PI controllers that are known to work well in practice usually have the sampling approximately synchronized to this frequency. It is, therefore, reasonable to adopt a sampling period that is close to this value. It is evident from the above discussion that a pre-requisite for the implementation of an adaptive controller for the application under consideration is a processor that can complete all computations within a few milliseconds. The speed considerations suggest that a DSP would be a suitable host for this application. DSPs permit all real-time operations including measurement, identification, control computation and control actuation to be performed by the same processor. With the in-house availability and some familiarity with the Texas Instruments TMS320C30 DSP, it was decided to explore the possibility of implementing the adaptive control algorithms proposed by Roy et al. (1991, 1993) on this DSP. This DSP can conveniently be used to implement large identification and control algorithms since the various tasks can be operated together at a fairly high speed. Details of the TMS320C30 DSP are given in Texas Instruments (1988). At a clock frequency of 33·33 MHz, it has an instruction time of 60 ns. The processor also has a large variety of floating point assembly level instructions, each of which has the same instruction time as above. This would evidently make implementation of computationally intensive adaptive algorithms extremely convenient as compared to the 80C186. Using the TMS320C30 DSP, it is possible to achieve a data processing rate of 16·7 MIPS by means of a pipelined execution queue. Because of such high speed, it is possible to organize a system of multiple processes on the same processing unit. A rough estimate of the instructions involved in the adaptive algorithm showed that, even in the worst possible case, a cycle of each algorithm involves a little more than 2500 floating point instructions which should take approximately 75!-1s to execute. Thus, compared to a 10 ms sampling time, the computations would be completed almost instantaneously. The hardware version of the TMS320C30, which has been used to develop the controller, is a commonly-available processor board (Spectrum Signal Processing, 1990) that can be installed in a personal computer with a capability of uninterrupted communication with the latter through the dual-port memory. It can be used in conjunction with a real-time processor monitor that allows close examination of the processor variables and registers at any time. The implementation board has a 10K bank of RAM for operation of the DSP system, and a 64K bank of dual-ported RAM that can be used by both the DSP and the host PC. The latter communicates with the DSP through this dual-ported RAM in order to allow the monitor to operate. In addition, there is 2K of internal general purpose RAM (Spectrum Signal Processing, 1990). By defining various memory segments within these locations, it is possible to assign parts of the complete programme to various memory locations. A basic schematic block diagram of the set-up as a controller is shown in Fig. 1. By designing the speed and actuator signals of the diesel engine to be discrete signals in a time base, instead of voltage signals, AID and D/A converters can be replaced by extremely simple signal conditioning circuits.
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PC INTERFACE
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EXTERNAL
RAM
Figure 1. Schematic block diagram of DSP-based control scheme.
Unlike most microcontroller integrated circuits, the TMS320C30 DSP does not have any specific on-chip features that enhance its applicability to controller implementation. Therefore, several of the on-chip peripherals had to be programmed suitably in order to run tasks such as output signal generation and speed sensing. While details of most of these are discussed in subsequent sections, a brief outline of these follows for ease of comparison with the corresponding features of the 80C186. (1) An external interrupt pin of TMS320C30 is programmed to receive the pulses from the speed transducer. The interrupt service routine then computes the speed on the arrival of each pulse. This is described in § 3. (2) An on-chip timer can be used to keep track of the required up-and-down times of the output PWM signal, and the latter can be output at one of the port pins of the DSP. Details of this are provided in § 4. (3) The discrete control signals mentioned with reference to the Woodward 701 can be replaced by a real-time interface programme on the personal computer that acts as a host to the DSP implementation board. The interface programme runs under PC-DOS, and can send various signals (and also set parameters) to the DSP board. It should be emphasized that, unlike the 80C186, the TMS320C30 has no distributed processing on-chip features that can assist in (a) through (c) above. In other words all the above tasks are to be performed by the single DSP processor, together with the computations related to the adaptive algorithm. However, it was found that this does not create any significant bottleneck in the form of over-interference of tasks in real-time, principally because of the very high processing speed of the TMS320C30. The fact that the DSP has a host of fast floating point instructions at assembly level makes the implementation of adaptive algorithms very fast, and this too is a significant contribution towards speeding up the overall implementation. 3. Speed measurement Speed measuring schemes based on a reluctance crankshaft position sensor mechanism are commonly used in conjunction with the diesel prime-mover (Wolber, 1981). Such transducers essentially consist of a magnetic pick-up riding over a toothed wheel, mounted on the prime-mover shaft. The magnetic pick-up
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Figure 2. The 'Speed measurement' task (activated by speed pulses).
(MPU) generates 'speed' pulses in an electromagnetic coil every time a tooth passes through it. The time between two consecutive 'speed' pulses is measured in terms of a train of regularly generated clock pulses that are counted in this period. Within the DSP, the Timer 0 is set up to count periodically in steps of 0·\ ms, and forms the basis of the system clock. At 0·1 ms intervals, the Timer updates a time count, and this count is used in the speed measurement. The quasi-sinusoidal pulses from the speed sensor are converted to a rectangular pulse train, and introduced to the DSP's external interrupt INT I. Every time a speed pulse arrives, the speed measurement programme is executed once. A detailed flowchart of the task is shown in Fig. 2. 4.
Control current generation Various alternatives are possible for control signal generation. The output control signal from a commonly-used micro-controller-based industrial controller (Woodward Governor Co., 1985 a) is obtained as a pulse-width-modulated (PWM) rectangular waveform of fixed frequency (500 Hz). Within each period of this waveform, the pulse up-time can be varied within 0-2 ms according to the required magnitude of the control signal. The PWM signal can be filtered by a third-order analogue filter circuit so as to output an averaged dc value between 0 and 5 V. A stage of amplification of this voltage produces a current that is proportional to the input signal generated, and can drive the oil flow actuator. To maintain conformity with the industrial controller for comparative tests and to minimize changes required in the event of replacing this controller with the adaptive controller, it was decided to keep the output control signal from
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Figure 3.
473
The 'PWM generation' task (activated by internal Timer-l interrupt).
the DSP in the PWM form. It was felt that the uniformity might bring out more clearly the relative merits of the proposed adaptive scheme compared to the commercial PI controller. The PWM signal in the DSP-based implementation of the adaptive control algorithms is generated on a selected port pin by using the Timer 1. Since the state of the port pin should be 0 V for the down-time and 5 V for the up-time, the Timer can be alternately programmed to count through up-time and down-time, and at the end of each period the port pin may be set or reset, as the case may be. The flowchart for this task is given in Fig. 3.
5.
Organization of control tasks The adaptive control algorithms described by Roy et al. (1991, 1993), and briefly outlined in the Appendix, have been implemented on the TMS320C30 DSP. The following is a brief account of the task breakdown, indicating the various aspects of the overall control action as performed by the DSP. The task distribution has been adapted to be as similar as possible to the Woodward 701 controller (Woodward Governor Co., 1985 a) despite the differences in the architectural and software organizations of the 80C 186 microcontroller used in the 701 controller and the TMS320C30 DSP.
Clock: is the heart of the complete controller organization on the DSP and it performs the major time-keeping operations. The accuracy of both the speed sensing and control computation tasks is dependent on the accuracy of the clock. The only major task which is completely independent of the clock is the generation of the PWM waveform.
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Initialize and wait: sets the values of all constants that are to be used repeatedly by the other tasks, programmes the various peripherals and interrupts, and provides an idle state from which other tasks can begin, and to which they can return. Control computation: is invoked by the 'Clock' at every 10 ms sampling step (T) by a software interrupt. This task is purely numerical in nature, and begins by conversion of input data (control signal) and output data (speed) to per-unit. The data is then filtered by fourth-order moving average filters to average them approximately over an engine revolution. The task next proceeds through the various stages of identification and control described by Roy et al. (1991, 1993). A general sequential flowchart is shown in Fig. 4. The final output is the control signal at the current instant of time. Speed sensing: is the only task that is activated by an externally-driven interrupt with the arrival of every speed pulse. This has been described in § 3. It is found convenient to use the interrupt INT 1, which is externally accessible through the analogue external interface of the board. PWM generation: this task uses the Timer 1 and Port 0 pin to generate the control signal in a pulse-width modulated rectangular waveform, as described in § 4. This waveform has a constant period of 2 ms. Each of the tasks mentioned above is carried out by the same TMS320C30 processor on a 'priority' basis. They are completely independent of one another save for the fact that variable values obtained from one task may be used by others in future executions. Connections between the various tasks together with their individual priorities are shown in Fig. 5.
Figure 4.
The 'Control computation' task.
DSP-based adaptive speed control algorithms (llMEA
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Interconnections between DSP tasks. Note that return paths are not shown. The thick arrows show changes due to external interrupts.
Reliability of the control scheme demands that a physical control signal should always be present. Therefore, the 'PWM generation' is assigned to Timer 1, and as a consequence has the highest priority of 10. The accuracy requirements of the computations demand that the 'Clock' should use Timer 0 which has the next highest priority of 9. Speed sensing using INT 1, the control computation task activated every sampling interval by the Clock through the software operation of the interrupt INT 0 and the initialize and wait task started only once at Reset, have priorities of 2, 1 and 0 respectively. 6. Test setup The complete TMS320C30 board can be installed in a PC-AT with a capability of uninterrupted communication with the latter through the dual-port memory. The DSP is provided with 16K of RAM and 64K of dual-ported RAM that can be used by the DSP monitor on the host Pc. In addition, there is 2K of internal general purpose RAM (Spectrum Signal Processing 1990). By defining various memory segments within these locations, it is possible to assign parts of complete programmes to various memory locations. This is done at the programme linking time through a 'command file'. To carry out real-time tests with the adaptive controllers implemented on TMS320, a real-time window monitor has been provided for the host Pc. Using this monitor, it is possible to load files, study their execution through single stepping and break-point features, and run them in the execution mode. It is also possible to leave a DSP progamme in execution and exit the monitor so that the PC can then be employed for other purposes. While in the monitor, it is possible to hold or interrupt the real-time execution of programmes, change memory and register contents, and thereby modify the execution. The DSP board is provided with an analogue interface through D/A and A/D devices, and a port connection that together provide the communication with external systems (Spectrum Signal Processing 1990). In conjunction with the monitor, the DSP is a fast and powerful tool to study real-time algorithms.
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The DSP-based control setup has been extensively tested on an industrial diesel engine simulator. The Woodward Governor CO.'s T-96992 tester (Woodward Governor Co. 1985 b) has a complete prime-mover simulation fabricated out of analogue and digital electronic circuits. This includes the actuator mechanism, the fuel-to-energy process within the engine, and the prime-mover and alternator ·inertias. The simulator has various switch-operated settings for inertia levels, load level settings, actuator limits on fuel-flow, and the load turn-on/turn-off process. It is commonly used for conducting real-time tests on prime-mover control schemes. The DSP system was interfaced to the T-96992 simulator through input and output shaping circuits (Fig. 1). These circuits were based on slight modifications of the circuits used in the Woodward 701 controller, thereby maintaining approximate uniformity of interface. The input interface performed the initial pulse shaping of the quasi-sinusoidal pulse train generated by the speed sensor. The output interface circuit was used to convert the PWM signal to an equivalent driving current to the actuator coil. The results reported in § 8 are obtained by running the T-96992 real-time simulator with the Woodward 701 controller, and the above mentioned adaptive schemes that use dead-time estimates.
7.
Tests on speed sensor In these tests, only the 'Speed sensing' and the 'PWM generation' tasks are left running on the DSP in real-time. These tasks, being either themselves high priority tasks or dependent on the accuracy of similar tasks (like the 'Clock'), represent the most significant processing loads on the TMS320C30. The T-96992 simulator is run at different speeds using an ordinary commercially-available PI controller. At various speeds, two quantities are recorded:
(a) normalized bias error the difference between mean measured speed, and the expected (set) speed, as a fraction of the actual set speed; (b) normalized standard deviation the standard deviation of the measured speed as a fraction of actual set speed. Each of these values are recorded on the real-time DSP monitor, and are computed in real-time over a hundred consecutive values of measured speed (10 ms intervals). The results shown in Fig. 6 indicate a nearly zero value of normalized bias error at low speeds, increasing up to 0·01 at speeds around 1·0 pu. Similarly, the normalized standard deviation of error is found to be about 0·01 at low speeds, which increases up to 0·03 at speeds around 1·0 pu and beyond. Since the speed is computed by the DSP by taking the reciprocal of the clock-pulse count, a larger speed-measurement error can be expected at high speeds (i.e. low counts) due to the hyperbolic nature of the time-to-speed conversion.
8.
Sample experimental results The following two types of tests, conducted in real-time on the laboratory setup, are given to illustrate the performance of the adaptive speed controller on
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a DSP: (a) startup tests from zero speed to 1·0 pu at no-load.
(b) with the initial running speed of 1·0 pu, full-load turn-on and turn-off for a peak-overshoot setting of 0·031 pu speed. Comparison is made on the basis of settling time.
In the Woodward 701 controller, the control for speed regulation is temporarily bypassed when starting T-96992 using the 'Start' switch. This behaviour is shown in Fig. 7(a) for zero droop. It should be mentioned that these responses are somewhat arbitrary, as the actual time when the prime-mover settles at the reference speed value is wholly dependent on how efficiently the 'Start' switch is manually operated. The traces show overshoot to the 1·17 pu limiting speed value in 0·5 s, from where they drop to 1·0 pu about 0·75 s after the switch is released. Response of the adaptive scheme with zero droop, using the pole representation for the dead-time, is shown in Fig. 7(b). The prime-mover starts to accelerate as each of these programmes is executed in real-time. A complete range of control signal variation is allowed (0-2·0 pu), and the prime-mover accelerates under its normal fuel-flow without any by-pass mechanism. When
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Appendix
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Basic structure of the adaptive algorithms Details of the two adaptive control algorithms referred to in this paper are described by Roy et al. (1991, 1993). In this section the basic outline for the same is provided. The formulation of the algorithms is based on the premise that the engine and load, when fitted with a turbocharger, have an input-output behaviour that may be expressed at discrete time instants as a cascade combination of: (a) a polynomial transfer function with time-varying parameters; (b) an input dead-time element representing the lag between injection of fuel and production of torque. Since both the time-lag, and the non-linear behaviour of the engine/turbocharger are unpredictably time-varying, they can be expected to be estimated as transfer function parameters as well as effective dead-time, both of which are time-varying. The two algorithms from Roy et al. (1991, 1993), essentially differ in the way the dead-time is modelled for identification purposes. In the algorithm described by Roy et al. (1991), the dead-time is assumed to give rise to an approximate zero behaviour at all time instants, while in Roy et al. (1993), the behaviour is assumed to be a pole-type behaviour. The overall structure of each algorithm may now be summarized in the following steps. (i) An observation vector at the current instant is obtained using normalized past values of input (actuator current) and output (speed) variables. (ii) A recursive least-squares estimate yields transfer function parameters for the plant. (iii) The convergence of the least squares algorithm is constrained to realistic ranges by imposing a set of conditional constraints. (iv) Using the estimated parameters, values of the plant dead-time and plant gains can now be estimated. Corrections may be applied to the parameters to obtain a low prediction error. (v) The results of step (iv) yield a linear predictor model for the diesel prime-mover. (vi) The linear predictor of step (v), together with the estimated dead-time of step (iv), is used to set up the objective function for a sub-optimal real-time controller based on estimated dead-time. Note that the latter takes a time-varying value. (vii) Optimization of this controller gives the control signal for current instant of time.
REFERENCES
A'''ROM. K. J.• and WITTENMARK, B., 1989, Adaptive Control (Reading, Mass: AddisonWesley). DESSAIN,.. L. A., HEUER,., B. J., LE-Huy, H., and CAVUO,." G., 1990, A DSP-based adaptive controller for a smooth positioning system. IEEE Transactions on Industrial Electron-
ics, 37(5), 372-377.
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HAZEl_I., P. A., and FLOWER, J. D., 1971 a, Sampled-data theory applied to the modelling and control analysis of compression ignition engines- Part I. International Journal of Control, 13(3), 549-562: Sampled-data theory applied to the modelling and control analysis of compression ignition engines- Parr II. Ibid., 13(4), 609-623. MALIK, O. P., HOPE, G. S., CHENG, S. J., and HANCOCK, G. c., 1987, A multi-microcomputer based dual-rate self-tuning power system stabilizer. IEEE Transactions on Energy Conversion, 2(3), 355-360. Roy, S., MALIK, O. P., and HOPE, G. S., 1991, An adaptive control scheme for speed control of diesel driven power-plants. IEEE Transactions on Energy Conversion, 6(4), 605-611; 1993, A k-stcp predictive scheme for speed control of diesel driven power-plants. IEEE Transactions on Industry Applications, 29(2), 389-396. SPECTRUM SIGNAL PROCESSING Inc .. 1990, TMS320C30 System Board Technical Reference Manual (Burnaby, B. C., Canada: Spectrum Signal Processing). TEXAS INSTRUMENTS, 1988, Third-generation TMS320 user's guide. Manual #SPRU031, (Dallas, Texas: Texas Instruments). WOLBER, W. G., 1981, Automotive engine control sensors '80, Society of Automotive Engineers paper No. 800121. WOODWARD GOVERNOR CO., 1985 a, 701 Digital Speed Control for Medium Speed Diesel Engines, Manual No. 85104C (Ft. Collins, Colorado, U.S.A.: Woodward Governor); 1985 b, 2301/230IA/EGM Speed Loop Test Set with Load Sensor (T96992). Manual No. 55011C (Ft. Collins, Colorado, U.S.A.: Woodward Governor); 1990, Communication dated 29 August 1990, Ft. Collins, Colorado. U.S.A.
