GB2512088A - Apparatus for controlling a spacecraft during docking - Google Patents

Abstract

Apparatus for controlling a spacecraft during docking has a coarse tuning assembly 1-3 and a fine tuning assembly 4. The coarse tuning assembly 1-3 uses at least an adaptive fuzzy logic controller 3 for nonlinear MIMO systems 2 with subsystems which comprise fuzzification, inference, and output processing. The fine tuning assembly 4 uses at least APACC or artificial precognition using adaptive model cognized control.

Description

APPARATUS FOR CONTROLLING A SPACECRAFT DURING DOCKING

This invention relates to control apparatus and, more especially, this invention relates to apparatus for controlling a spacecraft during docking.

The guidance, navigation and control (GNC) of a spacecraft requires controlling the spacecraft attitude. This requires sensors to measure the spacecraft attitude, actuators to apply torques needed to re-orient the spacecraft to a desired attitude, and algorithms to command the actuators based on (1) sensor measurements of the current attitude and (2) a

specification of a desired attitude. : -.

The control of multivariable multi-input multi-output (MIMO) systems is a common problem in practical spacecraft rendezvous control scenarios.

Most existing approaches deal only with uncertainties in the inertia, centripetal/Coriolis, and gravitational terms, assuming that an exact model of the actuators is available. This assumption is rarely satisfied in practice because the actuator parameters may also have uncertainties due to installation error, aging and wearing out of the mechanical and electrical parts, etc. Adaptive control with actuator uncertainty is not considered, even though this uncertainty results in significant degeneration of controller performance.

Although autonomous docking is now a well-established technology, autonomous capture (with a poorly cooperative target) is more delicate. The development of a GNC system for rendezvous and capture has been addressed by the European Space Agency's (ESA) High integrity Autonomous Rendezvous and Docking control system (HARVO). HARVD uses classical control theory. However in the last half decade or so, of the advanced control schemes, only linear model predictive control (MPC) has been widely used in spacecraft rendezvous control. The fundamental idea behind all MPC techniques is to rely on predictions of a plant, model to compute the optimal future control sequence by minimization of an objective function. MPC models include controlled variables, manipulated variables and disturbance. (perturbation) variables. -At each sampling, instant, the optimisation performed based on new measurement data, and the first control input of the sequence is applied. The remainder of the sequence is discarded and the process is repeated at the next sampling instant in a "receding horizon" manner.

Whilst MPG has its origins in the chemical process industries, there is increasing interest in its application to vehicle manoeuvre problems, including spacecraft trajectory control and attitude control. Essentially, the application of MPC builds upon the ideas of fuel and time optimal trajectory planning by bringing the optimisation on-board, prbviding a natural framework for increased autonomy and reconfigurability, whilst accounting for physical and operational constraints such as finite control authority, passive safety and collision avoidance.

Generalized predictive control (GPC) and its derivatives have received special attention. Particularly the ability of GPC to be applied to unstable or time-delayed MIMO systems in a straight forward manner and the low computational demands for static models make it useful for many different kinds of tasks. Systems behaving unexpectedly, human factors, failures and the environment are all factors that contribute to non-linear plant dynamics.

The drawback with MPG is that this method is limited to linear models. If nonlinear dynamics are present in the plant, a linear model might not yield sufficient predictions for MPC techniques to function adequately.

The two most general approaches to closed loop identification are direct approach and indirect approach. The direct approach ignores the presenceof feedback, and directly identifies the plant by plant input and output data. This has the advntage that no knowledge about the type of control..feeciback or even linearity of the controller is required. The indirect approach identifies the closed loop, and obtains the open loop model by deconvolution if possible. Obtaining the open loop model is only possible if the controller is known and both the closed loop plant model and the controller are linear.

It is an aim of the present invention to reduce the above mentioned problems.

Accordingly, in one non-limiting embodiment of the present invention there is provided apparatus for controlling a spacecraft during docking, which apparatus comprises a coarse tuning assembly and a fine tuning assembly, the coarse tuning assembly being such that it comprises: a. an interface presenting a surface to external spacecraft perturbations, b. fuzzy descriptions to model guidance, navigation and bontrol of the spacecraft, an adaptive fuzzy logic controller for nonlinear MIMO systems with subsystems which comprise fuzzification, inference, and output processing, which comprise both type reduction and defuzzification, and which provide stability of a resulting closed-loop system, the adaptive fuzzy logic controller including: (i) inference engine identifying relationships using a rule base and outputs as fuzzy sets' to a type reducer, and (ii) output control demands including torque actuators to the fuzzyfier fuzzyflying' the signal, and the fine tuning assembly being such that it comprises: a. inputs from the coarse tuning assembly, b. precognition horizons determining how many future samples the objective function considers for minimization and the length of the control sequence computed, c. a linearized MIMO regression model extracted from the adaptive fuzzy logic controller at each time step providing the line' tuning parameters, and d. a non-linear dynamic linearized regression controller providing: (i) a crisp output signal feeding into APACC synthesis computing the optimal future GNC control sequence, and (ii) reduced set output and APACC synthesis feeding into the APACC linear logic system. 1' -

The apparatus may be one which includes a synchronization assembly which oprimises the input signal to the output signals and which comprises cascaded diophantine frequency synthesis (DFS) means which predicts future GNS stablization output parameters of the spacecraft.

The use of neural networks for system identification is a relatively new approach as is fuzzy logic.

The apparatus of the present invention may be regarded as providing artificial precognition (AP) using adaptive (model) cognized control (APACC).

The APACC improves upon model predictive control (MPC). MPG is a known -optimization.based control strategy that is considered extremely attractive in autonomous GNC space rendezvous scenarios. With respect to an MPG solution, APACC allows a significant performance improvement both in trajectory and in propellant save. Furthermore, due to artificial precognition of possible system instabilities, the apparatus of the present invention may allow improvements in other areas to be identified (e.g. mission definition) that could not otherwise be known.

Embodiments of the invention will now be described solely by way of example and with reference to the accompanying drawings in which: Figure 1 is schematic of variable speed movement gyros (VSCMGs) in APACC controlled spacecraft rendezvous; Figure 2 shows a computer hardware core system for artificial precognition using APACC in spacecraft rendezvous; and Figure 3 shows Intel® Xeon® processor high performance computing for APACC in spacecraft rendezvous.

Referring to the drawings, non-linear spacecraft rendezvous dynamics can be cl'aracteristically fuzzy' with a high degree of non-linearity. APACC feeds the instantaneous linearization of a nonlinear model with the Cognized' output of a fuzzy logic circuit (fuzzyifier in Figure 1) in each sampling instant.

It is similar to GPC in most aspects except that the instantaneous linearization of the fuzzy logic circuit output yields an adaptive linear regression model.

A key benefit of fuzzy l6gic is that it lets the designer describe the desired system behaviour with simple if-then' relations. In many applications, this gets a simpler solution in less design time. In addition, the designer can use all available engineering know-how to optimise the system performance directly. While this is certainly the beauty of fuzzy logic, it has also been a major limitation. In many applications knowledge that describes desired system behaviour is contained in data sets. Here the designer has had to derive the if-then' rules from the data sets manually, which requires a major effort with large data sets. When data sets contain knowledge about the system to be designed, a neural net promises a solution because it can train itself from the data sets.

While neural nets are at advantage by learning from data sets, these have inherent disadvantages; for instance, the cause for a particular behaviour cannot be interpreted, nor can a neural net be modified manually to change to a certain desired behaviour. Also, selection of the appropriate net model and selling the parameters of learning algorithm are difficult and require much experience. On the-other hand, fuzzy logic solutions, are easy tO -. , . -verify and optimise. The present invention utilises a fuzzy logic controller that automates rule derivation': eliminating the need to perform this function manually to predict plant dynamics instantaneously.

Fuzzy control methodologies have emerged in recent years as promising ways to approach nonlinear control problems. Fuzzy control, in particular, has had an impact in the control community because of the simple approach it provides to use heuristic control knowledge for nonlinear control problems. In very, complicated situationé, where the plant parameters are * subject to perturbations or when the dynamics of the systems are very complex, adaptive schemes have to be used online to gather data and adjust the control parameters automatically. However, no stability conditions have been provided so far for these adaptive approaches. APACC introduces two components into its adaptive fuzzy control scheme. One is a fuzzy logic system for coarse tuning. The other is the instantaneous linéàrization of the fuzzy logic circuit output which yields an adaptive linear model. This acts as a kind of robust compensator such as supervisory control, sliding-mode control, for the fine-tuning.

Recently, several stable adaptive fuzzy control schemes have been developed for multiple-input-multiple-output (MIMO) nonlinear systems.

However, these adaptive control techniques are only limited to the MIMO nonlinear systems whose states are assumed to be available for measurement. In many practical situations, state variables are often unavailable in nonlinear systems. Thus, the output feedback or APACC * . adaptive fuzzy control i required for such complicated applications, the -.: fuzzy control system controls the MIMO system and maintains the system stability The coarse and fine tuning improves system performance by reducing the impact of external perturbations, guaranteeing closed-loop stability.

APACC coarse and fine tuning are applied to attitude control of a spacecraft with both the inertia and the actuator uncertainties, to instantaneously cognize possible inertia/actuator parameters and to stabilise the spacecraft during rendezvous and other manoeuvres Non-adaptive passive methods applied to spacecraft attitude control assume uncertainties in the 1inear' (e.g. inertia, centripetal/Coriolis, and gravitational terms) and also the non-linear' (e.g. installation error, aging and wearing out of the mechanical and electrical parts) and that an exact model of the actuators is available.

A cluster of variable-speed control moment gyros (VSCMGs) with flywheels is used for the torque actuator. While a conventional control moment gyro (CMG) keeps its flywheel spinning at a constant rate, a VSCMG is essentially a single-gimbal CMG with the flywheel allowed to have variable speed.

There are three levels of schematic as shown in Figure 1. Starting with a Cartesian inertial frame, Figure la describes roll, pitch and yaw geometry of a spacecraft body in dynamic motion within a Gimbal frame (i-th VSCMG frame) of a single-gimbal CMG. The gyro flywheel has a variable rotation -: speed of Tn9 and variable gimbal rotation rate of y9. As the spacecraft body rolls, pitches and yaws as in a docking manoeuvre1 Figure lb shows a gyro VSCMG four-clustec(CMG1, 2, .3 and 4) rotating about gimbaiaxes b1b2 and -: b3 corresponding to the single gimbal frame Cartesian axes y, rand x. Each CMG gimbal frame subtends a skew angle 13. The inertial motion of the VSCMG gyro cluster during spacecraft dynamic manoeuvres creates torque which feeds into the torque actuator shown in Figure 2. The physical design of the VSCMG is shown in Figure ic.

Figure 2 shows how APACC is used for GNC. The design in controlling vehicle attitude requires sensors to measure vehicle attitude, actuators to apply the torques needed to re-orient the vehicle to a desired attitude, and algorithms to command the actuators based on (1) sensor measurements of the current attitude and (2) specification of a desired attitude.

The VSCMG cluster has an output corresponding to automatic dynamic motion of the spacecraft as a consequence of the application of I0 APACC. The essential components for artificial precognition are embedded as subsystems within a computer hardware core system 1 shown in Figure 2.

The detailed implementation of this is shown in Figure 3. The inputs include perturbations to the VSCMG cluster. The inputs will include a great number of variables including inertia, centripetal/Coriolis, and gravitational. Input/output is managed by a multivariable multi-input multi-output (MIMO) subsystem 2 as shown in Figure 2. The torque differential is measured by sensors through a sequential control process which comprises feedback measurement (feedback elements) and a comparator that compares the differential between the input and output signals. The MIMO subsystem 2 is driven to different operating states using set points which are added to the input signal at the comparator. The actual excitation. signal amplitude has to be as large as possible to ensure maximal excitation around each set point. APACC instantaneously compensates the error between the actual and cognised (possible and probable) outpyts (e.g. thrusters firing) to stabilize spacecraft attitude. This error is shown in the MIMO subsystem 2 as being fed into a controller which provides control outputs to torque actuators. The torque actuator applies the torques needed to re-orient the vehicle to a desired attitude.

The coarse tuning is provided by the adaptive fuzzy logic controller 3 shown in Figure 2. It has been developed for nonlinear MIMO systems involving external perturbations using fuzzy descriptions to model the spacecraft GNC. The adapative fuzzy logic controller is a type-2 fuzzy logic system involving the operations of fuzzification, inference, and output processing. The torque actuators output control demands to the fuzzyfier which Tuzzyfies' the signal. There are variables (mentioned above) within the inputs that are closely correlated, and they will have high mutual information.

However, there are other pairs of variables that are related that will have low correlation, but high mutual information. The coarse tuning radically speeds up cognition process making it possible to make virtually instantaneous attitude correction possible. The Inference engine identifies these relationships using the rule base and outputs these as fuzzy sets' to the type reducer The adapative fuzzy logic controller provides "output processing," and.comprises both type reduction and defuzzificatior,.

Type reduction (reduced set) captures more information about rule uncertainties than does the defuzzified value (a crisp, number), however,, it is computationally intensive. The advantage is that itcan cognize unpredicted perturbations data uncertainties. The adaptive fuzzy controller can perform successful control and guarantee that the global stability of the resulting closed-loop system and the tracking performance can be achieved.

The adaptive fuzzy logic controller output processing is fed into a non-linear dynamic linearized regression controller 4 shown in Figure 2, and which provides the fine tuning. The crisp output signal feeds into APACC synthesis.

The reduced set output and the APACC synthesis feed into the APACC linear logic system. The linearized MIMO regression model that is extracted from the fuzzy logic controller at each time step is used to provide the fine' tuning parameters. Non-linear dynamic linearized regression controller computes the optimal future GNC control sequence according to the objective function. Two precognition horizons' determine how many future samples the objective function considers for minimization and the length of the control sequence that is computed. As is common in most MPG methods, a receding horizon strategy is used and thus only the first control signal that is computed is actually applied to the spacecraft GNG systems to achieve loop closure.

Synchronization optimization of the input signal to the output signals is achieved using cascaded diophantine frequency synthesis (DFS) implemented using two or more phase lock loops (PLL). The DFS is DFS 5 in Figure 2. The DFS 5 is used to predict future outputs of continuous-time, infinite-dimensional time-varying and non-linear.systems. Its primary*iunction is to parameterize spacecraft GNS stabilization factors and lock them, into a continuous feedback 1PR. . ê The APAGGjor the guidance, navigation and contrál (GNG)'.:, of the spacecraft requires the virtually instantaneous analysis of enormous data volumes. To achieve this, the convey high performance computing (HPG) architecture from Intel® was selected for APAGC-GNG.. . The convey computer's approach provides very fast access to random access to memory, and is very useful for the complex functions used in APACC-GNC.

The architecture is based on Intel® Xeon® processor shown in Figure 3. The architecture features a highly parallel memory subsystem to further increase performance. Programmable "on the fly," FPGAs are a way to achieve hardware-based, application-specific performance. Particular APAGC-GNG algorithms, for example are optimized and translated into code that is loaded onto the FPGAs at runtime.

The VSCMG cluster presents an input to APACC automating dynamic motion of the spacecraft.

The APACC assembly comprises sensors to measure vehicle attitude, actuators to apply the torques needed to re-orient the vehicle to a desired attitude, and algorithms to command the actuators based on (1) sensor measurements of the current attitude and (2) specification of a desired attitude: a. APACC inputs comprise surfaces locally exposed to perturbations to the VSCMG cluster b. inputs include inertial, centripetal/coriolis, and gravitational measurements.

The multivariable multi-input multi-output (MIMO) subsystem 2 comprises a sensor assembly comprising a sequential control process measuring feedback and a comparator comparing the differential between the input and output torque signal. The MIMO subsystem 2 operates as follows: a. The MIMO subsystem 2 is driven to different operating states using set points added to the input signal at the comparator.

b. APACC instantaneously compensates the error between the actual and cognised (possible and probable) outputs including thrusters firing to stabilize spacecraft attitude.

c. To ensure maximal excitation around each set point, the excitation signal amplitude is maximized.

d. The error is fed into the controller providing control outputs to torque actuators.

e. The torque actuator applies the torques needed to re-orient the vehicle to a desired attitude.

The high performance computing (l-IPC) architecture provides very fast access to random access to memory for virtually instantaneous analysis of enormous data volumes for APAAC-GNC control.

!t is..to be appreciated that the embodiments of the invention described above, with reference to the accompanying drawings have been given by way of example and that modifications may be effected. Individual components shown in the drawings are not limited to use in their drawings and they may be used in other drawings and in all aspects of the invention.

Claims (3)

1. CLAIMSApparatus for controlling a spacecraft during docking, which apparatus comprises a coarse tuning assembly and a fine tuning assembly, the coarse tuning assembly being such that it comprises: a. an interface presenting a surface to external spacecraft perturbations, b. fuzzy descriptions to model guidance, navigation and control of the spacecraft,...an adaptive fuzzy logic controller for nonlinear MIMO systems with subsystems which comprise fuzzification, inference, and output processing, which comprise both type reduction and defuzzification, and which provide stability of a resulting closed-loop system, the adaptive fuzzy logic controller including: (i) inference engine identifying relationships using a rule base and outputs as fuzzy sets' to a type reducer, and (ii) output control demands including torque actuators to the fuzzyfier fuzzyflying' the signal, and the fine tuning assembly being such that it comprises: a. inputs from the coarse tuning assembly, b. precognition horizons determining how many future samples the objective function considers for minimization and the length of the control sequence computed, c. a linearized MIMO regression model extracted from the adaptive fuzzy logic controller at each time step providing the fine' tuning parameters, and d. a non-linear dynamic linearized regression controller providing (I) a crisp output signal feeding into APACC synthesis cdmputing the optimal futiir GNC control sequence; and (ii) reduced set output and APACC synthesis feeding into the APACC linear logic system.
2. 2. Apparatus according to claim 1 and including a synchronizajo assembly which optimizes the input signal to the output signals and which comprises cascaded diophantine frequency synthesis (DFS) means which predicts future GNS stabilization output parameters of the spacecraft.
3. 3. Apparatus for controlling a spacecraft during docking, substantially as herein described with reference to the accompanying drawings.