Adaptive Control Using Model Error Control Synthesis - Revision history

Johnc at 18:02, 27 April 2012

2012-04-27T18:02:11Z

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Linares2 at 19:38, 11 March 2012

2012-03-11T19:38:16Z

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Linares2 at 19:38, 11 March 2012

2012-03-11T19:38:04Z

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	Research is being performed to develop a new approach for robust adaptive control. This approach, called Model-Error Control Synthesis (MECS), uses an optimal real-time nonlinear estimator to determine model-error corrections to the control input. The estimator determines the model error using a one time-step ahead approach. Control compensation is achieved by using the estimated model-error as a signal synthesis adaptive correction to the nominal control input so that maximum performance is achieved in the face of extreme model uncertainty and disturbance inputs. The MECS approach has many significant advances, including:		Research is being performed to develop a new approach for robust adaptive control. This approach, called Model-Error Control Synthesis (MECS), uses an optimal real-time nonlinear estimator to determine model-error corrections to the control input. The estimator determines the model error using a one time-step ahead approach. Control compensation is achieved by using the estimated model-error as a signal synthesis adaptive correction to the nominal control input so that maximum performance is achieved in the face of extreme model uncertainty and disturbance inputs. The MECS approach has many significant advances, including:

Linares2 at 19:37, 11 March 2012

2012-03-11T19:37:27Z

← Older revision		Revision as of 19:37, 11 March 2012
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	[[File:block.gif~~\|200px~~]]		[[File:block.gif]]

	Research is being performed to develop a new approach for robust adaptive control. This approach, called Model-Error Control Synthesis (MECS), uses an optimal real-time nonlinear estimator to determine model-error corrections to the control input. The estimator determines the model error using a one time-step ahead approach. Control compensation is achieved by using the estimated model-error as a signal synthesis adaptive correction to the nominal control input so that maximum performance is achieved in the face of extreme model uncertainty and disturbance inputs. The MECS approach has many significant advances, including:		Research is being performed to develop a new approach for robust adaptive control. This approach, called Model-Error Control Synthesis (MECS), uses an optimal real-time nonlinear estimator to determine model-error corrections to the control input. The estimator determines the model error using a one time-step ahead approach. Control compensation is achieved by using the estimated model-error as a signal synthesis adaptive correction to the nominal control input so that maximum performance is achieved in the face of extreme model uncertainty and disturbance inputs. The MECS approach has many significant advances, including:

Linares2 at 19:37, 11 March 2012

2012-03-11T19:37:11Z

← Older revision		Revision as of 19:37, 11 March 2012
Line 1:		Line 1:
			[[File:block.gif\|200px]]
	Research is being performed to develop a new approach for robust adaptive control. This approach, called Model-Error Control Synthesis (MECS), uses an optimal real-time nonlinear estimator to determine model-error corrections to the control input. The estimator determines the model error using a one time-step ahead approach. Control compensation is achieved by using the estimated model-error as a signal synthesis adaptive correction to the nominal control input so that maximum performance is achieved in the face of extreme model uncertainty and disturbance inputs. The MECS approach has many significant advances, including:		Research is being performed to develop a new approach for robust adaptive control. This approach, called Model-Error Control Synthesis (MECS), uses an optimal real-time nonlinear estimator to determine model-error corrections to the control input. The estimator determines the model error using a one time-step ahead approach. Control compensation is achieved by using the estimated model-error as a signal synthesis adaptive correction to the nominal control input so that maximum performance is achieved in the face of extreme model uncertainty and disturbance inputs. The MECS approach has many significant advances, including:

Linares2 at 16:35, 9 March 2012

2012-03-09T16:35:12Z

← Older revision		Revision as of 16:35, 9 March 2012
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	*Model parameters need not be updated in order to achieve robust performance		*Model parameters need not be updated in order to achieve robust performance
	*It can easily handle both time-varying parameter changes in the model and unmodeled disturbance inputs		*It can easily handle both time-varying parameter changes in the model and unmodeled disturbance inputs


	This work is sponsored by National Science Foundation. Results from research can be obtained from:		This work is sponsored by National Science Foundation. Results from research can be obtained from:

Linares2 at 16:34, 9 March 2012

2012-03-09T16:34:39Z

← Older revision		Revision as of 16:34, 9 March 2012
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	This work is sponsored by National Science Foundation. Results from research can be obtained from:		This work is sponsored by National Science Foundation. Results from research can be obtained from:


	[1] Kim, J.-R., and Crassidis, J.L., “Spacecraft Attitude Control Using Approximate Receding-Horizon Model-Error Control Synthesis,” AIAA Journal of Guidance, Control, and Dynamics, Vol. 29, No. 5, Sept.-Oct. 2006, pp. 1023-1031.		[1] Kim, J.-R., and Crassidis, J.L., “Spacecraft Attitude Control Using Approximate Receding-Horizon Model-Error Control Synthesis,” AIAA Journal of Guidance, Control, and Dynamics, Vol. 29, No. 5, Sept.-Oct. 2006, pp. 1023-1031.


	[2] Alsuwaidan, B., and Crassidis, J.L., “Robust Aircraft Longitudinal Control Using Model-Error Control Synthesis,” AIAA Guidance, Navigation, and Control Conference, Keystone, CO, Aug. 2006, AIAA Paper #2006-6050.		[2] Alsuwaidan, B., and Crassidis, J.L., “Robust Aircraft Longitudinal Control Using Model-Error Control Synthesis,” AIAA Guidance, Navigation, and Control Conference, Keystone, CO, Aug. 2006, AIAA Paper #2006-6050.


	[3] Kim, J.-R., and Crassidis, J.L., “Spacecraft Attitude Control Using Approximate Receding-Horizon Model-Error Control Synthesis,” AIAA Guidance, Navigation, and Control Conference, San Francisco, CA, Aug. 2005, AIAA Paper #2005-6178.		[3] Kim, J.-R., and Crassidis, J.L., “Spacecraft Attitude Control Using Approximate Receding-Horizon Model-Error Control Synthesis,” AIAA Guidance, Navigation, and Control Conference, San Francisco, CA, Aug. 2005, AIAA Paper #2005-6178.


	[4] Kim, J.-R., and Crassidis, J.L., “Fundamental Relation of Approximate Receding-Horizon Optimization to the Simple Mass-Spring-Damper System,” AIAA Guidance, Navigation, and Control Conference, Austin, TX, Aug. 2003, AIAA Paper #2003-5563.		[4] Kim, J.-R., and Crassidis, J.L., “Fundamental Relation of Approximate Receding-Horizon Optimization to the Simple Mass-Spring-Damper System,” AIAA Guidance, Navigation, and Control Conference, Austin, TX, Aug. 2003, AIAA Paper #2003-5563.


	[5] Kim, J.-R., and Crassidis, J.L., “Limit-Cycle Oscillation Control of Aeroelastic System Using Model-Error Control Synthesis,” AIAA Guidance, Navigation, and Control Conference, Austin, TX, Aug. 2003, AIAA Paper #2003-5506.		[5] Kim, J.-R., and Crassidis, J.L., “Limit-Cycle Oscillation Control of Aeroelastic System Using Model-Error Control Synthesis,” AIAA Guidance, Navigation, and Control Conference, Austin, TX, Aug. 2003, AIAA Paper #2003-5506.


	[6] Kim, J.-R., and Crassidis, J.L., “Robust Spacecraft Attitude Control Using Model-Error Control Synthesis,” AIAA Guidance, Navigation, and Control Conference, Monterey, CA, Aug. 2002, AIAA Paper #02-4576.		[6] Kim, J.-R., and Crassidis, J.L., “Robust Spacecraft Attitude Control Using Model-Error Control Synthesis,” AIAA Guidance, Navigation, and Control Conference, Monterey, CA, Aug. 2002, AIAA Paper #02-4576.


	[7] Kim, J.-R., and Crassidis, J.L., “Model-Error Control Synthesis Using Approximate Receding-Horizon Control Laws,” AIAA Guidance, Navigation, and Control Conference, Montreal, CA, Aug. 2001, AIAA Paper #01-4220.		[7] Kim, J.-R., and Crassidis, J.L., “Model-Error Control Synthesis Using Approximate Receding-Horizon Control Laws,” AIAA Guidance, Navigation, and Control Conference, Montreal, CA, Aug. 2001, AIAA Paper #01-4220.


	[8] Kim, J.-R., and Crassidis, J.L., “Linear Stability Analysis of Model Error Control Synthesis,” AIAA Guidance, Navigation, and Control Conference, Denver, CO, Aug. 2000, AIAA Paper #00-3963.		[8] Kim, J.-R., and Crassidis, J.L., “Linear Stability Analysis of Model Error Control Synthesis,” AIAA Guidance, Navigation, and Control Conference, Denver, CO, Aug. 2000, AIAA Paper #00-3963.


	[9] Crassidis, J.L., “Robust Control of Nonlinear Systems Using Model Error Control Synthesis,” AIAA Journal of Guidance, Control, and Dynamics, Vol. 22, No. 4, July-Aug. 1999, pp. 595-601.		[9] Crassidis, J.L., “Robust Control of Nonlinear Systems Using Model Error Control Synthesis,” AIAA Journal of Guidance, Control, and Dynamics, Vol. 22, No. 4, July-Aug. 1999, pp. 595-601.

Linares2 at 16:30, 9 March 2012

2012-03-09T16:30:04Z

← Older revision		Revision as of 16:30, 9 March 2012
Line 1:		Line 1:
	Research is being performed to develop a new approach for robust adaptive control. This approach, called Model-Error Control Synthesis (MECS), uses an optimal real-time nonlinear estimator to determine model-error corrections to the control input. The estimator determines the model error using a one time-step ahead approach. Control compensation is achieved by using the estimated model-error as a signal synthesis adaptive correction to the nominal control input so that maximum performance is achieved in the face of extreme model uncertainty and disturbance inputs. The MECS approach has many significant advances, including:		Research is being performed to develop a new approach for robust adaptive control. This approach, called Model-Error Control Synthesis (MECS), uses an optimal real-time nonlinear estimator to determine model-error corrections to the control input. The estimator determines the model error using a one time-step ahead approach. Control compensation is achieved by using the estimated model-error as a signal synthesis adaptive correction to the nominal control input so that maximum performance is achieved in the face of extreme model uncertainty and disturbance inputs. The MECS approach has many significant advances, including:

	The determined model is a natural by-product of the state estimator		*The determined model is a natural by-product of the state estimator
	Model parameters need not be updated in order to achieve robust performance		*Model parameters need not be updated in order to achieve robust performance
	It can easily handle both time-varying parameter changes in the model and unmodeled disturbance inputs		*It can easily handle both time-varying parameter changes in the model and unmodeled disturbance inputs


	This work is sponsored by National Science Foundation. Results from research can be obtained from:		This work is sponsored by National Science Foundation. Results from research can be obtained from:
	Kim, J.-R., and Crassidis, J.L., “Spacecraft Attitude Control Using Approximate Receding-Horizon Model-Error Control Synthesis,” AIAA Journal of Guidance, Control, and Dynamics, Vol. 29, No. 5, Sept.-Oct. 2006, pp. 1023-1031.

			[1] Kim, J.-R., and Crassidis, J.L., “Spacecraft Attitude Control Using Approximate Receding-Horizon Model-Error Control Synthesis,” AIAA Journal of Guidance, Control, and Dynamics, Vol. 29, No. 5, Sept.-Oct. 2006, pp. 1023-1031.

	Alsuwaidan, B., and Crassidis, J.L., “Robust Aircraft Longitudinal Control Using Model-Error Control Synthesis,” AIAA Guidance, Navigation, and Control Conference, Keystone, CO, Aug. 2006, AIAA Paper #2006-6050.		[2] Alsuwaidan, B., and Crassidis, J.L., “Robust Aircraft Longitudinal Control Using Model-Error Control Synthesis,” AIAA Guidance, Navigation, and Control Conference, Keystone, CO, Aug. 2006, AIAA Paper #2006-6050.

	Kim, J.-R., and Crassidis, J.L., “Spacecraft Attitude Control Using Approximate Receding-Horizon Model-Error Control Synthesis,” AIAA Guidance, Navigation, and Control Conference, San Francisco, CA, Aug. 2005, AIAA Paper #2005-6178.		[3] Kim, J.-R., and Crassidis, J.L., “Spacecraft Attitude Control Using Approximate Receding-Horizon Model-Error Control Synthesis,” AIAA Guidance, Navigation, and Control Conference, San Francisco, CA, Aug. 2005, AIAA Paper #2005-6178.

			[4] Kim, J.-R., and Crassidis, J.L., “Fundamental Relation of Approximate Receding-Horizon Optimization to the Simple Mass-Spring-Damper System,” AIAA Guidance, Navigation, and Control Conference, Austin, TX, Aug. 2003, AIAA Paper #2003-5563.

	Kim, J.-R., and Crassidis, J.L., “Fundamental Relation of Approximate Receding-Horizon Optimization to the Simple Mass-Spring-Damper System,” AIAA Guidance, Navigation, and Control Conference, Austin, TX, Aug. 2003, AIAA Paper #2003-5563.		[5] Kim, J.-R., and Crassidis, J.L., “Limit-Cycle Oscillation Control of Aeroelastic System Using Model-Error Control Synthesis,” AIAA Guidance, Navigation, and Control Conference, Austin, TX, Aug. 2003, AIAA Paper #2003-5506.


	Kim, J.-R., and Crassidis, J.L., “Limit-Cycle Oscillation Control of Aeroelastic System Using Model-Error Control Synthesis,” AIAA Guidance, Navigation, and Control Conference, Austin, TX, Aug. 2003, AIAA Paper #2003-5506.


	Kim, J.-R., and Crassidis, J.L., “Robust Spacecraft Attitude Control Using Model-Error Control Synthesis,” AIAA Guidance, Navigation, and Control Conference, Monterey, CA, Aug. 2002, AIAA Paper #02-4576.


	Kim, J.-R., and Crassidis, J.L., ~~“Model~~-Error Control Synthesis ~~Using Approximate Receding-Horizon Control Laws~~,” AIAA Guidance, Navigation, and Control Conference, ~~Montreal~~, CA, Aug. ~~2001~~, AIAA Paper #01-~~4220~~.		[6] Kim, J.-R., and Crassidis, J.L., “Robust Spacecraft Attitude Control Using Model-Error Control Synthesis,” AIAA Guidance, Navigation, and Control Conference, Monterey, CA, Aug. 2002, AIAA Paper #02-4576.

			[7] Kim, J.-R., and Crassidis, J.L., “Model-Error Control Synthesis Using Approximate Receding-Horizon Control Laws,” AIAA Guidance, Navigation, and Control Conference, Montreal, CA, Aug. 2001, AIAA Paper #01-4220.
	Kim, J.-R., and Crassidis, J.L., ~~“Linear Stability Analysis of Model~~ Error Control Synthesis,” AIAA Guidance, Navigation, and Control Conference, ~~Denver~~, CO, Aug. ~~2000~~, AIAA Paper #00-~~3963~~.

			[8] Kim, J.-R., and Crassidis, J.L., “Linear Stability Analysis of Model Error Control Synthesis,” AIAA Guidance, Navigation, and Control Conference, Denver, CO, Aug. 2000, AIAA Paper #00-3963.

	Crassidis, J.L., “Robust Control of Nonlinear Systems Using Model Error Control Synthesis,” AIAA Journal of Guidance, Control, and Dynamics, Vol. 22, No. 4, July-Aug. 1999, pp. 595-601.		[9] Crassidis, J.L., “Robust Control of Nonlinear Systems Using Model Error Control Synthesis,” AIAA Journal of Guidance, Control, and Dynamics, Vol. 22, No. 4, July-Aug. 1999, pp. 595-601.

Linares2 at 16:26, 9 March 2012

2012-03-09T16:26:34Z

← Older revision		Revision as of 16:26, 9 March 2012
Line 1:		Line 1:

	~~[File:www.acsu.buffalo.edu/~johnc/block.gif]~~

	Research is being performed to develop a new approach for robust adaptive control. This approach, called Model-Error Control Synthesis (MECS), uses an optimal real-time nonlinear estimator to determine model-error corrections to the control input. The estimator determines the model error using a one time-step ahead approach. Control compensation is achieved by using the estimated model-error as a signal synthesis adaptive correction to the nominal control input so that maximum performance is achieved in the face of extreme model uncertainty and disturbance inputs. The MECS approach has many significant advances, including:		Research is being performed to develop a new approach for robust adaptive control. This approach, called Model-Error Control Synthesis (MECS), uses an optimal real-time nonlinear estimator to determine model-error corrections to the control input. The estimator determines the model error using a one time-step ahead approach. Control compensation is achieved by using the estimated model-error as a signal synthesis adaptive correction to the nominal control input so that maximum performance is achieved in the face of extreme model uncertainty and disturbance inputs. The MECS approach has many significant advances, including:

	The determined model is a natural by-product of the state estimator		The determined model is a natural by-product of the state estimator
	Model parameters need not be updated in order to achieve robust performance		Model parameters need not be updated in order to achieve robust performance
	It can easily handle both time-varying parameter changes in the model and unmodeled disturbance inputs		It can easily handle both time-varying parameter changes in the model and unmodeled disturbance inputs

Linares2 at 04:28, 22 February 2012

2012-02-22T04:28:40Z

← Older revision		Revision as of 04:28, 22 February 2012
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	[[File:www.acsu.buffalo.edu/~johnc/block.gif]		[File:www.acsu.buffalo.edu/~johnc/block.gif]

	Research is being performed to develop a new approach for robust adaptive control. This approach, called Model-Error Control Synthesis (MECS), uses an optimal real-time nonlinear estimator to determine model-error corrections to the control input. The estimator determines the model error using a one time-step ahead approach. Control compensation is achieved by using the estimated model-error as a signal synthesis adaptive correction to the nominal control input so that maximum performance is achieved in the face of extreme model uncertainty and disturbance inputs. The MECS approach has many significant advances, including:		Research is being performed to develop a new approach for robust adaptive control. This approach, called Model-Error Control Synthesis (MECS), uses an optimal real-time nonlinear estimator to determine model-error corrections to the control input. The estimator determines the model error using a one time-step ahead approach. Control compensation is achieved by using the estimated model-error as a signal synthesis adaptive correction to the nominal control input so that maximum performance is achieved in the face of extreme model uncertainty and disturbance inputs. The MECS approach has many significant advances, including: