The present application claims priority to and all the benefits of Italian Patent Application No. 10 2013 201 247.0, filed on Jan. 25, 2013, both of which are hereby expressly incorporated herein by reference in their entirety.
The present invention relates to a device for detecting the attitude of motor vehicles, using at least one filter of a complementary type to estimate angles of attitude of the motor vehicle.
There are known in the sector of vehicles, including motor vehicles, inertial navigation systems (INSs) that enable, with respect to known navigation systems, based, for example, on GNSS signals, alternative or complementary navigation means. The INS records relative movements of the vehicle on which it is mounted and, on the basis of these, makes evaluations of the speed of the vehicle and the path followed.
The above INSs comprise inertial measurement units (IMUS) essentially based upon the measurements made by acceleration sensors and gyroscopes. In particular, these INSs use the measurements of accelerometers and gyroscopes to estimate angles of roll, pitch, and yaw of the motor vehicle, which enable evaluation of the attitude thereof.
INSs are based for the most part on extended Kalman filters (EKFs) in order to estimate the above angles of attitude of the vehicle using sensor-fusion techniques. This type of technique involves a high degree of computational complexity, especially if there is available a large number of sensor channels, as well as difficulty of calibration as regards the observability matrix Q and the covariance matrix R of the Kalman filter, and hence a high cost in terms of man hours for its development.
An alternative solution envisages use of complementary filters, which are instead easy to tune.
Various embodiments of complementary filters that can be used for providing an estimator of attitude are illustrated, for example, in the paper by Walter Higgins, “A comparison of complementary and Kalman filtering”, Aerospace and Electronic Systems, IEEE Transactions on (Volume: AES-11, Issue: 3), May 1975. However, complementary filters are valid only for a specific dynamic range, in which the model of the vehicle can be linearly approximated.
The object of the present invention is to provide an improved device that will enable use of filters with a lower degree of complexity, maintaining the performance of extended Kalman filters.
According to the present invention, the above object is achieved thanks to a device for detecting the attitude of motor vehicles that includes using at least one filter of a complementary type for computing an estimate ({circumflex over (x)}i) of angles of attitude (θ, φ, ψ) of the motor vehicle as a function of input signals comprising an acceleration signal (A) and an angular-velocity signal (ω). The device includes a plurality of complementary filters, each tuned for operating in a specific dynamic range, and a supervisor module, adapted to recognise the dynamic range of the input signals (A, ω) and select (S) a corresponding filter in said plurality of complementary filters.
Other objects, features and advantages of the present invention will be readily appreciated as the same becomes better understood after reading the subsequent description taken in connection with the accompanying drawings.
The invention will now be described with reference to the annexed drawings, which are provided purely by way of non-limiting example and in which:
In brief, the solution according to the invention regards a device for detecting the attitude of motor vehicles that comprises using at least one filter of a complementary type to estimate angles of attitude of the motor vehicle, the device including a plurality of complementary filters, each tuned for operating in a specific dynamic range, and a supervisor unit, that acts to recognise the dynamic range of the input signals and select a corresponding filter in said plurality of complementary filters.
Illustrated in
Shown at input to the above attitude-detection device 10 is a signal representing an acceleration A of the motor vehicle, measured by an accelerometer, not illustrated in
Moreover shown at input to the attitude-detection device 10 is a signal representing an angular velocity ω of the vehicle measured by a gyroscope, which is not illustrated in
The attitude-detection device 10 includes a bank 12 of complementary filters. This bank 12 of filters comprises a plurality of complementary filters 121, . . . , 12n, each of which receives in parallel the acceleration signal A and the angular-velocity signal ω. Each i-th complementary filter 12i supplies at output a respective i-th estimate {circumflex over (x)}i of the angles of attitude of the vehicle, i.e., angles of roll θ, pitch φ, and yaw ψ, as represented in greater detail with reference to
The attitude-detection device 10 further comprises a supervisor module 11, which also receives at input the acceleration signal A and the angular-velocity signal ω, the velocities of the four wheels ws, and the steering angle α and, on the basis of the values of these signals, controls, via a selection signal S, a selector 13 so that it connects one of the outputs {circumflex over (x)}i of the filters 12i to an output U of the attitude-detection device 10.
The above operation of tuning of each filter 12i for a different dynamic range is obtained in particular by setting a value of a respective time constant τ of the filter 12i, as described more clearly in what follows with reference to
The supervisor module 11 acts to detect the dynamic range associated to the acceleration signal A and to the angular-velocity signal ω at input generated by the vehicle while it is travelling and for selecting the complementary filter 12i tuned for the corresponding dynamic range.
In various embodiments, the supervisor module 11 acts to detect the level, i.e., the amplitudes Ax, Ay, Az of the acceleration signal A and the level, i.e., the amplitudes p, q, r of the angular velocity that can be derived from the angular-velocity signal ω supplied by the gyroscope.
In a preferred embodiment, the supervisor module may comprise, for example stored in a memory of the microcontroller that implements it, a plurality of different models of the vehicle that estimate one or more of the acceleration values Ax, Ay, Az and/or of the signals of the gyroscope p, q, r, on the basis of other measured values of dynamic quantities of the vehicle that affect the value of the attitude in order to identify the appropriate complementary filter to be used in the estimate of attitude.
The models stored are different in so far as they take into account different operating conditions of the vehicle, both in terms of dynamic range (for example, range of speeds, acceleration, values of friction) and in terms of type of manoeuvre.
In this connection,
The supervisor block 11 further comprises an evaluator block 111, that evaluates which of the aforesaid vehicle models 111, . . . , 11n makes the best estimate of a current dynamic state of the vehicle. On the basis of the evaluation of the evaluator block 111, i.e., on the basis of the j-th model 11j that best estimates the dynamic state of the vehicle, the model of the complementary filter 121, . . . , 12n to be used is chosen, by issuing a corresponding selection signal S.
In particular, in the example of
Evaluation of the vehicle model 11j in the evaluation block 111 may, for example, be carried out by measuring an error ej as distance of estimates Âx, Ây, Âz of the accelerations from the acceleration values Ax, Ay, Az, effectively measured by the inertial platform. In general, this distance may be obtained via calculation of a norm applied to these estimates Âx, Ây, Âz and to the corresponding measurements Ax, Ay, Az.
The above norm may be of different types; for example, it may be a norm that calculates a Euclidean distance between quantities.
In a preferred embodiment, the above distance, or, error ei may be obtained as
∫f(Âz−Az)2+f(Ây−Ay)2+f(Âx−Ax)2dt
i.e., a norm where to the quadratic distances between the components a cost functional f is moreover applied to take into account specific aspects of the vehicle condition, understood as dynamic range, but also as type of manoeuvre and possibly other parameters such as conditions of friction or wind.
The above cost functional f may be implemented, for example, as a weighted measurement, i.e., as a weight applied to the norm or to the measurement of distance, the weights being chosen on the basis of observations on standard manoeuvres carried out by the motor vehicle.
It should be pointed out that the evaluator 111 operates in the first place to calculate the closeness between the estimate and the measurement supplied by each model. In this perspective, the cost functional f represents a possible further degree of freedom that can be applied to the evaluator 111 to render it more flexible in regard to particular vehicle conditions. In theory, the model could be complex to the point of taking into account every parameter of interest for the estimate and not require the above functional, or else a less flexible estimate without the functional may be accepted. Hence, in various embodiments, the aforesaid cost functional f is not present, or may be equal to 1 in the equation appearing above.
In various embodiments, the above cost functional f may be the same for all the models 111, . . . , 11n.
In various embodiments, the aforesaid cost functional f is different for different models associated to different dynamic ranges, to take into account, for example, the specificity of certain manoeuvres associated to those dynamic ranges and models. There may be applied to the blocks 111, . . . , 11n functionals fj that represent, for example, a vehicle condition with low coefficient of friction, a condition of highspeed ring, or a condition of twist of the steering wheel.
In the example specifically described in
∫f(Âyj−Ay)2+f(Âxj−Ax)2dt
where Âyj,xj is an estimate of the acceleration along the axes x and y for each model 11j, Ay,x is the corresponding measurement made by the inertial platform, and f is the cost functional.
The minimum min(e1, e2, . . . , en) of the error ej evaluated in block 111, determines the value assumed by the selection signal S, which corresponds to the choice of the model 11i associated to a respective dynamic range DRi that best estimates the quantity at its input, and hence to the choice of the complementary filter 121, . . . , 12n to be used dynamically that best operates in the corresponding dynamic range DRi.
Each vehicle model 11j with j=1, . . . , n defines a particular vehicle condition whereby a given error ej is the minimum for all the models considered if the accelerations and angular velocities measured derive from the vehicle condition associated to the corresponding model 11j. The vehicle model 11j identifies a particular dynamic range of the vehicle quantities Ax, Ay, Az, of the acceleration signal A and of the angular velocities p, q, r measured. Furthermore, for each vehicle model 11j there exists just one filter 12i with a time constant τ defined for that particular vehicle condition.
In particular, for example, stored in the supervisor module 11 is a look-up table or other data structure that sets each of the vehicle models 111, . . . , 11n in relation with one of the filters 121, . . . , 12n. It is envisaged to construct empirically the above look-up table by identifying, on the basis of the correctness of the estimator {circumflex over (x)}i supplied at output, the value of time constant τ that corresponds to a given dynamic range, i.e., that enables a correct estimate of the angle of attitude, for example within a pre-defined error value. Hence, in the look-up table the vehicle model 11j associated to that given dynamic range is set in relation with the filter with time constant that enables correct estimation of the attitude in the context of the system 10. When the minimum min(e1, e2, . . . , en) of the error ej indicates that a certain model 11j is the most suited to the dynamic range of the acceleration and/or gyroscopic signals at input, by accessing the look-up table with the index of that model 11j at output there is obtained, as selection signal S, the index of the filter 12i with a time constant τ suited to its dynamic range and hence to be selected to obtain the best estimate of attitude.
The supervisor module 11 is obtained via a microprocessor module, for example the ECU of the motor vehicle. Also the selector 14 may be integrated within this microprocessor.
Illustrated in
The complementary filter 12i further comprises a module for computing attitude rates 126, which receives at input the angular-velocity signal ω and performs the calculation of the values of the first derivatives of the angles of roll θ, pitch φ, and yaw ψ, i.e., {dot over (θ)}, {dot over (φ)}, {dot over (ψ)}. The attitude-rate computing module 126 makes, in particular, the following calculation:
From the angular velocities p, q, r measured by the gyroscope and from the values of the angles of attitude, or Euler angles, it is possible to measure the attitude rates, i.e., the first derivatives {dot over (θ)}, {dot over (φ)}, {dot over (ψ)} of the Euler angles using Eqs. (2), which are in themselves known.
In particular, the above Eqs. (2) may be written in matrix form as:
taking into account that
The relations (3) and (4) represent the construction of block 126, the matrix A−1 being the matrix of rotation that enables calculation of the angular velocities {dot over (θ)}, {dot over (φ)}, {dot over (ψ)} in the vehicle reference system starting from the angular velocities p, q, r measured by the gyroscope (sensor reference system) and from the estimate of the angles of roll θ and pitch φ.
The calculated values of the angles of roll θ, pitch φ, and yaw ψ are supplied by the tilt-angle computing module 121 at input, as set-point, to a control loop that as a whole constitutes a complementary lowpass filter and a complementary highpass filter, through a derivator block 123, with time constant τ, and an integrator block 125. Specifically, the aforesaid calculated values of the angles of roll θ, pitch φ, and yaw ψ are supplied at input, as set-point, to an adder node 122, which computes the difference with respect to the estimate
Hence, the complementary filter 12i is in this way used for combining two independent measurements that are in themselves noisy, i.e., the acceleration signal A supplied by the accelerometer and the angular-velocity signal ω supplied by the gyroscope, where each measurement is corrupted by different types of spectral noise. The filter 12i provides an estimate of the real angle of attitude of the vehicle {circumflex over (x)}i via the two complementary highpass and lowpass filters with time constant τ.
The values of the angles of roll θ, pitch φ, and yaw ψ are supplied already directly by the tilt-angle computing module 121 on the basis of the acceleration signal A. This measurement, however, is accurate only at low dynamics.
The time constant τ according to a main aspect of the solution described herein is set different for each filter 12i so as to tune the respective filter to a different dynamic range of the input signals A and co.
Hence, from what has been described above, the advantages of the solution proposed emerge clearly.
The device for detecting the attitude of motor vehicles via the use of a bank of complementary filters enables use of filters with a lower degree of complexity, maintaining the performance of extended Kalman filters.
The device for detecting the attitude of motor vehicles, as compared to the extended Kalman filter used in applications linked to the dynamics of the motor vehicle, guarantees levels of performance that fully meet the specifications, but with a reduction of the complexity of tuning of the filters and of the use of computational resources.
The device for detecting the attitude of motor vehicles based upon an approach of the ‘Data Fusion’ type combines the measurements of gyroscopic and acceleration sensors present on board the vehicle in the framework of an inertial navigation system (INS), increasing and advantageously enriching each of the above measurements, so that there is obtained, for the vehicle, a powerful system of positioning of the attitude on three axes.
The estimate of attitude can be used for continuous mobile positioning obtained with the IMU.
The stable relative position supplied by the INS can then be used as bridge information for covering periods of time during which a navigator based upon GNSS signals, in particular GPS signals, receives degraded signals or these signals are not available.
The invention has been described in an illustrative manner. It is to be understood that the terminology which has been used is intended to be in the nature of words of description rather than of limitation. Many modifications and variations of the invention are possible in light of the above teachings. Therefore, within the scope of the appended claims, the invention may be practiced other than as specifically described.
Number | Date | Country | Kind |
---|---|---|---|
2011A0934 | Nov 2014 | IT | national |
Number | Name | Date | Kind |
---|---|---|---|
20090105900 | Tan | Apr 2009 | A1 |
Number | Date | Country |
---|---|---|
102006023574 | Nov 2007 | DE |
1154281 | Nov 2001 | EP |
Entry |
---|
Vasiliy M. Tereshkov, An Intuitive Approach to Inertial Sensor Bias Estimation, 2013, International Journal of Navigation and Observation, vol. 2013, Article ID 762758, 7 pp. |
Search Report issued by the Italian Patent Office for Italian Patent Application No. TO2014A000934 dated Jul. 1, 2015. |
Higgins, Jr., Walter T., “A Comparison of Complementary and Kalman Filtering,” IEEE Transactions on Aerospace and Electronic Systems, vol. AES-11, No. 3, pp. 321-325 (May 1975). |
Shen, Xiaowei, et al., “Adaptive complementary filter using fuzzy logic and simultaneous perturbation stochastic approximation algorithm,” Measurement, vol. 45, No. 5, pp. 1257-1265 (2012) (available online Jan. 30, 2012). |
Number | Date | Country | |
---|---|---|---|
20160131482 A1 | May 2016 | US |