WP4-33 - Revision history

Grolleaue: /* Autonomy, cooperation, and awareness */

2023-03-10T08:41:06Z

Autonomy, cooperation, and awareness

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	\|-		\|-
	\| TRL \|\| 3-4		\| TRL \|\| 3-4
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			\| Contact \|\| mario.diferdinando at univaq.it
	\|}		\|}

Univaq: /* Contribution and Improvements */

2022-11-12T17:34:26Z

Contribution and Improvements

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	Robust Digital Control for Autonomous Navigation		Robust Digital Control for Autonomous Navigation

	In the context of ~~UC 5~~, in order to provide a control strategy for the compensation and rejection of environmental perturbations, measurement uncertainties, and possible faults, a robustified quantized sampled-data drone flight controller is proposed. In particular, by the use of ISS (Input-to-State Stability) redesign methodology (see, for instance, [13]), a new term to be added to the feedback is designed in order to make it robust with respect to any bounded measurement error as well as any bounded actuation disturbance. Then, the stabilization in the sampled-and-hold sense theory (see [10]-[12]) is used as tool for analytically proving the existence of a suitably small sampling period and of an accurate quantization of the input/output channels such that the semi-global practical stability of the related quantized sampled-data closed-loop system with arbitrarily small final tracking error is ensured. It is highlighted that such control algorithm does not need any knowledge concerning the nature of the measurement errors and of the actuation disturbances. The only knowledges required for the implementation of the control algorithm are their amplitude bounds. Moreover, the provided algorithm is a static state feedback controller which can be easily implemented on FPGAs. Summarizing, in the context of UC5, the provided control algorithm contributes to the following requirements:		In the context of UC5, in order to provide a control strategy for the compensation and rejection of environmental perturbations, measurement uncertainties, and possible faults, a robustified quantized sampled-data drone flight controller is proposed. In particular, by the use of ISS (Input-to-State Stability) redesign methodology (see, for instance, [13]), a new term to be added to the feedback is designed in order to make it robust with respect to any bounded measurement error as well as any bounded actuation disturbance. Then, the stabilization in the sampled-and-hold sense theory (see [10]-[12]) is used as tool for analytically proving the existence of a suitably small sampling period and of an accurate quantization of the input/output channels such that the semi-global practical stability of the related quantized sampled-data closed-loop system with arbitrarily small final tracking error is ensured. It is highlighted that such control algorithm does not need any knowledge concerning the nature of the measurement errors and of the actuation disturbances. The only knowledges required for the implementation of the control algorithm are their amplitude bounds. Moreover, the provided algorithm is a static state feedback controller which can be easily implemented on FPGAs. Summarizing, in the context of UC5, the provided control algorithm contributes to the following requirements:
	• managing of critical situation with improved situation awareness (task interferences, scheduling issues, fault of failure conditions)		• managing of critical situation with improved situation awareness (task interferences, scheduling issues, fault of failure conditions)
	• providing efficient digital implementation of discrete–time controllers on FPGAs		• providing efficient digital implementation of discrete–time controllers on FPGAs

Univaq: /* Detailed Description */

2022-11-12T17:34:18Z

Detailed Description

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	==Detailed Description==		==Detailed Description==

	One of the main challenges when dealing with drone flight control systems is to provide control algorithms taking into account possible measurement errors as well as possible actuation disturbances. Indeed, in practical operative situations, the measurements provided by the sensors to the drone flight control are often affected by errors mainly due to devices uncertainties. As well, the actuators involved in the control actions (i.e., the drone motors) are often affected by disturbances due to possible malfunctioning of the motors or to uncertainties in the devices which make use of the control input. In the literature concerning drone flight control systems, control algorithms are often provided without taking into account of such possible issues. Moreover, such control algorithms are often designed in a continuous-time basis and problems concerning the sampling and the quantization of the control law are not taken into account. These facts easily lead to a deterioration of the performances when such control algorithms are applied in practice. The drone flight control should be able to compensate and reject possible actuation disturbances and measurement uncertainties. Furthermore, the control algorithm should be designed by taking into account the sampling and the quantization of the input/output channels. In the context of ~~UC 5~~, the developed software concerns a novel robust sampled—data control strategy allowing the attenuation of bounded actuation disturbances as well as of bounded observation error.		One of the main challenges when dealing with drone flight control systems is to provide control algorithms taking into account possible measurement errors as well as possible actuation disturbances. Indeed, in practical operative situations, the measurements provided by the sensors to the drone flight control are often affected by errors mainly due to devices uncertainties. As well, the actuators involved in the control actions (i.e., the drone motors) are often affected by disturbances due to possible malfunctioning of the motors or to uncertainties in the devices which make use of the control input. In the literature concerning drone flight control systems, control algorithms are often provided without taking into account of such possible issues. Moreover, such control algorithms are often designed in a continuous-time basis and problems concerning the sampling and the quantization of the control law are not taken into account. These facts easily lead to a deterioration of the performances when such control algorithms are applied in practice. The drone flight control should be able to compensate and reject possible actuation disturbances and measurement uncertainties. Furthermore, the control algorithm should be designed by taking into account the sampling and the quantization of the input/output channels. In the context of UC5, the developed software concerns a novel robust sampled—data control strategy allowing the attenuation of bounded actuation disturbances as well as of bounded observation error.

	It is well-known that a swarm of drone, in which each agent locally interacts with the other agents and the environment, is able to perform complex tasks which are not achievable by the use of a single agent. Therefore, the use of multiple UAVs as an organized swarm can significantly increase the performances of the single UAV, as well as of the overall group (see [1]-[3]). Indeed, each agent of the swarm is allowed to make use of the resources and capabilities of other agents through communication and/or coordination, and provide it with extra capabilities. Many approaches to the formation control problem of UAVs have been presented in the literature (see, among the others, [4]-[9]). On the other hand, contributions which simultaneously address time-varying formation, switching topology and the collision avoidance have never been provided. In the context of UCs 4 and 5, the developed software concerns a novel control strategy for the deployment of multi-UAV systems in a distributed time-varying set-up, where UAVs rely on local communication and computation. In particular, the control algorithm allows the main swarm intelligence strategies, namely flight formation, swarm tracking, and social foraging. Finally, the robust control strategy proposed in Section 1.1 is integrated to the algorithm and an efficient methodology for its implementation on FPGAs is presented.

			It is well-known that a swarm of drone, in which each agent locally interacts with the other agents and the environment, is able to perform complex tasks which are not achievable by the use of a single agent. Therefore, the use of multiple UAVs as an organized swarm can significantly increase the performances of the single UAV, as well as of the overall group (see [1]-[3]). Indeed, each agent of the swarm is allowed to make use of the resources and capabilities of other agents through communication and/or coordination, and provide it with extra capabilities. Many approaches to the formation control problem of UAVs have been presented in the literature (see, among the others, [4]-[9]). On the other hand, contributions which simultaneously address time-varying formation, switching topology and the collision avoidance have never been provided. In the context of UC5, the developed software concerns a novel control strategy for the deployment of multi-UAV systems in a distributed time-varying set-up, where UAVs rely on local communication and computation. In particular, the control algorithm allows the main swarm intelligence strategies, namely flight formation, swarm tracking, and social foraging. Finally, the robust control strategy proposed in Section 1.1 is integrated to the algorithm and an efficient methodology for its implementation on FPGAs is presented.

	==Contribution and Improvements==		==Contribution and Improvements==

Univaq: /* References */

2022-11-12T17:33:38Z

References

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	[15] M. Di Ferdinando, P. Pepe and S. Di Gennaro, "Robust Sampled-Data Consensus-Based Cooperative Control of Multi–UAVs," 2021 29th Mediterranean Conference on Control and Automation (MED), 2021, pp. 167-172, doi: 10.1109/MED51440.2021.9480249.		[15] M. Di Ferdinando, P. Pepe and S. Di Gennaro, "Robust Sampled-Data Consensus-Based Cooperative Control of Multi–UAVs," 2021 29th Mediterranean Conference on Control and Automation (MED), 2021, pp. 167-172, doi: 10.1109/MED51440.2021.9480249.

	[16] R. Carli, G. Cavone, N. Epicoco, M. Di Ferdinando, P. Scarabaggio and M. Dotoli, Consensus-Based Algorithms for Controlling Swarms of Unmanned Aerial Vehicles, ~~{\sl~~ Ad-Hoc, Mobile, and Wireless Networks. ADHOC-NOW 2020. Lecture Notes in Computer Science}.		[16] R. Carli, G. Cavone, N. Epicoco, M. Di Ferdinando, P. Scarabaggio and M. Dotoli, Consensus-Based Algorithms for Controlling Swarms of Unmanned Aerial Vehicles, Ad-Hoc, Mobile, and Wireless Networks. ADHOC-NOW 2020. Lecture Notes in Computer Science.

	[17] M. Di Ferdinando, D. Bianchi, S. Di Gennaro, P. Pepe, On the Robust Quantized Sampled--Data Leaderless Consensus Tracking of Nonlinear Multi--Agent Systems, IEEE Conference on Decision and Control, 2021.		[17] M. Di Ferdinando, D. Bianchi, S. Di Gennaro, P. Pepe, On the Robust Quantized Sampled--Data Leaderless Consensus Tracking of Nonlinear Multi--Agent Systems, IEEE Conference on Decision and Control, 2021.

Univaq: /* References */

2022-11-12T17:27:54Z

References

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	[13] E. D. Sontag, “Smooth stabilization implies coprime factorization”, IEEE Trans. Automat. Control, vol. 34, 1989, pp 435-443.		[13] E. D. Sontag, “Smooth stabilization implies coprime factorization”, IEEE Trans. Automat. Control, vol. 34, 1989, pp 435-443.

			[14] M. Di Ferdinando, P. Pepe, A. Borri, On Practical Stability Preservation under Fast Sampling and Accurate Quantization of Feedbacks for Nonlinear Time–Delay Systems, IEEE Transactions on Automatic Control, vol 66, pp 314–321, 2021.

			[15] M. Di Ferdinando, P. Pepe and S. Di Gennaro, "Robust Sampled-Data Consensus-Based Cooperative Control of Multi–UAVs," 2021 29th Mediterranean Conference on Control and Automation (MED), 2021, pp. 167-172, doi: 10.1109/MED51440.2021.9480249.

			[16] R. Carli, G. Cavone, N. Epicoco, M. Di Ferdinando, P. Scarabaggio and M. Dotoli, Consensus-Based Algorithms for Controlling Swarms of Unmanned Aerial Vehicles, {\sl Ad-Hoc, Mobile, and Wireless Networks. ADHOC-NOW 2020. Lecture Notes in Computer Science}.

			[17] M. Di Ferdinando, D. Bianchi, S. Di Gennaro, P. Pepe, On the Robust Quantized Sampled--Data Leaderless Consensus Tracking of Nonlinear Multi--Agent Systems, IEEE Conference on Decision and Control, 2021.

Univaq at 10:08, 3 April 2022

2022-04-03T10:08:11Z

← Older revision		Revision as of 10:08, 3 April 2022
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	Simulations of the provided control algorithm have been performed. A comparison of the robustified controller with the non-robustified one is also carried out. See figures 1-4.		Simulations of the provided control algorithm have been performed. A comparison of the robustified controller with the non-robustified one is also carried out. See figures 1-4.

	[[File:01.jpg\|400px\|Figure 1. Trajectory of the quadcopter along x, y and z axes: the desired position in the space is reported with a rhomboidal black point; the trajectory of the quadcopter in the case of robustified and non robustified controller is reported in blue and red line, respectively.]]		[[File:01.jpg\|400px\|Figure 1. Trajectory of the quadcopter along x, y and z axes: the desired position in the space is reported with a rhomboidal black point; the trajectory of the quadcopter in the case of robustified and non robustified controller is reported in blue and red line, respectively.]] [[File:02.jpg\|400px\|Figure 2 Trajectory of the quadcopter along x, y and z axes: the desired trajectory in the space is reported with a yellow line; the trajectory of the quadcopter in the case of robustified and non robustified controller is reported in blue and red line, respectively.]]

	[[File:02.jpg\|400px\|Figure 2 Trajectory of the quadcopter along x, y and z axes: the desired trajectory in the space is reported with a yellow line; the trajectory of the quadcopter in the case of robustified and non robustified controller is reported in blue and red line, respectively.]]		[[File:03.jpg\|400px\|Figure 3. Angles and positions of the UAV: the trajectory of the quadcopter in the case of robustified and non robustified controller is reported in blue and black line, respectively.]] [[File:04.jpg\|400px\|Figure 4. Tracking errors for angles and position of the UAV: the trajectories of the tracking errors in the case of robustified and non robustified controller are reported in blue and black lines, respectively.]]

	[[File:03.jpg\|400px\|Figure 3. Angles and positions of the UAV: the trajectory of the quadcopter in the case of robustified and non robustified controller is reported in blue and black line, respectively.]]

	[[File:04.jpg\|400px\|Figure 4. Tracking errors for angles and position of the UAV: the trajectories of the tracking errors in the case of robustified and non robustified controller are reported in blue and black lines, respectively.]]

	Controlling Swarms of Drones		Controlling Swarms of Drones

Univaq at 10:07, 3 April 2022

2022-04-03T10:07:42Z

← Older revision		Revision as of 10:07, 3 April 2022
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	Simulations of the provided control algorithm have been performed. A comparison of the robustified controller with the non-robustified one is also carried out. See figures 1-4.		Simulations of the provided control algorithm have been performed. A comparison of the robustified controller with the non-robustified one is also carried out. See figures 1-4.

	[[File:01.jpg\|~~700px~~\|Figure 1. Trajectory of the quadcopter along x, y and z axes: the desired position in the space is reported with a rhomboidal black point; the trajectory of the quadcopter in the case of robustified and non robustified controller is reported in blue and red line, respectively.]]		[[File:01.jpg\|400px\|Figure 1. Trajectory of the quadcopter along x, y and z axes: the desired position in the space is reported with a rhomboidal black point; the trajectory of the quadcopter in the case of robustified and non robustified controller is reported in blue and red line, respectively.]]

	[[File:02.jpg\|~~700px~~\|Figure 2 Trajectory of the quadcopter along x, y and z axes: the desired trajectory in the space is reported with a yellow line; the trajectory of the quadcopter in the case of robustified and non robustified controller is reported in blue and red line, respectively.]]		[[File:02.jpg\|400px\|Figure 2 Trajectory of the quadcopter along x, y and z axes: the desired trajectory in the space is reported with a yellow line; the trajectory of the quadcopter in the case of robustified and non robustified controller is reported in blue and red line, respectively.]]

	[[File:03.jpg\|~~700px~~\|Figure 3. Angles and positions of the UAV: the trajectory of the quadcopter in the case of robustified and non robustified controller is reported in blue and black line, respectively.]]		[[File:03.jpg\|400px\|Figure 3. Angles and positions of the UAV: the trajectory of the quadcopter in the case of robustified and non robustified controller is reported in blue and black line, respectively.]]

	[[File:04.jpg\|~~700px~~\|Figure 4. Tracking errors for angles and position of the UAV: the trajectories of the tracking errors in the case of robustified and non robustified controller are reported in blue and black lines, respectively.]]		[[File:04.jpg\|400px\|Figure 4. Tracking errors for angles and position of the UAV: the trajectories of the tracking errors in the case of robustified and non robustified controller are reported in blue and black lines, respectively.]]

	Controlling Swarms of Drones		Controlling Swarms of Drones
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	[[File:05.jpg\|400px\|Figure 5. Control of the UAVs trajectories with a sinusoidal target movement.]] [[File:06.jpg\|400px\|Figure 6. Control of the UAVs trajectories in a narrow corridor.]]		[[File:05.jpg\|400px\|Figure 5. Control of the UAVs trajectories with a sinusoidal target movement.]] [[File:06.jpg\|400px\|Figure 6. Control of the UAVs trajectories in a narrow corridor.]]

	[[File:07.jpg\|400px\|Figure 7. The case of robustified (black line) and no-robustified (red line) controller.]]		[[File:07.jpg\|400px\|Figure 7. The case of robustified (black line) and no-robustified (red line) controller.]] [[File:08.jpg\|400px\|Figure 8. The case of robustified (continuous line) and no-robustified (dashed line) controller.]]

	[[File:08.jpg\|400px\|Figure 8. The case of robustified (continuous line) and no-robustified (dashed line) controller.]]

	==References==		==References==

Univaq at 10:07, 3 April 2022

2022-04-03T10:07:10Z

← Older revision		Revision as of 10:07, 3 April 2022
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	In Figs. 5-8, simulations of the proposed control algorithm are reported.		In Figs. 5-8, simulations of the proposed control algorithm are reported.

	[[File:05.jpg\|400px\|Figure 5. Control of the UAVs trajectories with a sinusoidal target movement.]]		[[File:05.jpg\|400px\|Figure 5. Control of the UAVs trajectories with a sinusoidal target movement.]] [[File:06.jpg\|400px\|Figure 6. Control of the UAVs trajectories in a narrow corridor.]]

	[[File:06.jpg\|400px\|Figure 6. Control of the UAVs trajectories in a narrow corridor.]]

	[[File:07.jpg\|400px\|Figure 7. The case of robustified (black line) and no-robustified (red line) controller.]]		[[File:07.jpg\|400px\|Figure 7. The case of robustified (black line) and no-robustified (red line) controller.]]

Univaq at 10:06, 3 April 2022

2022-04-03T10:06:49Z

← Older revision		Revision as of 10:06, 3 April 2022
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	In Figs. 5-8, simulations of the proposed control algorithm are reported.		In Figs. 5-8, simulations of the proposed control algorithm are reported.

	[[File:05.jpg\|~~700px~~\|Figure 5. Control of the UAVs trajectories with a sinusoidal target movement.]]		[[File:05.jpg\|400px\|Figure 5. Control of the UAVs trajectories with a sinusoidal target movement.]]

	[[File:06.jpg\|~~700px~~\|Figure 6. Control of the UAVs trajectories in a narrow corridor.]]		[[File:06.jpg\|400px\|Figure 6. Control of the UAVs trajectories in a narrow corridor.]]

	[[File:07.jpg\|~~700px~~\|Figure 7. The case of robustified (black line) and no-robustified (red line) controller.]]		[[File:07.jpg\|400px\|Figure 7. The case of robustified (black line) and no-robustified (red line) controller.]]

	[[File:08.jpg\|~~700px~~\|Figure 8. The case of robustified (continuous line) and no-robustified (dashed line) controller.]]		[[File:08.jpg\|400px\|Figure 8. The case of robustified (continuous line) and no-robustified (dashed line) controller.]]

	==References==		==References==

Univaq at 10:06, 3 April 2022

2022-04-03T10:06:13Z

← Older revision		Revision as of 10:07, 3 April 2022
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	Simulations of the provided control algorithm have been performed. A comparison of the robustified controller with the non-robustified one is also carried out. See figures 1-4.		Simulations of the provided control algorithm have been performed. A comparison of the robustified controller with the non-robustified one is also carried out. See figures 1-4.

	[[File:01.jpg\|~~700px~~\|Figure 1. Trajectory of the quadcopter along x, y and z axes: the desired position in the space is reported with a rhomboidal black point; the trajectory of the quadcopter in the case of robustified and non robustified controller is reported in blue and red line, respectively.]]		[[File:01.jpg\|400px\|Figure 1. Trajectory of the quadcopter along x, y and z axes: the desired position in the space is reported with a rhomboidal black point; the trajectory of the quadcopter in the case of robustified and non robustified controller is reported in blue and red line, respectively.]]

	[[File:02.jpg\|~~700px~~\|Figure 2 Trajectory of the quadcopter along x, y and z axes: the desired trajectory in the space is reported with a yellow line; the trajectory of the quadcopter in the case of robustified and non robustified controller is reported in blue and red line, respectively.]]		[[File:02.jpg\|400px\|Figure 2 Trajectory of the quadcopter along x, y and z axes: the desired trajectory in the space is reported with a yellow line; the trajectory of the quadcopter in the case of robustified and non robustified controller is reported in blue and red line, respectively.]]

	[[File:03.jpg\|~~700px~~\|Figure 3. Angles and positions of the UAV: the trajectory of the quadcopter in the case of robustified and non robustified controller is reported in blue and black line, respectively.]]		[[File:03.jpg\|400px\|Figure 3. Angles and positions of the UAV: the trajectory of the quadcopter in the case of robustified and non robustified controller is reported in blue and black line, respectively.]]

	[[File:04.jpg\|~~700px~~\|Figure 4. Tracking errors for angles and position of the UAV: the trajectories of the tracking errors in the case of robustified and non robustified controller are reported in blue and black lines, respectively.]]		[[File:04.jpg\|400px\|Figure 4. Tracking errors for angles and position of the UAV: the trajectories of the tracking errors in the case of robustified and non robustified controller are reported in blue and black lines, respectively.]]

	Controlling Swarms of Drones		Controlling Swarms of Drones
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	[[File:05.jpg\|400px\|Figure 5. Control of the UAVs trajectories with a sinusoidal target movement.]] [[File:06.jpg\|400px\|Figure 6. Control of the UAVs trajectories in a narrow corridor.]]		[[File:05.jpg\|400px\|Figure 5. Control of the UAVs trajectories with a sinusoidal target movement.]] [[File:06.jpg\|400px\|Figure 6. Control of the UAVs trajectories in a narrow corridor.]]

	[[File:07.jpg\|400px\|Figure 7. The case of robustified (black line) and no-robustified (red line) controller.]]		[[File:07.jpg\|400px\|Figure 7. The case of robustified (black line) and no-robustified (red line) controller.]] [[File:08.jpg\|400px\|Figure 8. The case of robustified (continuous line) and no-robustified (dashed line) controller.]]

	[[File:08.jpg\|400px\|Figure 8. The case of robustified (continuous line) and no-robustified (dashed line) controller.]]

	==References==		==References==